how global consumer health leader accelerated veeva vault adoption

Soroco Admin — Sat, 21 Feb 2026 07:50:40 +0000

Global Consumer Health How a Global Consumer Health Leader Increased Veeva Vault Adoption by 17% in 6 Weeks The Challenge At a leading global Consumer Health organization, R&D submissions teams depended on Veeva Vault as their core document management system for regulatory submissions. R&D submissions teams were navigating critical questions around efficiency and adoption: how effectively teams were using Veeva Vault, the real impact of configuration changes, where process inefficiencies were hiding, and which improvements could meaningfully accelerate adoption. The lack of visibility across the R&D submissions ecosystem made it difficult to drive standardization and identify areas for improvement across roles such as Local Regulatory Affairs, Global Regulatory Operations, Compliance Support, Regulatory Project Leads, Global Labelling Managers, Publishing & Archivist, and Business Support. Industry Global Consumer Health Timeline 6 weeks Function R&D Submissions Teams Enter To gain a unified view of how teams were truly interacting with Veeva Vault, the organization partnered with Soroco. Scout’s data-driven insights provided clear visibility into how this core workflow system was actually being used—highlighting adoption gaps, the real impact of configuration changes, and hidden process inefficiencies. An unprecedented view into work patterns across applications, capturing how much time was spent on core versus non-core apps, toggling behaviours, and process inefficiencies. The Findings Scout revealed significant variation in how teams used their systems: Nearly 53% of total effort was diverted to non-core tools such as Outlook, Excel, and SharePoint. Five out of seven teams  spent only ~10% of effort on Veeva Vault. Toggling from Veeva Vault to other applications was  2–4x higher  than the benchmark average. The Impact of Configuration Change In just one month, Scout captured measurable efficiency improvements post Veeva Vault configuration changes: 2–7% reduction in overall effort 17% increase in daily Veeva Vault usage 17% reduction in application toggling Deep Dive into Two Critical Processes 1. Creation of Records Each transaction required 18 minutes of effort. Only 50% followed the golden path varian 17% of transactions were touched across multiple days, leading to 38% potential rework. 2. Uploading Loader Sheets Over 6,000 requests were analyzed. Each request took 17 minutes, with 50% showing errors, driving 6% additional rework. Scout identified >50% optimization potential across these two processes through Automation Data consolidation Input standardization Rework reduction For Record Creation, automation and dashboard integration could reduce nearly half the effort. For Loader Sheets, process standardization and automated uploads could save 1,400+ hours—accelerating metadata updates and boosting accuracy. The Results Armed with these insights, the Consumer Health organization implemented Scout’s recommendations to strengthen Veeva Vault adoption by integrating: Regulatory assessment inputs External trackers Guidance documents Automated report generation With Scout, the organization gained clear visibility into work patterns, continuous improvement opportunities, and cross-functional dependencies. They stand to unlock 50% optimization across key processes—driving measurable productivity gains, faster project delivery, and sustained Veeva Vault adoption. The Outcome Scout shows how work really gets done. By uncovering hidden inefficiencies, Scout helped a global Consumer Health leader: Drive platform adoption Eliminate rework Unlock hidden capacity Scale R&D submissions processes with confidence See Scout in action. Schedule your demo now! Get in Touch

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Abhijit Shroff

Soroco Admin — Thu, 15 Jan 2026 06:35:13 +0000

A distributed and networked systems enthusiast, George loves solving challenging problems. He also happens to be a semi-professional Greek dancer who has graced the stage at events across the U.S.

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Munjal Jhala

Soroco Admin — Thu, 15 Jan 2026 06:03:03 +0000

A distributed and networked systems enthusiast, George loves solving challenging problems. He also happens to be a semi-professional Greek dancer who has graced the stage at events across the U.S.

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How a Global Manufacturing Leader Unlocked Millions in Hidden Sales Capacity

Soroco Admin — Sat, 01 Nov 2025 07:21:54 +0000

Manufacturing How a Global Manufacturing Leader Unlocked Millions in Hidden Sales Capacity The Challenge: Hidden Inefficiencies Draining Selling Time Are your field sales teams truly spending their time where it matters? For one of the world’s leading manufacturing companies, the answer was no. Despite healthy market demand, sales performance had plateaued. Area Sales Managers (ASMs) across Germany, Poland, and the UK were buried under administrative work — juggling reports, CRM updates, and email threads instead of engaging customers. Leadership lacked objective data visibility into how time was being spent making it hard to drive accountability and sales efficiency. Traditional sales analytics showed what was closing — not why productivity was stalling. Industry Manufacturing Timeline 6 weeks Function Sales Ops The Root Cause: Fragmented Digital Workflows Scout’s diagnostic revealed a striking pattern: 60% of ASM time lost to non-core digital tasks 126% higher effort variance across regions 2% of customers driving 30% of total email volumeThese inefficiencies weren’t visible in any existing system. The company needed a way to see how work actually happened — across tools, apps, and regions — to reclaim lost selling capacity. Enter Scout uses granular, comprehensive and user anonymized user interaction data across all systems — clicks, app usage, and workflow transitions, coupled with an AI-powered recommendation engine that provides you surgical insights to streamline customer lifecycles by increasing productivity and creating 30% more time for customers. See, down to the minute, how your team spends their day. Get recommendations on which behaviors to prioritize and which to eliminate. Within six weeks, Scout delivered a live, factual map of how sales work actually happened and the bottlenecks in the work. Within 6 weeks Scout delivered a privacy-safe map of how sales teams were truly working — and where time was being lost. Key Insights Uncovered Scout’s data-driven visibility exposed the hidden bottlenecks holding back growth: 60%+ of ASM time was spent on non-core administrative work. 2% of customers = 30% of total email load — showing disproportionate focus. 126% time variance across regions highlighted inconsistent processes. Clear opportunities emerged to automate, standardize, and streamline key workflows for faster deal cycles. For the first time, leadership had a single, factual source of truth on how work was being done — not how people thought it was being done. The Impact: Time Reclaimed, Growth Unlocked Scout’s insights powered targeted transformation initiatives across global sales operations: Standardized best practices across all regional teams. Automated repetitive admin workflows. Reallocated ASM time toward customer engagement, renewals, and upselling. Launched focused outreach for high-value accounts. These actions delivered measurable, rapid results: 30% more selling time unlocked per ASM. 25% higher productivity across regional teams. 15% increase in renewals through focused outreach. $4M+ incremental revenue potential per region. 3.5x ROI achieved in under four months. 26K+ hours saved annually for every 100 users. What started as a visibility challenge became a productivity breakthrough. Scout turned invisible digital work into actionable intelligence — transforming how the company sells, plans, and grows. The Outcome: From Digital Chaos to Sales Clarity Today, the organization runs a smarter, data-driven sales engine — powered by continuous visibility from Scout. What was once lost time is now productive capacity. What was invisible is now measurable. And what was fragmented is now unified. Replicated across 50+ global regions, this transformation continues to drive consistent growth, agility, and operational excellence at scale. In the Customer’s Words “Scout helped us see what no CRM could — the hidden patterns behind every click, email, and process. That visibility has changed the way we manage productivity.” VP, Sales Transformation, Global Manufacturing Leader From digital chaos to sales clarity — that’s the Scout advantage. See Scout in action. Schedule your demo now! Get in Touch

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GCC Transformation Pulse Survey 2025

Soroco Admin — Tue, 07 Oct 2025 06:47:46 +0000

Pulse Survey GCC Transformation Pulse Survey 2025 The Hidden Friction Blocking Scale – A Soroco Illuminate Leadership Study As global enterprises continue to recalibrate for speed, resilience, and innovation, Global Capability Centers (GCCs) find themselves thrust into new roles — not just as engines of efficiency, but as hubs for operational excellence, AI acceleration, and talent innovation. To understand how prepared GCCs are to deliver on AI and Automation mandates, Soroco surveyed 132 GCC leaders across India and the United States in 2025. The goal was simple: take a real-time pulse of where operational initiatives are succeeding, where friction persists, and which areas lack visibility. Initial takeaways from the GCC Transformation Pulse Survey 2025: “You can’t improve what you can’t see, and in today’s GCCs, operational change initiatives start with knowing what’s really happening on the ground.” Senior Vice President, Global Supply Chain Excellence, Fortune 100 Life Sciences GCC See Scout in action. Schedule your demo now! Get in Touch

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Peak matrix 2025 slider

Soroco Admin — Mon, 11 Aug 2025 05:24:45 +0000

“Soroco continues to demonstrate strong execution of its vision to unlock sustainable and scalable business value for its clients. The depth and breadth of product capabilities and use cases, its investments in foundational AI models for interaction data, and a strong market presence in the DII space have helped Soroco emerge as a Leader and Star Performer on Everest Group’s Digital Interaction Intelligence Products PEAK Matrix® 2025,” said Amardeep Modi, Vice President at Everest Group. “Its product vision and roadmap, ease of use and intuitive interface, the platform’s strong AI foundation, quality of insights, and responsive customer support are some of the key strengths indicated by its clients.” Amardeep Modi, Vice President, Everest Group

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Soroco named Leader and Star Performer in Digital Interaction Intelligence Products 2025

Soroco Admin — Wed, 30 Jul 2025 10:05:05 +0000

Four Years in a Row: Soroco Tops Everest Group’s PEAK Matrix® Assessment on Digital Interaction Intelligence 2025, Once Again. Download the report today Why this matters for you In a crowded market of task mining and process intelligence tools, Soroco’s Scout goes further—capturing how teams truly work across applications and workflows, revealing patterns invisible to process-centric solutions. This report gives you: Independent, comparative analysis of 18 leading DII providers globally Soroco’s strengths, including market adoption and product capabilities Key technology and market trends from generative and agentic AI Benchmarks to pick the right DII platform for your transformation programs Download the report today What sets Soroco apart? According to the 2025 Everest Group assessment, Soroco stands out for its: Deep Capture Engine powered by Edge AI Deep Capture Engine powered by Edge AI collects digital interactions at the OS level across clicks, keystrokes, visuals, browser DOMs, mainframes, and Citrix environments. First foundation model for interaction data First foundation model for interaction data – Tribescope transforms raw interaction data into business-relevant insights, mapping workflows to outcomes and KPIs. Advanced AI capabilities Advanced AI capabilities from supervised/unsupervised ML task classification to generative AI-driven SOP creation, AI-based root-cause analysis, and natural language conformance rule configuration. Self-service analytics Self-service analytics enables business users to build role-specific dashboards and metrics without technical support. Strong privacy and compliance controls Strong privacy and compliance controls dedicated PII monitoring dashboard aligned to GDPR and enterprise policies, with multi-tenant architecture for secure deployments. Proven market adoption Proven market adoption deployed in 200+ organizations, including Fortune 500 companies, with a growing partner ecosystem spanning Accenture, KPMG, EY, Whatfix, and major RPA providers. What is Digital Interaction Intelligence? Discover how DII surpasses traditional task and process mining solutions with advanced AI capabilities, across industries and functions. Download the Everest Group Playbook See Scout in action. Schedule your demo now! Get in Touch

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How Firstsource Unlocked 50% Automation Potential in Logistics Data Entry Using Scout

Soroco Admin — Wed, 02 Jul 2025 11:45:01 +0000

Logistics How Firstsource Unlocked 50% Automation Potential in Logistics Data Entry Using Scout The Challenge A leading logistics company in the US, operating across multiple locations, was grappling with inefficient and inconsistent back-office processes. These inefficiencies led to increased time expenditure, revenue leakage, over 50% reduction in process accuracy, a suboptimal end-user experience, and ultimately high operational costs and production inefficiencies. Despite prior efforts using manual discovery methods to identify end-to-end cost optimization opportunities, the company was unable to achieve the desired level of transformation. Industry Logistics Timeline 6 weeks Function Data Entry Operations Attempted Solution before Scout The company initially relied on manual discovery to address its operational challenges. However, this traditional method yielded fragmented insights that failed to offer a holistic view of inefficiencies. It lacked the end-to-end visibility and advanced analytics needed to identify critical bottlenecks, and did not account for nuanced workflow variations across teams and locations. Enter Firstsource, a global services partner, deployed Scout to transform the shipment form validation process for a logistics technology client. Led by Firstsource’s automation and transformation team, the initiative focused on effort-intensive workflows involving multiple form types and frequent cross-system data transfers. Over six weeks, Scout uncovered hidden effort patterns, identified automation-ready workflows, and provided data-backed inputs to redesign standard operating procedures (SOPs). These insights empowered Firstsource’s Center of Excellence (CoE) to craft a transformation roadmap aimed at reducing manual effort by 50%, positioning them as a strategic enabler of operational excellence. Over 6 weeks Scout uncovered hidden effort patterns, and provided data-backed inputs to redesign SOPs. Objectives: Enabling Effort Efficiency Through Firstsource-Led Discovery Pinpoint manual effort hotspots and repetitive work patterns Identify automation-ready workflows across form types Quantify time spent on data entry, navigation, and referencing tasks Equip Firstsource’s internal CoE with step-level, data-backed insights to drive change Scout Insights: A Clearer Picture of Effort Distribution High Manual Effort Across Steps Scout revealed that 57% of user effort was spent on repetitive actions within a single application, while another 42% was spent navigating across 25 screens or tabs to reference and input data. These patterns pointed to opportunities for workflow redesign and data integration – particularly in legacy form processing. Expert vs. Standard User Variance Standard users took 1.2–2x more time and steps per task compared to process experts, indicating a lack of standardization. This variance highlighted the opportunity to codify expert behavior into improved SOPs. Fragmented Application Usage Significant effort fragmentation was observed across internal systems and an external verification portal. Frequent screen / tab switching for simple tasks introduced delays and increased error risk, underscoring the need for streamlined workflows and interface design. Scout Recommendations helped Firstsource turn Insight into Action Introduce Self server OCR BOT Introduced a self-serve OCR bot which can read the key info and generate a comparison report, eliminating over 70% of screen switching and data referencing effort. Automate ‘New Form’ Processing The “New Form” workflow—structured, rules-based, and highly repetitive—was identified as the most automation-ready. Automating this process can free up analysts to focus on complex cases and exceptions. Standardize Execution Patterns Benchmark high-performing user behavior to reduce variability. Drive consistency in navigation, data validation, and task execution across the team. Redesign SOPs & Training Use real interaction data—not assumptions—to update SOPs and develop targeted training programs. This ensures alignment with actual user behavior and improves adoption. Potential Business Impact: Unlocking Tangible Results for the Client By acting on Scout-driven insights, Firstsource helped define a transformation roadmap that is expected to deliver: ~50% reduction in manual effort across data entry operations A validated automation roadmap with use-case level granularity Faster turnaround times for SLA-bound transactions Improved consistency and accuracy in execution across the team Scout empowered Firstsource to move beyond anecdotal views and lead a transformation grounded in real user behavior – delivering targeted automation, streamlined operations, and scalable change for the client. What’s next? The success of this Firstsource-led Scout deployment lays the foundation for scaled adoption across adjacent workflows and faster automation rollout. For the client, the focus now shifts to executing the roadmap, capturing effort savings, and using this engagement as a model for broader transformation. See Scout in action. Schedule your demo now! Get in Touch

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brokerage industry

Soroco Admin — Wed, 25 Jun 2025 12:45:21 +0000

Scout discovered variations for a brokerage giant and unlocked optimization potential by 25% through system adoption

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insurance industry

Soroco Admin — Wed, 25 Jun 2025 11:28:22 +0000

Scout identified a 15% potential effort per claim reduction for a leading insurance company, ensuring continuous improvement

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automotive industry

Soroco Admin — Wed, 25 Jun 2025 07:48:59 +0000

Scout cuts effort per order and effort disparity for a F500 auto company, saving manual effort by 47%.

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Everest Group DII Business Case

Soroco Admin — Fri, 23 May 2025 09:44:20 +0000

White Paper Why Digital Interaction Intelligence is the Next Must-Have for Enterprise Ops Go Beyond Process Maps. Act on Interaction-Level Insights. Despite years of investment in automation and transformation, most enterprises still can’t answer a fundamental question: “How is work actually getting done?” Everest Group highlights the growing disconnect between how work is imagined in systems and how it’s executed by people. This whitepaper shows how Digital Interaction Intelligence (DII) bridges that gap – offering a clear, data-driven case for unlocking hidden value across your operations. Who Should Read This Report? Process Intelligence and Automation Leaders CTOs, CIOs, Heads of Transformation, and AI Leaders Operations Heads and Line-of-Business Leaders Download This Report to See How Work Actually Happens Uncover invisible inefficiencies by capturing interaction-level data across applications – beyond what process maps and system logs can show. Build a Stronger Business Case for Change Learn how leading enterprises quantify cost savings, productivity gains, and automation opportunities using DII insights. Bridge the Gap Between Systems and People Understand how DII connects user behavior with business outcomes to drive more accurate decisions, faster transformation, and sustainable impact. What You Will Learn Why traditional task and process mining tools fall short – and how DII fills the gap A practical roadmap to building a business case for DII in your organization A real-world example from a $2B insurance company that achieved a 3x return on DII investments How BT Group scaled DII across 60+ teams using a structured Center of Excellence model About the authors: This whitepaper was produced by Everest Group, a leading global research firm, and licensed to Soroco. The insights are drawn from independent analysis, enterprise case studies, and expert interviews. Ready to see how DII unlocks the truth about how work gets done? Download the Everest Group whitepaper to discover the strategic and financial value behind Digital Interaction Intelligence. Download Your Free Copy Today See Scout in action. Schedule your demo now! Get in Touch

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Teach AI to Work Like a Member of Your Team

Soroco Admin — Mon, 21 Apr 2025 10:51:48 +0000

Teach AI to Work Like a Member of Your Team By reverse engineering your team’s workflow, you can customize generic AI tools to fit your processes. Authored by CXOs for CXOs Read our HBR article here Authors Rohan Murty Founder, WorkFabric AI & Soroco Ravi Kumar S CEO, Cognizant Hemanth Yamijala Senior Director of Engineering, Soroco George Nychis Co-founder, WorkFabric AI & Soroco Synopsis Across boardrooms, one theme is becoming clear: enterprise leaders are not questioning if AI can create value — they’re asking why it hasn’t yet. Sparked by a discussion about how the work graph and interaction data can help AI deployments in customers, between Rohan Narayana Murty and Ravi Kumar S (CEO, Cognizant) and brought to life in an actual customer environment, this article is a manifesto on fixing AI’s productivity paradox in the enterprise. At Soroco, we see this every day – large enterprises roll out powerful AI models with high expectations. But the productivity gains are marginal. The business impact is unclear. And the tools, while sophisticated, are disconnected from how work teams really get work done. But this is exactly what Soroco, and its work graph have been excavating. The work graph contains the interaction data necessary to bridge the gap between the promise of enterprise AI and its current limitation – missing context. Read our latest article in Harvard Business Review (HBR) on how interaction data helps orgs solve the key bottleneck for widespread AI adoption, including a live customer case study. We introduce reverse mechanistic localization (RML), a new operating model for enterprise AI. Written in conjunction with Workfabric AI, a new spinoff from Soroco, as well as our partner Cognizant. Co-authored with Hemanth Yamijala and George Nychis. Read the article to know Why most enterprise AI rollouts underdeliver How interaction data exposes the “invisible first mile” of work A real-world case study from a global enterprise How CXOs can drive real adoption and impact Read our HBR article Watch Ravi Kumar S in conversation with Rohan Murty Related read The context advantage: Why your company’s collective ethos is the new AI frontier Read article

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ViperGPT: Visual Inference via Python Execution for Reasoning

Soroco Admin — Wed, 16 Apr 2025 12:00:08 +0000

ViperGPT: Visual Inference via Python Execution for Reasoning 13th May 6:30 PM IST, 9:00 AM EST, 1:00 PM GMT About this Session Answering queries about visual inputs is a complex task that requires both visual processing and reasoning. In this talk, Sachit will demonstrate how large language models can be instrumental in reasoning within such settings, which extend beyond traditional language tasks. ViperGPT utilizes a provided API to access computer vision modules and composes them by generating Python code that is subsequently executed. This simple approach requires no additional training and achieves state-of-the-art results across various complex visual tasks. Sachit will also discuss how ViperGPT inspired the development of code-based agents and share insights on the future potential of such agents. About the Speaker Sachit Menon is a PhD student in Computer Science at Columbia University advised by Professor Carl Vondrick. His research centres around models trained at scale and ways to use them for novel tasks, such as using large language models to perform visual reasoning. About Tech Talks A regular series by Soroco, Tech Talks are expert-led technical sessions that deep dive into a specific area of technology and provide engineers valuable insights and tools. It also examines fascinating research, use cases and facilitates larger conversations around cutting-edge tech. Registration is now closed for this Tech Talk Watch the Talk See Scout in action. Schedule your demo now! Get in Touch

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[Engineering Blog Template] #2

Soroco Admin — Fri, 11 Apr 2025 14:42:02 +0000

Lorem Ipsum Dolor Sit Amet Replace with Author Name 11 May 2025 4 minute read Overview Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Nulla facilisi. Fusce at nisl ut justo vehicula fermentum. Curabitur ut libero nec justo tristique tincidunt. Integer vel sem vitae nunc finibus eleifend. Suspendisse potenti. Cras convallis massa ac sapien tristique, non bibendum sapien tincidunt. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Nulla facilisi. Fusce at nisl ut justo vehicula fermentum. Curabitur ut libero nec justo tristique tincidunt. Integer vel sem vitae nunc finibus eleifend. Suspendisse potenti. Cras convallis massa ac sapien tristique, non bibendum sapien tincidunt. Details Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam sit amet libero vitae massa tincidunt lacinia. Sed nec velit lorem. Quisque fringilla justo vel enim bibendum, non sollicitudin libero vestibulum. Cras in faucibus ante. Suspendisse ut dictum nisl. Curabitur luctus fringilla odio, sit amet faucibus magna posuere in. Nulla facilisi. In ac lectus purus. Nam sagittis sodales sapien, nec imperdiet libero convallis vitae. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Morbi sollicitudin magna a orci varius, at congue tortor egestas. Integer a ante id libero sagittis tincidunt. Morbi dignissim ante vitae nisl pulvinar, nec convallis metus sagittis. Nullam eleifend, ipsum a faucibus viverra, ante neque accumsan nisi, non tincidunt nunc diam sed enim. Mauris volutpat, sem non vehicula sagittis, dolor nulla gravida ligula, vitae sagittis nunc sem vitae elit. Donec sed urna ut nisi feugiat iaculis. Pellentesque vitae semper lacus, id condimentum turpis. Integer mollis semper diam, vitae feugiat neque dictum at. Etiam vel lectus libero. Mauris non ultrices tortor. Integer ut tortor vitae diam dictum porta. Integer scelerisque augue ut sem volutpat tincidunt. Quisque congue nulla in est convallis, at egestas enim rutrum. Vestibulum sed turpis quis orci ullamcorper bibendum. Vivamus feugiat vehicula nisl, sed dapibus justo blandit id. Aliquam erat volutpat. Nunc scelerisque volutpat tellus, nec faucibus justo tincidunt et. Suspendisse facilisis, metus id ultricies bibendum, sapien sem malesuada quam, ut egestas augue velit a libero. Morbi condimentum lorem sed sem fringilla, eget ultrices tortor sollicitudin. Pellentesque dapibus augue sit amet ligula tempor, ut efficitur mauris tincidunt. Curabitur eget augue nec leo vehicula luctus. Mauris suscipit sapien et lacus finibus, nec pretium nulla facilisis. Donec placerat tellus ac pulvinar gravida. Curabitur hendrerit, ante vitae porta condimentum, eros est luctus enim, sed sagittis lectus libero vel velit. Curabitur id suscipit orci. Nam et erat id nulla luctus mattis. Aenean ut ex tortor. Suspendisse id purus in orci vestibulum lacinia at ac turpis. Integer placerat enim at lacus efficitur, sed vestibulum nisi ultrices. Aenean a lacus justo. Vestibulum imperdiet bibendum sem, et accumsan turpis tempor in. Suspendisse potenti. Vestibulum congue congue lorem, sit amet ultrices tellus hendrerit nec. Curabitur at sapien et augue porta vulputate. Etiam nec lacinia sem. Etiam cursus ipsum at malesuada lacinia. Curabitur id suscipit orci. Nam et erat id nulla luctus mattis. Aenean ut ex tortor. Suspendisse id purus in orci vestibulum lacinia at ac turpis. Integer placerat enim at lacus efficitur, sed vestibulum nisi ultrices. Aenean a lacus justo. Vestibulum imperdiet bibendum sem, et accumsan turpis tempor in. Suspendisse potenti. Vestibulum congue congue lorem, sit amet ultrices tellus hendrerit nec. Curabitur at sapien et augue porta vulputate. Etiam nec lacinia sem. Etiam cursus ipsum at malesuada lacinia. The figure below puts these projects, their scale, and complexity in perspective. Content Explorer See Scout in action. Schedule your demo now! Get in Touch

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[Engineering Blog Template]

Soroco Admin — Fri, 11 Apr 2025 14:19:54 +0000

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Optimizing Causal Graphs: A Modern Perspective

Soroco Admin — Tue, 07 Jan 2025 05:24:04 +0000

Optimizing Causal Graphs:A Modern Perspective 21st Jan 2024 6:30 PM IST | 8:00 AM EST | 1:00 PM GMT About this Session Two important factors in modern machine learning systems are interpretability and causality. These factors help us determine the basis for decisions made by machine learning algorithms, which, in turn, improve the system’s performance and prevent unexpected failures. Understanding how these factors influence each other is crucial for building trustworthy and transparent models. Graphical models, particularly directed acyclic graphs (DAGs), are effective tools for representing these relationships. They clearly illustrate how one factor can directly cause another. However, despite their intuitive nature, determining the structure of DAGs from data is challenging. This process often requires testing numerous possible combinations and relies heavily on heuristic methods. In this talk, Kevin will present a fresh perspective on this challenge. Instead of traditional methods, he will demonstrate how to transform the problem into a smoother, more streamlined optimization process that avoids complex discrete combinations. This innovative approach opens up new possibilities for efficiently and generically discovering causal relationships in data. About the Speaker Kevin Bello is a Research Scientist at Soroco. Previously, he was an NSF Computing Innovation Fellow and a postdoctoral researcher jointly affiliated with the Machine Learning Department at Carnegie Mellon University and the Booth School of Business at the University of Chicago. Before that, Kevin earned his Ph.D. in Computer Science from Purdue University. About this Session A regular series by Soroco, Tech Talks are expert-led technical sessions that deep dive into a specific area of technology and provide engineers valuable insights and tools. It also examines fascinating research, use cases and facilitates larger conversations around cutting-edge tech. Registration is now closed for this Tech Talk Watch the Talk See Scout in action. Schedule your demo now! Get in Touch

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What is Interaction Data and Why You Should Care?

Soroco Admin — Thu, 17 Oct 2024 07:05:44 +0000

Blog What is Interaction Data and Why You Should Care? Table of Contents Drive value across your organization, one team at a time. We’ll show you how. Get started What is Interaction Data? Interaction Data refers to the digital footprint left by your teams as they interact with various systems and applications, such as ERP, CRM, or custom software. These interactions can include anything from mouse clicks, keystrokes, and navigation between applications. Interaction Data paints a real-time, detailed picture of how work is being done, from individual tasks to broader workflows. Collecting and analyzing this data allows you to understand how processes are executed, revealing the inefficiencies, bottlenecks, and opportunities that remain hidden when only high-level data or task completion rates are monitored. Getting intelligence out of this Interaction Data requires strong AI models to provide a comprehensive, user-centric view of business processes, like Scout. Scout captures every detail of how users interact with applications in real-time, creating a dynamic work graph that can be analyzed for process improvement, workforce optimization, and automation opportunities. While traditional process mining tools focus on structured event logs from enterprise applications, technologies like Scout take things a step further by capturing unstructured, user-driven data. It uses AI and machine learning to contextualize user actions, helping your businesses understand not just what work is being done, but how and why it’s being performed. How Do You Build Intelligence from Interaction Data? Tools like Scout, leverage advanced technology components to provide insights that were previously unattainable. Some key functionalities include: Data Capture and Cleaning Captures Interaction Data from multiple enterprise applications and devices, including desktop environments, even virtual desktops and mainframes. This data is then cleaned and processed to ensure accuracy and relevance. Work Graph This cleaned data is then used to generate work graphs, which visualize user workflows and allow your businesses to analyze deviations, bottlenecks, and other process inefficiencies. Data-backed Recommendations Uses machine learning models to classify tasks, identify automation opportunities, and highlight areas for improvement. It can also simulate various scenarios, allowing businesses to make data-driven decisions Why Should You Care About AI and Interaction Data? The value of AI and Interaction Data is far-reaching, impacting everything from operational efficiency to employee productivity and customer satisfaction. Here’s why you should care: Holistic Process Visibility Provides a complete picture of your business processes by capturing both structured and unstructured data. Traditional process mining solutions only capture around 40% of the data, while the remaining 60%—the human-machine interactions—remain untracked. With AI and Interaction Data, you get a holistic view that helps uncover hidden inefficiencies. Data-Driven Decision Making Equips decision-makers with fact-based insights into how work is being performed. By analyzing user interaction patterns, your enterprises can make informed decisions about process changes, workflow optimizations, and automation strategies that align with your business goals. Improved Employee and Customer Experiences Your organizations can improve both employee productivity and customer satisfaction. By identifying repetitive manual tasks, you can implement automation to reduce employee burnout and focus on value-adding activities. For customers, optimizing behind-the-scenes processes means faster response times and a smoother overall experience. Enhanced Efficiency and Cost Savings Granular insights help your businesses uncover areas where time is wasted—such as application toggling or non-productive tasks. By identifying these inefficiencies, your companies can implement solutions to streamline processes, resulting in cost savings and higher ROI on digital transformation initiatives. Scaling Automation Pipeline One of the standout features of AI and Interaction Data is its ability to identify tasks that are ripe for automation. Whether it’s simple data entry or more complex workflows, you can highlight areas where automation can make the most impact, helping your businesses scale their automation efforts more effectively. AI-Powered Process Optimization Incorporates AI and machine learning to provide predictive insights, identify trends, and suggest optimizations. This means your processes can evolve dynamically, improving as new data is collected, rather than relying on periodic reviews or audits. Bottomline In a world where hyper optimization of your people, processes, and technology is becoming the lifeline of business success, visibility into how work really happens in your organizations is of utmost importance. Leveraging the power of AI and Interaction Data can not only just give you that critical visibility but also find & fix your operational bottlenecks. This allows your enterprises to reduce costs, improve revenue and elevate customer and employee experience. By adopting AI and Interaction Data, your organizations can unlock the full potential of their digital transformation efforts, driving better business outcomes in a competitive market. Talk to us to know how Soroco is using the power of AI and Interaction Data in helping organizations globally to enhance and smoothen their digital transformation journey Book a demo Everest calls this technology category DII. To learn more about AI and Interaction Data, download the complete playbook here. Download the Everest Group Playbook for free Disclaimer: This content is created with inputs from the Everest Playbook on Digital Interaction Intelligence 2024. Click here to learn more Additional resources Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Forbes article Lighting up the dark side of the moon Read article Load more

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AI powered business growth with Scout

Soroco Admin — Mon, 09 Sep 2024 07:47:15 +0000

Webinar AI powered business growth with Scout Webinar co-hosted with Tech Mahindra, BPS function About this Session Business leaders worldwide are driving three primary objectives: Revenue growth Improved profits Enhanced customer and employee experience To achieve these goals, they need deep insights into how businesses operate at a granular level, identify points of friction, and eliminate them. Watch this Webinar on Soroco’s AI powered platform – Scout for Business Operations that is designed to provide strategic insights into how teams experience work, identify sources of friction and eliminate them through AI recommended interventions. This webinar will help you understand how Scout can be leveraged for business growth to shape your digital transformation journey by helping you light the dark side of the moon. Unique Value Proposition Find, at any scale. Analyse where your company is expending manual effort. Identify where exactly does manual effort affect business outcomes. Get complete data to streamline manual work. About the Speaker Arindam Sengupta, Head of Partner Success (GSI), Sales, Soroco Arindam Sengupta is the Head of Partner Success at Soroco for GSI and Consulting Partners. He has overall 16+ years of experience in the industry across IT product organizations and consulting firms. His experience includes specializing in large scale technology-enabled business transformations, including Process Mining, RPA and Intelligent Automation across a global customer base. Related Webinars AI-powered Business Growth with Scout- IT services function Abhijit Shroff Head of Product Growth Watch Webinar Using Scout to reveal improvements to your business KPIs Marcos Nunes Director of New Business, Growtec Watch Webinar View all Webinars See Scout in action. Schedule your demo now! Get in Touch

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A Hattrick with Task Mining 2.0. Soroco named a leader in Everest Group’s Digital Intelligence Interaction Peak Matrix Assessment 2024

Soroco Admin — Mon, 09 Sep 2024 01:57:07 +0000

Analyst Report A Hattrick with Task Mining 2.0. Soroco named a leader in Everest Group’s Digital Interaction Intelligence PEAK Matrix Assessment 2024 Download this report to understand how Soroco’s DII solution Scout helps you Analyze work patterns to uncover new opportunities for optimisation through workforce, application and compliance related insights. Mine human-machine interaction using supervised and unsupervised techniques to classify tasks, processes and underlying sequences. Identify automation potential for processes and provides OOTB apps to rationalise spending on technology. The evaluation was based on specific criteria that analyzed the company’s flagship product, Scout, along with 21 other Digital Interaction Intelligence (DII) technology providers for their market impact, vision, and capability. Soroco leads the category because of these reasons as outlined in the report,. Analyzing work patterns With Soroco’s Work Graph Explorer, data scientists and analysts can uncover new opportunities for optimization from the work graph and offers workforce insights, application insights, and compliance metrics. Mining human-machine interaction Leveraging both supervised and unsupervised learning techniques to classify tasks and processes, Soroco’s Scout classifies team interactions and annotates process maps with business context, thereby discovering underlying sequences in the process. Identifying automation potential Soroco offers the ability to determine the automation potential for processes and provides multiple out-of-the-box applications to rationalize the spending on technology and data storage to eliminate data silos and duplication. “Soroco’s Digital Interaction Intelligence product strategy is focused on leveraging AI to help enterprises unlock business value from user interaction data. A large and growing client base, depth and breadth of product functionalities, and continued investments in innovation and AI capabilities have helped Soroco reinforce its position as a Leader on Everest Group’s Digital Interaction Intelligence Products PEAK Matrix® 2024. Ease of deployment, leverage of AI, and comprehensive nature of insights are some of the key strengths highlighted by its clients.” Amardeep Modi, Everest Group Amardeep Modi, Vice President at Everest Group See Scout in action. Schedule your demo now! Get in Touch

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Transforming Customer Onboarding for a Global Investment Bank with Scout by reducing operational cost by 30%

Soroco Admin — Wed, 04 Sep 2024 07:55:42 +0000

Investment Banking Scout transforms customer onboarding for a global investment bank by reducing operational cost by 30% Investment Banking Transforming Customer Onboarding for a Global Investment Bank with Scout by reducing operational cost by 30% The Challenge A leading U.S.-based investment bank aimed to optimize its Global Markets Operations, with a focus on improving customer onboarding and trade settlement processes. The initiative sought to analyze the ‘cost to serve’ for top clients while enhancing the end-to-end customer experience. Industry Investment Banking Location US Attempted Solution before Scout Extensive efforts included conducting interviews, analyzing thousands of customer interactions, and creating 200 dashboards The bank assembled a team of senior associates and consulting partners to develop a sustainable solution. Despite extensive efforts, including interviews, analysis of thousands of customer interactions, and the creation of 200 dashboards, the results were insufficient.The solutions lacked deep insights into critical metrics and failed to provide a comprehensive view of the end-to-end process, leaving essential questions unanswered. Enter The Head of the Global Market Division introduced Scout to provide data-based, near real-time insights into the core business questions. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed the ‘cost to serve’ by breaking down total team effort across applications, emails, and documents for each client. It was able to measure this across three metrics: Scout’s AI Model could be quickly put into action because it didn’t need complicated integration with current systems. Responsiveness The speed at which the client receives a response. Seamless experience Assessed by the number of interactions needed per case. Variations Identified client-specific variations or ways in which clients were serviced. Responsiveness the speed at which the client receives a response. Seamless experience assessed by the number ofinteractions needed per case. Variations identified client-specific variations or ways in which clients were serviced. Scout’s AI to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the customer onboarding team and connected them to business activities, by analyzing interactions between the onboarding team and their systems. It then automatically classified these work patterns as either core or non-core activities. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more Within two weeks, based on this analysis, Scout’s AI model delivered the following insights: Identified specific pain points, such as the team’s heavy reliance on MS Outlook and Excel. Uncovered an opportunity for email and process automation, which could boost efficiency by nearly 10% through toil reduction. Suggested automating data flow between external websites, spreadsheets, and Outlook, which could further enhance efficiency by 17%. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Armed with these insights, the management team initiated a series of quick and deep fixes to address the identified issues. Quick Fixes These were ‘no-code’ fixes based on standardisation and user training. Standardization of Data Inputs The management team enforced standardization of data inputs across teams in the Customer Onboarding, Equity Essential Services/Trade (Buy & Sell), Security Settlement, and Prime Brokerage functions. This standardization enabled the generation of new metrics and insights.This fix – Reduced the total effort required to service clients by the Global Market Operations teams and lowered cost to serve per client. Allowed teams to focus on high-impactactivities, better align workforces, and deliver an improved, seamless client experience. Standardization of Reporting Specific reports were developed using Scout data to provide a unified view of effort and touchpoints for each client service request. These reports tracked: Effort spent per client at various lifecycle stages. The number of follow-ups per client. The total effort expended across the entire service process. This standardization allowed for a clearer understanding of resource allocation and client interactions at every stage. Deep Fixes Systemic and long-term fixes planned across the organisation. Scout also provided detailed insights into the cost of unintegrated underwriting systems and applications, as well as the disconnection debt within the organisation. Armed with these insights, the management team initiated a comprehensive transformation program to eliminate, automate, and transition low-impact workloads that delivered minimal value to both clients and the bank. This enabled more effective workload prioritization. Additionally, the customer journey view in Salesforce CRM was enhanced by integrating Scout platform data. This integration provided Client Relationship Managers, Functional Leads, and Leadership with a single source of truth, offering visibility into key metrics and enabling them to monitor the impact of actions and interventions seamlessly. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read more It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout’s intervention led to significant improvements: 40% Improvement in Turnaround Times: For customer service and requests 30% Reduction in Operational Costs: Streamlining processes and eliminating inefficiencies 15% Enhanced Revenue from Operations: Attributed to increased trade volumes from large customers Elimination of Low-Impact Workloads: Enabling prioritization of high-value tasks How Scout Lit up the “Dark Side of the Moon” Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes. We call this lighting up the ‘dark side of the moon’. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more

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How Scout helped Worldnet International Rationalize their Application Portfolio and Reduce Disconnection Debt

Soroco Admin — Fri, 30 Aug 2024 06:37:12 +0000

Logistics How Scout helped Worldnet International Rationalize their Application Portfolio and Increase Tech Adoption Watch video The Customer Worldnet, a leading logistics company, operates in over 200 countries, ensuring seamless logistics for high-value shipments worldwide. They manage shipments for high-profile celebrities at some of the world’s most glamorous events, including the Oscars, Golden Globes, and the MET Gala. Worldnet utilizes cutting-edge technology, including AI and RPA for real-time tracking for their logistics. Industry Logistics Location Global 250+ Employees 200 Countries 250+ Employees 200 Countries The Challenge Worldnet developed an in-house transport management system to manage their shipments, but it wasn’t utilized as expected. This led to several challenges, including:Difficulty in identifying how users interacted with the system and the reasons behind its underutilizationChallenges in pinpointing the specific factors contributing to the system’s underperformanceReliance on feedback from frontline staff that proved insufficient for gaining accurate insightsThe necessity to seek external expertise to uncover and address the underlying issues effectively Enter Scout was onboarded to gather interaction data and provide insightful visualizations. Within weeks, Scout identified unknown interactions, revealing the “dark side of the moon” of their operations. This discovery led to actionable recommendations such as redesigning the user interface to enhance intuitiveness and functionality. Within 4 weeks Scout identified unknown interactions Scout’s AI to “find and fix” Step 1: Find Scout AI discovered that the user interface was not intuitive, was overly complex and lacked key functionalities available in other applications. It also revealed that Worldnet was using 370 different applications – more than the number of staff that existed in their organization. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more Step 2: Fix By implementing Scout’s insights, Worldnet was able to: Redesign the transport management system’s user interface, simplifying complex elements and adding key functionalities Develop a structured governance model to incorporate interaction data into existing strategic frameworks, facilitating better execution of recommendations Create a dedicated AI strategy under their business transformation strategy, driving significant efficiencies Manage their application portfolio by identifying opportunities to consolidate or retire applications It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout was able to drive the following business outcomes 16% increase in the utilization of their transport management system 2% reduction in their Application Portfolio Management (APM) footprint AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more

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How Scout Assisted Bayer Navigate a Complex SAP S/4HANA Migration

Soroco Admin — Thu, 29 Aug 2024 07:58:49 +0000

Pharmaceutical How Scout Assisted Bayer Navigate a Complex SAP S/4HANA Migration Watch video The Customer Bayer, a leading pharmaceutical company with over 110,000 global employees and an annual revenue of approximately $40 billion faced a significant challenge: migrating their complex SAP ECC systems to SAP S/4HANA as part of their CORE transformation program. This multi-year initiative aimed to streamline business processes across their global operations in the EMEA region. Industry Pharmaceutical Location EMEA 110,000+ Employees ~$40B Annual Revenue 110,000+ Employees ~$40B Annual Revenue The Challenge Massive Scaleand Complexity The multi-year S/4HANA migration required careful coordination and detailed planning across a vast array of processes and applications. Risk of Errors There was a substantial risk of errors during the fit-to-template analysis, which could lead to critical process failures. Undocumented Process Variations Variations in the as-is processes across core and satellite applications were undocumented, further complicating the migration effort.Scout’s expertise was crucial in overcoming these challenges, ensuring a smooth and efficient migration while significantly reducing manual effort for Bayer’s Customer Interaction (CI) team. Enter Within 4 weeks of deployment, Scout mapped 3600 hours’ worth effort across 18 users in Bayer’s Customer Interaction, Supply Chain and Master Data Management teams. Scout also began identifying redundant effort, validating benefits from Bayer’s CORE implementation. Within just 4 weeks of deployment Scout mapped 3600 hours’ worth effort The Pre-Migration Deployment of Scout To address these challenges, Scout was deployed to aid discovery analysis and facilitate a smooth transition. The deployment focused on: Business and Leadership Teams Gaining comprehensive visibility into all processes and understanding how work is executed. Identifying short-term improvements to processes ahead of the S/4HANA migration. Mapping interactions between SAP and other applications to ensure seamless integration. CORE Team Providing detailed insights into how SAP is utilized within processes. Offering data on usage at a module/T-code level to help test the viability of the future-state process. The Pilot Scout was initially deployed in Bayer’s supply chain teams in Spain, Portugal, and potentially Switzerland, which were pilot countries for the CORE implementation. The goal was to drive data-driven decisions to ensure the migration’s success and foster wider continuous improvement efforts. Scout’s AI to “find and fix” Step 1: Find Scout provided detailed insights into current processes by scouting 43 and identifying 73 variations, potentially discovering 19 non-core applications. Scout also identified and highlighted areas where manual checks were being performed, pinpointing specific processes that could be automated. With AI layered on interaction data, Scout offered comprehensive and consumable visibility across core and satellite applications. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more Step 2: Fix By implementing the insights provided by Scout Bayer was able to: Eliminate manual checks, saving the CI team 430 hours of effort De-risk the CORE pilot with a significantly lower cost to program. Significantly minimize integration efforts. It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout was able to drive the following business outcomes Eliminated manual checks to save 430 hours of effort Help de-risk the CORE implementation pilot Lowered cost to program AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more

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Transforming Business Challenges into Opportunities with AI

Soroco Admin — Tue, 27 Aug 2024 07:47:09 +0000

AI to Transform Business Challenges into Opportunities Harnessing Scout to unlock unprecedented growth 25th September, 2024 Venue: Aunt Bernie’s, New York 2:00pm EST onwards, followed by a networking session and an open bar Charting the Future Owing to the increasing need for digitalization and automation across industries, Soroco, a global leader in AI and Interaction Data, and TranSigma, a leading Process Intelligence Consulting Company, believe in redefining the approach towards digital transformation and automation through a joint initiative. Join this roundtable to understand how Soroco’s Scout AI model leverages human-machine Interaction Data to drive value for operations, process excellence, and automation across your organization. Build a case for automation, streamline processes, remove redundancies & improve visibility into how work gets done in your organization. Agenda The primary goal of this event will be to connect with other business leaders to discuss common challenges and ideas, highlight the transformative power of Soroco’s AI model, Scout and engage with key decision-makers in the region. We look forward to fostering collaborative opportunities and enhance how teams work everywhere, thus solidifying the collective pursuit aimed at achieving: Cost Reduction in Operations Maximizing Return on Technology Investments IT Transformation and Optimization Enhancing Customer and Employee Experience Who should attend This roundtable is designed for professionals from the field of automation, business operations, and digital transformations, including: Chief Digital Officers Chief Transformation Officers Chief Information Officers Chief Technology Officers Chief Financial Officers Continuous Improvement Leaders Automation Leaders Digital Transformation Leaders Business Process Owners BU heads Don’t miss a chance to connect with brilliant minds across the industry. Looking forward to your participation! Speakers Abhijit Shroff Head of Product Growth, Soroco Abhijit Shroff is the Head of Product Growth at Soroco for Scout. He has overall 24+ years of Read more Richard Metz CISSP – Chief Operating Officer, Transigma Richard Metz began his TranSigma journey in 2013, initiating their UK and Ireland operations Read more Sean Ferguson Director of Process Intelligence, Transigma Sean Ferguson began his career at TranSigma in 2018 after earning his master’s Read more Richard Metz CISSP – Chief Operating Officer, Transigma Richard Metz began his TranSigma journey in 2013, initiating their UK and Ireland operations Read more Soroco Offerings Soroco is on a mission to change how the world gets work done. Powered by multiple patents, its flagship product, the Scout AI model, generates a work graph – a map of friction points teams experience at work and their impact on business outcomes. Today, this graph drives productivity improvements in 150+ organizations globally, including several Fortune 500 companies. Cost Reduction in Operations By streamlining processes and identifying inefficiencies, Scout contributes to reducing operational costs and maximizing resource utilization – through process standardization, automation or Gen AI interventions. Maximizing Return on Technology Investments Customers leverage Scout to maximize returns on investments in automation and AI, ensuring optimal utilization and effectiveness. IT Transformation and Optimization From S4/HANA migration to application rationalization and modernizing mainframe systems, Scout is instrumental in driving IT modernization initiatives. Enhancing Customer and Employee Experience Scout helps deliver exceptional experiences across stakeholders—be it customers, employees, or vendors—through improved service delivery and interaction channels. TranSigma Offerings TranSigma’s mission is to enable their clients to surpass their business objectives. As leaders in the Process Intelligence space, TranSigma has spent 20 years implementing and improving business processes – saving companies tens of millions in the process and become trusted business advisors along the way. Process Intelligence TranSigma’s flagship offering leverages the use of innovative technologies across the fields of Interaction Data, Process Mining, and Business Process Management to help customers objectively understand their business processes and effectively improve outcomes Vulnerability Management With TranSigma’s decades long expertise in data science and process improvement, this revolutionary approach to managing cybersecurity vulnerabilities greatly reduces enterprise risk in the most cost-effective manner possible.

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A Glimpse into the Universe at Cosmic Dawn

Soroco Admin — Tue, 27 Aug 2024 05:53:59 +0000

The Universe at Cosmic Dawn and Demystifying It with ML 12th Sep 2024 6:30 PM IST | 9:00 AM EST | 1:00 PM GMT About this Session The epoch of Cosmic Dawn offers a promising window into the fundamental physics of our Universe. Current observations by the Hubble and James Webb Space Telescopes are extending our view into the distant past, revealing the properties of the earliest generations of galaxies. In this talk, a brief overview of the current state of this field, starting with observations of distant galaxies and concluding with the cosmic 21-cm signal will be discussed. Throughout, Nashwan will also highlight some analysis and machine learning techniques employed in extracting information from data. About the Speaker Nashwan Sabti is a new member of the Soroco AI/ML team. Prior to joining Soroco, he was a Research Fellow at Johns Hopkins University, where he conducted research at the intersection of physics, data science, and machine learning. His work mainly focused on exploring the early Universe, particularly the formation of the first generations of galaxies, from both astrophysical and cosmological perspectives. Nash grew up in the Netherlands, where he completed his MSc and BSc studies, and later lived in London for a few years while pursuing his PhD About this Session A regular series by Soroco, Tech Talks are expert-led technical sessions that deep dive into a specific area of technology and provide engineers valuable insights and tools. It also examines fascinating research, use cases and facilitates larger conversations around cutting-edge tech. Registration is now closed for this Tech Talk See Scout in action. Schedule your demo now! Get in Touch

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Wonderful with Soroco

Soroco Admin — Thu, 22 Aug 2024 13:36:20 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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The Wonderful Company Digital Interaction Intelligence Case Study

Soroco Admin — Thu, 22 Aug 2024 11:55:48 +0000

FMCG The Wonderful Company discovers 12000 hours of process improvement benefits in order processing and shipping processes with Scout Published by Everest Group for Soroco: Read here Enterprise overview Lynda and Stewart Resnick co-founded The Wonderful Company, a privately held US $5 billion global corporation that’s dedicated to delivering high-quality, healthy brands and improving the lives of the people who live and work in the regions where it operates. Its iconic brands include FIJI Water, POM Wonderful, Wonderful Pistachios, Wonderful Halos, Wonderful Seedless Lemons, Teleflora, and JUSTIN and Landmark wines. The Wonderful Company’s diverse holdings make it the world’s leading grower of tree nuts, America’s largest citrus grower, and the world’s largest flower delivery service. It is also a market leader in wines, bottled water, and pomegranate juice. Headquartered in Los Angeles, with major operations in California’s Central Valley and Fiji, its 10,000-person workforce is passionate about building a better world. In pursuit of this mission, the company has partnered with Soroco to utilize its digital interaction intelligence solutions to further optimize its internal processes. Industry FMCG Employees 10000 Headquarters Los Angeles Drivers of adoption Scale automation Leverage task mining to identify and prioritize automation opportunities across the organization. Continuous process improvement Adopt a fact-based approach to discover and analyze processes to continuously improve overall process efficiencies. Improve employee productivity Enable employees to realize their scope and potential for improving task execution. Approach to DII initiatives Project initiation A Digital Interaction Intelligence pilot project was introduced in 2022 to understand the impact on hours saved to perform different operations. Process selection Major stakeholders involved in process selection for deploying these solutions were the business, automation, continuous improvement, and IT project teams. Talent Wonderful leveraged Soroco to train its internal teams and to obtain solutions to queries surrounding various technology aspects. Organization structure While Wonderful has dedicated teams for continuous improvement initiatives, it also leverages Soroco stakeholders as needed to help implement task mining projects. Its digital transformation team is responsible for securing the budget for these initiatives. Strategic Payoff ~12000hrs of potential process improvement benefits discovered as a result of successfully implemented digital interaction intelligence for multiple processes. ~75% of the insights derived were converted into actionable items and leveraged in use cases. Status of these initiatives Current status 2 projects that involved order entry and closing process and shipping process have Digital interaction intelligence underway 15 users is now the scale of deployment with rotational licenses. 60+ use cases identified by Wonderful, using task mining or digital interaction intelligence solutions. Future plans Aim to enable continuous improvement across processes so that other teams can also start working independently.Continue to leverage digital interaction intelligence to identify more use cases across multiple projects.Expand the reach of the technology across different teams and deployments and ensure enhanced employee and customer experience through these initiatives. Challenges Addressing concerns from operational teams regarding the adoption of task mining / DII technology due to the apprehensions around increased visibility and transparency into the ways of working Lack of understanding about the applications and benefits that DII technology could offer Getting approvals from the IT team related to data usage in the initial phase of the journey Key Takeaways The team leading these initiatives needs to be present on the ground to help users gain exposure to technology and address their queries in real-time. Have an effective change management program in place to align with different stakeholders. This is crucial to secure executive sponsorship and buy-in and to establish proper communication channels. Gain deeper process understanding or leverage process SMEs to perform the analysis to identify relevant insights and improvement opportunities. Build highly collaborative cross-functional relationships to accelerate the delivery speed following these initiatives. To get more insights about Digital Interaction Intelligence and download the complete DII playbook by Everest Group Download the Everest Group Playbook

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BT with Soroco

Soroco Admin — Thu, 22 Aug 2024 11:20:35 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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BT Group Digital Interaction Intelligence Case Study

Soroco Admin — Tue, 20 Aug 2024 10:28:15 +0000

Telecom BT Group identifies a 20% improvement in operational efficiency by leveraging the Scout AI model Published by Everest Group for Soroco: Read here Enterprise overview BT Group is the UK’s leading provider of fixed and mobile telecommunications and related secure digital products, solutions, and services. BT also provides managed telecommunications, security network, and IT infrastructure services to customers across 180 countries. It serves a wide range of customers such as small and medium-sized enterprises, start-ups, wholesale customers, commercial premises, public sector organizations, and large corporate firms. The provider has expanded its presence globally across Europe, Asia Pacific, the Middle East, Africa, and America. The company partnered with Soroco to deploy Scout, its task mining / DII offering in 2021 to discover and analyze processes. Industry Telecommunications Countries 180 Location Europe, Asia Pacific, the Middle East, Africa, and America Drivers of adoption Process transparency Gaining visibility into the as-is processes that are executed across different operational teams Generate process insights Improving the accuracy of data for the operational teams to work on to generate reliable insights about business processes Improve employee productivity Helping employees realize the potential for improvement in executing their tasks Process improvement Leveraging a fact-based approach to discover and analyze processes for improving the overall process efficiency Approach to DII initiatives Project initiation DII was introduced in January 2021 to analyze user interactions across different applications and gain visibility into processes Process selection Process excellence team conducted PoCs across different business units to identify processes for deployment. DII was first implemented in BT Group’s GBS (Group Business Services) unit, for use cases such as order management and pricing excellence Talent This is built through internal training programs and it does not rely on any third-party service providers for talent. It introduced certification courses to train its employees Organization structure BT Group has implemented a hub and spoke CoE model wherein the central hub manages license management and best practice sharing, and the spokes within business units leverage the insights for process improvement initiatives Strategic Payoff Identified the potential to realize a 15-20% improvement in operational efficiency by leveraging digital interaction intelligence solutions Reduced the time to obtain the operational data that was otherwise challenging to obtain by manual methods Improved upstream and downstream processes by identifying and eliminating redundant processes Status of these initiatives Current status of these initiatives ~60 teams leverage DII across hundreds of projects. BT Group leverages DII for a wide range of use cases such as record to report (R2R), order management, billing assurance, pricing excellence, and helpdesk. Plans for these initiatives Become self-sufficient in leveraging DII and increase value realization efforts based on the identified opportunitiesExpand deployment to other teams and prepare department-specific use cases with clearly defined targetsContinue to leverage DII to identify more process standardization and automation opportunities Challenges Addressing concerns from operational teams regarding the adoption of task mining / DII technology due to the apprehensions around increased visibility and transparency into the ways of working Lack of understanding about the applications and benefits that DII technology could offer Getting approvals from the IT team related to data usage in the initial phase of the journey Winning insights Establish a CoE model within the organization to manage these initiatives or utilize existing CoEs to collaborate and manage these initiatives Create a business case to analyze the expense and resources consumed and the return and benefits that can be achieved for further adoption in other areas to overcome the barriers to adoption Have an effective change management program to have proactive and continuous communication with relevant stakeholders to educate them about the benefits of technology and address their concerns or apprehensions related to data privacy To get more insights about Digital Interaction Intelligence and download the complete DII playbook by Everest Group Download the Everest Group Playbook

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Analyst Report Widget for Home

Soroco Admin — Tue, 20 Aug 2024 09:31:03 +0000

Harvard Business Review Teach AI to Work Like a Member of Your Team Read more Read more

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Everest group dii report Widget for Home

Soroco Admin — Mon, 19 Aug 2024 06:55:24 +0000

Everest Group Soroco Tops Everest PEAK Matrix® for Digital Interaction Intelligence Download your copy Download your copy

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Nelshon hall slider 1

Soroco Admin — Wed, 14 Aug 2024 12:01:31 +0000

“Soroco was positioned as a Leader in NelsonHall’s 2023 Process Understanding NEAT evaluation in the Task Mining market segment due to its ability to create work graphs from human–computer interactions to support process transformations; this goes beyond simple RPA and into workflow automation, IDP, email templatization, and conversational AI. Soroco’s platform also remains one of the few task mining platforms to support the ingestion of process mining data.” Mike Smart, NelsonHall Neat

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Nelshon Hall slider 2

Soroco Admin — Wed, 14 Aug 2024 12:01:23 +0000

“To be featured as ‘Leaders’ for three consecutive years by an industry expert has cemented our unprecedented capabilities in this dynamic space. With AI and interaction data, enterprises can transform their business operations by capturing human-machine interactions, uncovering significant bottlenecks that are hurting enterprises. These hidden pain points have the potential to create actionable insights and with Soroco’s Scout AI model, enterprises can reach new heights in their digital transformation journey.” Samson David CEO, Soroco

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Tech Mahindra leverages Soroco to elevate customer success

Soroco Admin — Fri, 09 Aug 2024 11:12:50 +0000

Tech Mahindra leverages Soroco to elevate customer success Watch video

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Percipere’s take on the power of AI and Interaction Data

Soroco Admin — Fri, 09 Aug 2024 11:11:54 +0000

Percipere’s take on the power of AI and Interaction Data Watch video

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iac

Soroco Admin — Thu, 08 Aug 2024 11:17:07 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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Percipere partnership with Soroco

Soroco Admin — Thu, 08 Aug 2024 10:59:47 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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CGI partnership with Soroco

Soroco Admin — Thu, 08 Aug 2024 10:37:52 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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Apromore and Soroco announce strategic partnership

Soroco Admin — Thu, 08 Aug 2024 09:53:12 +0000

Partnership announcement Apromore and Soroco announce strategic partnership Read more Read more

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Driving the Future- Catalysts for Technology Transformation Excellence

Soroco Admin — Thu, 08 Aug 2024 09:43:16 +0000

Mention Driving the Future- Catalysts for Technology Transformation Excellence Read more Read more

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Scout – One Platform, Infinite Value

Soroco Admin — Thu, 08 Aug 2024 07:54:55 +0000

Scout – One Platform, Infinite Value Murali Manohar S Senior Manager, Partner Solutions, Soroco Ankur Sharma Senior Cloud Architect, Tech Mahindra Watch Webinar

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Using Scout to reveal improvements to your business KPIs

Soroco Admin — Thu, 08 Aug 2024 07:38:27 +0000

Using Scout to reveal improvements to your business KPIs Marcos Nunes Director of New Business, Growtec Abhijit Shroff Head of Product Growth, Soroco Watch Webinar

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Transforming customer experiences through our strategic partnership.

Soroco Admin — Thu, 08 Aug 2024 07:36:12 +0000

Transforming customer experiences through our strategic partnership. Taranvir Jouhar VP, Business Head, Tech Mahindra Watch Webinar

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AI-powered Business Growth with Scout- IT services function

Soroco Admin — Thu, 08 Aug 2024 07:31:30 +0000

AI-powered Business Growth with Scout- IT services function Abhijit Shroff Head of Product Growth Watch Webinar

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Tech Mahindra on enhancing customer success with Soroco

Soroco Admin — Thu, 08 Aug 2024 07:09:51 +0000

Tech Mahindra on enhancing customer success with Soroco Watch video

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How AI is helping the public sector up the ante on service transformations

Soroco Admin — Thu, 08 Aug 2024 06:22:28 +0000

CGI White Paper How AI is helping the public sector up the ante on service transformations Read more

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Events and Webcasts

Soroco Admin — Wed, 07 Aug 2024 12:58:21 +0000

AI-powered Business Growth with Scout for the BPS vertical Arindam Sengupta Head of Partner Success (GSI), Sales Watch Webinar

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E-Commerce

Soroco Admin — Wed, 07 Aug 2024 11:24:17 +0000

E-Commerce Watch now hidden-card-id

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Transforming customer experiences through our strategic partnership.

Soroco Admin — Wed, 07 Aug 2024 10:36:29 +0000

Transforming customer experiences through our strategic partnership. Watch video

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IOpex on Soroco’s impact in today’s dynamic industry landscapes

Soroco Admin — Wed, 07 Aug 2024 07:11:13 +0000

IOpex on Soroco’s impact in today’s dynamic industry landscapes Watch video

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Why Tech Mahindra chooses Soroco: A value proposition overview

Soroco Admin — Tue, 06 Aug 2024 07:04:41 +0000

Tech Mahindra’s choice: Exploring Soroco’s value proposition Watch video

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CGI on shifting customer perspectives towards Scout for digital transformation

Soroco Admin — Mon, 05 Aug 2024 07:15:27 +0000

CGI on shifting customer perspectives towards Scout for digital transformation Watch video

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IAC’s insight on the transformative power of AI and Interaction Data

Soroco Admin — Sun, 04 Aug 2024 07:08:47 +0000

IAC’s insight on the transformative power of AI and Interaction Data Watch video

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Wipro explores the value proposition of Soroco’s Scout AI model

Soroco Admin — Sat, 03 Aug 2024 11:32:59 +0000

Wipro explores the value proposition of Soroco’s Scout AI model Watch video

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Apromore’s take on how Soroco enhances customer value

Soroco Admin — Fri, 02 Aug 2024 07:05:57 +0000

Apromore’s take on how Soroco enhances customer value Watch video

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What sets Soroco apart: CGI’s insights into the value of the partnership

Soroco Admin — Thu, 01 Aug 2024 07:00:25 +0000

What sets Soroco apart: CGI’s insights into the value of the partnership Watch video

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Peak matrix 2024 slider 1

Soroco Admin — Tue, 30 Jul 2024 06:25:58 +0000

“Soroco’s Digital Interaction Intelligence product strategy is focused on leveraging AI to help enterprises unlock business value from user interaction data. A large and growing client base, depth and breadth of product functionalities, and continued investments in innovation and AI capabilities have helped Soroco reinforce its position as a Leader on Everest Group’s Digital Interaction Intelligence Products PEAK Matrix® 2024. Ease of deployment, leverage of AI, and comprehensive nature of insights are some of the key strengths highlighted by its clients.” Amardeep Modi, Vice President, Everest Group

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Soroco named Leader in Digital Interaction Intelligence Products 2024

Soroco Admin — Wed, 24 Jul 2024 08:12:42 +0000

Soroco Scores a Hattrick: Tops Everest Group’s PEAK Matrix® Assessment on Digital Interaction Intelligence 2024, Third Year in a Row Download the report today Learn why it matters for you This report evaluates 19 Digital Interaction Intelligence (DII technology providers, positioning them on Everest Group’s PEAK Matrix® as Leaders, Major Contenders, and Aspirants. It helps buyers select the best-fit provider for their digital transformation needs. Inside, you’ll find: Understand the evolution of DII from traditional Task Mining Assess DII products – in terms of their strengths & limitations Discover why Soroco ranks higher than other DII products Evaluate key DII technology and market trends Download the report today What sets Soroco apart? Deep Capture Engine Deep Capture Engine powered by Edge AI that collects interactions at the OS level — capturing metadata, browser DOMs, object IDs, and even green-screen layouts like Citrix. Smart Learning Smart Learning through both supervised and unsupervised models. Scout learns process signatures from SMEs, discovering underlying process sequences and unlocking insights into task classifications. Hierarchical Flow Graphs Hierarchical Flow Graphs empower business users to analyze workflows and get high-level business intent at a glance. Automation Potential Automation Potential identification, with out-of-the-box applications to eliminate data silos, optimize tech spend, and streamline processes. Continuous Monitoring Continuous Monitoring of processes against business KPIs like team productivity, employee experience, and more, with actionable recommendations. Generative AI & Adaptability Generative AI & Adaptability Clients love the ease of deployment and the use of cutting-edge generative AI in Scout’s use cases. What is Digital Interaction Intelligence? Discover how DII surpasses traditional task and process mining solutions with advanced AI capabilities, across industries and functions. Download the Everest Group Playbook See Scout in action. Schedule your demo now! Get in Touch

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DII Paper Widget for Home

Soroco Admin — Wed, 17 Jul 2024 05:32:52 +0000

Playbook Everest Group: First Ever Playbook on AI and Interaction Data Download your copy Download your copy

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S/4HANA Widget 2

Soroco Admin — Mon, 08 Jul 2024 12:56:21 +0000

“Companies who focus on adoption are the most successful in terms of meeting their business objectives.” Resulting IT

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S/4HANA Widget

Soroco Admin — Mon, 08 Jul 2024 06:47:13 +0000

“Companies with a deeper understanding of their processes and applications achieve better budget and schedule adherence.” Samson David, CEO, Soroco

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Global energy leader sees a 64% boost in Accounts Payable throughput

Soroco Admin — Fri, 28 Jun 2024 11:43:51 +0000

Oil and Gas Global energy leader sees a 64% boost in Accounts Payable throughput The Challenge A leading oil and gas company with a workforce of over 5,000, faced significant challenges within its Accounts Payable department. With around 4,000 invoices pending at any given time, the team struggled with an effort-intensive and time-consuming process, impacting overall efficiency and financial performance. Industry Oil and Gas Location Asia Pacific 5,000 Employees Attempted Solution before Scout Traditional methods like RPA and manual discovery added to the team’s workload Prior to Scout’s involvement, the organization’s leadership attempted to address these challenges through RPA and manual discovery. However, these efforts did not lead to the desired improvements in invoice processing efficiency, on the other hand it added more workload to the already stretched teams resulting in decreased productivity. Enter The Head of Transformation introduced Scout to tackle these challenges and to gain a deeper understanding of the actual processes in place. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how the Accounts Payable team interacted with various applications like SAP, Coupa and other applications like email and chat to map how work happens and identify inefficiencies. Scout was easily deployed as there were no complex integrations involved. Scout to “find and fix” Step 1: Find As the first step, the Scout’s AI model decoded the work patterns of the Accounts Payable team, distinguishing between core and non-core activities by analyzing their interactions with business management applications. Based on this analysis of 9 sub processes in 5 weeks of fresh data collection, Scout’s AI model provided the following insights: Harvard Business Review Do You Know How Your Teams Get Work Done? Read more Scout analyzed 9 sub-processes over 5 weeks and provided deep insights. 33% of the team’s effort was spent on correcting activities which should have been ‘First Time Right’ including manual data entry and handling exceptions, due to information silos and disconnected systems. 40% of these were repetitive actions like rule-based validations in SAP. This startling statistic pointed to a work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Based on the above insights, Scout’s AI model recommended two levels of fixes: Quick Fixes These were ‘no-code’ fixes based on standardization and user training. Enhance the 3-way bot Enhance the 3-way bot Prioritize enhancing existing 3-way bot capability to further automate exception handling cases with a potential to deliver 64% higher throughput. Cognitive automation Cognitive automation Programs to integrate front and back-office workflows and enhance system functionalities were launched, addressing unintegrated systems, data duplication, and fragmentation. Deep Fixes Systemic and long-term fixes planned across the organization Reduce disconnected debt Reduce disconnected debt Scout’s findings led to a recommendation of a single case management system (either COUPA or SAP) to streamline invoice journey across one single flow. This can reduce the disconnected debt arising from using disparate and disconnected systems. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all these recommendations, privacy was of utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues below: Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff With Scout’s insights and the subsequent interventions, the following improvements were achieved: 64% increase in throughput, significantly reducing invoice backlogs 30% decrease in manual effort, lowering cost to serve. 20% improvement in first time right cases, enhancing operational efficiency How AI connects interaction data to business outcomes Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes. We call this lighting up the ‘dark side of the moon’. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more See Scout in action. Schedule your demo now! Get in Touch

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Improved response time by 40% leading to a boost in CX scores for one of the World’s Leading Airline with Scout

Soroco Admin — Fri, 28 Jun 2024 06:27:53 +0000

Airline One of the world’s leading airlines Improved response time by 40%, significantly boosting customer experience scores with Scout Email it to me The Challenge A leading Business Process Services company in Europe, employing over 15,000 staff with a global reach, encountered significant operational challenges servicing their top-tier airline clients. The operations teams were only meeting 50% of the Service Level Agreements (SLAs), posing a serious risk of contract non-renewal. This situation threatened not only the service provider’s revenue but also the airline’s reputation due to subpar customer service. Industry Airline Location Europe 15,000 Employees Attempted Solution before Scout 2,000 hours requested for workshops, adding to team stress and attrition A SWAT team was appointed to rectify this problem. Despite deploying a process mining solution and conducting manual ‘discovery workshops’ to decipher the team’s work patterns, these efforts were insufficient. The process mining tool took too long to implement and failed to provide a clear view of operations. Moreover, the workshops demanded roughly 2,000 hours from the already stretched teams, exacerbating stress and attrition issues. Enter The Head of Operations decided to implement Scout to find and fix this problem. Scout was deployed swiftly and started generating insights without the need for complex system integrations. Scout unveiled critical insights within 2 weeks Scout to “find and fix” Step 1: Find Scout unveiled critical insights within two weeks: The front-office team spent just 45% of their time on primary tasks With the rest consumed by inefficient processes, including time spent toggling between applications, fetching customer details and searching for flight options all from different systems and applications. This highlighted the major disconnection debt the organization was carrying due to information silos. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more The back-office team dedicated 75% of their efforts to redundant tasks With a lack of standardized communication templates affecting customer experience. This surprising statistic pointed to a significant work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Quick Fixes These were ‘no-code’ fixes based on standardization and user training. RPA Bots for Post-Call Analysis RPA Bots for Post-Call Analysis Automated bots were introduced to generate post-call notes, saving approximately 8% of the front-office team’s bandwidth and reducing customer call waiting times by 5%. Email Communication Templates Email Communication Templates Programs to integrate front and back-office workflows and enhance system functionalities were launched, addressing unintegrated systems, data duplication, and fragmentation. Deep Fixes Systemic and long-term fixes planned across the organization. Digital Transformation Initiatives Digital Transformation Initiatives Programs to integrate front and back-office workflows and enhance system functionalities were launched, addressing unintegrated systems, data duplication, and fragmentation. Automated Customer Verification Automated Customer Verification A voice bot integration significantly reduced call times, and an upgrade to the ticketing system optimized alternate flight searches, decreasing active call times by 15-20%. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all these recommendations, privacy was of utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues below: Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff With Scout’s insights and the subsequent interventions, the following improvements were achieved: Call time for voice calls reduced by approximately 30% Overall customer waiting times decreased by around 20% Email communication response times improved by about 40% Net Promoter Score (NPS) for voice calls improved by 200 basis points Operator productivity across both teams increased by 30%-40% How AI connects interaction data to business outcomes Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes. We call this lighting up the ‘dark side of the moon’. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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CGI leveraged Scout to pinpoint resource intensive processes within a large local council to reduce its operational workload by 67%

Soroco Admin — Mon, 24 Jun 2024 07:04:07 +0000

Government council CGI leverages Scout to identify opportunities within a large local council and reduce its operational workload by 67% The Challenge CGI, a global leader in consulting and IT services with over 80,000 employees, encountered a significant challenge with one of its clients. Scotland’s third-largest historic county, employing over 18,000 people, was under pressure to uphold customer service standards while dealing with budget constraints. To address this, CGI launched a digital transformation program for the council, aimed at achieving a 5% efficiency gain by streamlining operations, reducing manual effort, and enhancing work efficiency. However, CGI faced significant hurdles in identifying and prioritizing processes for automation and improvement for the council. They needed a solution that could: Identify the most resource-intensive processes and determine inefficiencies Quantify the potential benefits of process optimization and prioritize them for transformation These objectives had to be met without placing substantial burdens on their business teams. Industry Government council Location UK 18000+ Employees Attempted Solution before Scout CGI analysed that roughly 80% of the council’s cost was spread across 20% of business functions Business leaders within the council attempted to identify and shortlist processes for transformation by holding workshops, interviewing process SMEs, and examining existing documentation. The SMEs struggled to quantify potential benefits and prioritize processes for improvement due to a lack of empirical process data. . This led the council to seek recommendation from CGI, who analysed that roughly 80% of the council’s cost came from 20% of business functions which were handled by desk-based teams. Essentially, a few key functions were costing a lot, so focusing on these areas could help in managing or reducing costs. However, manually exploring these functions would be a mammoth exercise. Therefore, CGI recommended adopting an AI solution to: Provide detailed process insights through interactive flowgraphs and auto generated documentation Identify resource intensive processes and quantify process inefficiencies within the business functions, and   Prioritize digital transformation initiatives  Enter To achieve a comprehensive understanding of processes, CGI leveraged its domain expertise and onboarded Scout to find and fix problems. The council chose the accounts payable function for the pilot. The Scout AI model was quickly put into action because it didn’t require complicated integration with current systems and was compatible with the customer’s application stack. Scout analysed how teams interacted with various applications, mapping out how and why work happens the way it does, within the accounts payable function of the council.  Within a week, Scout discovered that 70% of the team’s effort was spent on 4 major processes Within a week, Scout discovered the following key insights: 70% of the team’s effort was spent on 4 major processes. 20% higher reliance on core invoice processing apps was observed compared to other similar functions across Scout customers, indicating streamlined operations Scout to “find and fix” Step 1: Find As the first step, the Scout AI model analysed interactions in the accounts payable function with business applications and recognized repeated patterns of work. This analysis revealed which processes consumed most of the team’s effort. Scout then deep dived into the shortlisted processes and delivered the following key insights to guide the business in its transformation journey: Harvard Business Review Do You Know How Your Teams Get Work Done? Read more The intelligent document processing (IDP) solution implemented for select suppliers was found to deliver 7% savings in processing effort per invoice compared to the ones that were processed using the legacy system. 0.5% of suppliers accounted for 15% of the effort spent in the legacy system. 2.3x time was spent on invoices that required re-work versus time spent on straight through invoices. >80% of effort was concentrated on structured applications across major processes, highlighting significant potential savings through automation. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Based on the above insights, two levels of fixes were recommended: Quick Fixes Efficient supplier migration Scout identified that the effort per invoice could be reduced by 7% if suppliers were transitioned to the IDP solution. By prioritizing the top 0.5% who currently contribute 15% of the effort on the legacy system, the council could achieve substantial savings by migrating suppliers within a short timeframe. Streamlining process documentation initiative The council could reduce operational workload, standardize training, and automate the creation of process documentation across various processes and variations by leveraging Scout. Deep Fixes Systemic and long-term fixes to improve operational efficiencies. Enhance existing IDP system to match invoice lines accurately Scout highlighted that there were significant digital gaps in invoice processing; and these were predominantly due to manual effort being spent to match invoice lines and to upload invoices in the system. While the existing system automatically updated invoice headers, users had to manually match line items and send emails to suppliers for discrepancies. By enhancing the system to match invoice lines and sending out automated responses to suppliers, the council could significantly improve efficiency and save 70% of manual effort spent on processing invoices. Deploy RPA bots Detailed insights were generated in the Statements and Non-PO invoicing processes. Scout assessed a savings of ~ 40% when RPA bots were deployed to service these requests. AI driven Process Prioritization The AI prioritized the assessed process for improvement based on operational effort, ease of implementation, and potential savings. This approach facilitated the development of an automation pipeline guided by data-driven recommendations. It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, CGI leveraged the Scout AI model and was able to achieve the following business outcomes: 7% reduction in processing effort per invoice by migrating suppliers to the IDP solution 67% efficiency savings across all scouted processes 65% efficiency savings on invoice processing by enhancing the existing solution 40% savings by deploying RPA bots… Continue reading CGI leveraged Scout to pinpoint resource intensive processes within a large local council to reduce its operational workload by 67%

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DII 2

Soroco Admin — Tue, 18 Jun 2024 07:19:12 +0000

“All you need, to get started on AI and Interaction Data”

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DII 1

Soroco Admin — Tue, 18 Jun 2024 07:17:50 +0000

“The most comprehensive playbook ever”

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Global-scale Image Storage with Order(s) of Magnitude Less Space

Soroco Admin — Thu, 13 Jun 2024 08:28:42 +0000

Film: Global-scale Image Storage with Order(s) of Magnitude Less Space Wolfgang Richter 15 February 2021 7 minute read Overview What if we could store 10-100x more photos? How many more memories would we keep? How many new applications would we invent? Soroco’s Film, a state-of-the-art image storage system, is a landmark rethinking of the storage stack for modern computer vision, machine learning, and web serving workloads achieving out of the box measured 12x storage savings on big data image data sets and 100x potential with optimization. In 2017, Soroco began storing big data for our Scout Enterprise product. We realized very early on that we had to drastically reduce our disk footprint, or else either we or our customers would have to build a storage layer that rivals large public clouds like AWS, Azure, or Google Cloud! That’s when we invented Film. Out of the box we achieve 12x storage reduction, and we believe that current optimizations we are researching can push that beyond 100x for our workload—achieving two orders of magnitude of on-disk storage saving. Getting to this point required a rethink of the storage layer for image data in combination with the workloads we wanted it to serve. We essentially compress and deduplicate pixel data while maintaining required accuracy for computer vision, machine learning, and other algorithmic workloads. While we use Film for screenshots, early tests show that it works well even for general photos as sampled from data sets like ImageNet. We have two key innovations in our state-of-the-art image storage system Film: Transcoding large (1,000+) groups of loosely related (temporally and spatially) images into large containers, and, Sorting those images by a perceptual hash to present them in an optimal order for pixel-level deduplication by a video codec Lossy Storage is OK Soroco is interested in machine workloads. We don’t have to show most of our images back to people, except in limited use cases. Thus, we only cared about keeping their fidelity just good enough for computer vision and related workloads. Critically, we don’t need pixel-by-pixel perfect image retrieval. Write once randomly, read many (usually) sequentially Our workload is write once, read many times. For our computer vision and machine learning applications, we can guarantee that the data sets are read off disk in sequential order. People-facing workloads may require random retrieval, but these were limited in our use case. We also feel that, in general, image retrieval is typically not in random order which is why caching layers often work so well. Popular images are easy to cache, and when an individual user browses their albums, we can guarantee an almost sequential retrieval pattern. Thus, we do not need to store each image separately for quick, random access. This means we can cut down heavily on wasted file system metadata and efficiently use a larger container for the images. Similar to a column-store style database, Film collates many related images in large containers. What we can’t control are the write patterns. We know users are likely, in our workloads, to upload many images at once or as a stream throughout the day. But, there is definitely more randomness in write patterns across a population of users. We do have to handle fast, high volume random insertions across our user population. This means we need a scalable approach to grouping images that doesn’t slow down the write path. Storing 12x More Pictures with Pixel Deduplication Image similarity with duplicate pixels stuck out to us as the primary cause of wasted space on disk. Why are we storing so many duplicate pixels? Actually, we unfortunately had a pathological case with our Scout Enterprise product. Our image data was screenshots from across the enterprise. Of course, desktops within one organization all look very similar. Often they have the same desktop background. Often, enterprise users are running similar software. For example, many users may work on Microsoft Excel for many hours. All of their screenshots would contain duplicate pixels, and individual users would have large amounts of duplicate pixels as they work on the same Excel file. So now the question becomes how will we deduplicate the pixels in massive amounts of screenshots? We very quickly realized that video codecs were a perfect solution to our problem, even though they weren’t designed to solve it. Video codecs deduplicate pixels inside frames and across multiple frames in a video stream taking advantage of temporal and spatial locality. We chose an off-the-shelf video codec HEVC/H.265, although AV1, VP9, or any other suitable codec could be used as a drop-in replacement. Film is future proof by virtue of its modular architecture from any advancements in video codec compression techniques. We set the quality settings of the codec to a high value (we default to CRF=28). Note, one optimization is to tune CRF based on the accuracy and recall of algorithmic workloads on top of our dataset. While we are OK with lossy storage, we still need Film capable of serving user-facing workloads such as web browser rendering and still be legible for some of our Scout Enterprise features. Figure 1. Storage container format. Many similar images are grouped together in containers, and then transcoded into a single, large video stream on disk. We break up images and transcode them typically 1,000 at a time. However, this is a tunable parameter and could really be anything. We felt it was a good tradeoff between transcoding time, number of unprocessed files we have to cache, and our server’s capacity (CPU, memory, disk). Increasing this value slows down transcoding times requiring more CPU, and decreasing this value lowers the maximum compression possible. Very large storage systems may want to increase this value to 100,000 or even 1,000,000+ especially for archival workloads. Storing 100x More Pictures by Optimizing Image Order with Perceptual Hashing There is a way we can save even more across our users and their images. Obviously, with images coming in roughly chronological order for each user, a video codec will find lots of duplicate pixels. For example, when they… Continue reading Global-scale Image Storage with Order(s) of Magnitude Less Space

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Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part I]

Soroco Admin — Thu, 13 Jun 2024 08:21:08 +0000

Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part I] Rohan Murty 26 February 2021 4 minute read Overview As a company, the more we build and scale Scout, the more we see strong parallels to how astronomers collect data, build pipelines, and find patterns in astronomical data. Like astronomers, at Soroco, we too worry about data capture, cleanliness, noise, aggregation, clustering, and filtering. Similarly, we too deal with similar scale of data capture on Scout and we focus on storage and indexing. And finally, Scout too has several pipelines for extracting faint patterns in the data, with many parallels to how astronomers find patterns in their data. In some cases, however, the challenges we see with Scout data exceed the challenges that astronomers face. For example, astronomers often have the facility to search for a pre-defined template or pattern. Or the rate at which Scout gathers data far exceeds the rate at which astronomy data gathering is growing (doubling roughly every two years). Hence, we look to astronomy to learn from the parallels while also being inspired to solve those problems specific to Scout. Hence, recently, we at Soroco, began working with Shravan Hanasoge, a professor at the Tata Institute for Fundamental Research (TIFR), on computational questions tied to Scout by looking for inspiration from how Shravan and his team think of finding patterns to detect gravitational waves. And this is not the first time we have collaborated with scientists and engineers working on astronomy. We even interview and recruit astronomers (apply here, if you’re interested!). So, when a couple of my younger colleagues, asked me why do we interview astronomers, read their papers, or even collaborate with them, we figured this may be a point of view that may be of broader interest to the community as well. This is not a conventional point of view among software companies and we believe it is fairly unique to how we, at Soroco, solve problems at scale, think of team composition, and value diverse talent. Our hope is this article will do justice to astronomers and readers will share our enthusiasm for astronomers. Astronomy and computing at scale Astronomy projects are about literally finding the needle in the haystack at scale. These projects generate a tremendous volume of data that are stored, ingested, and analyzed, to make new discoveries. Companies that build and manage platforms that collect, ingest, and analyze large volumes of data are typically well-funded and staffed by large teams of engineers, computer scientists performing R&D, product managers, QA teams, and SRE teams. On a relative basis, astronomy projects, however, are built by small teams of astronomers and engineers and operate on budgets that are several orders of magnitude lower (even when compared to a startup that has raised Series B). And yet, these teams in astronomy projects build systems that demonstrate tremendous scale by collecting large volumes of data, storing, indexing, processing, and ultimately finding a signal in the noise. And these systems survive and persist over extended periods. Long before the explosion of data in social networking companies, astronomy projects have often outpaced companies and the computing industry in their ability to build systems that process data at scale. This article hopes to highlight the incredible engineering work done by small teams of engineers and astronomers and here are some examples that illustrate this point on scale and complexity of astronomy projects. These projects produce large volumes of data, have to deal with scale, and are significant software engineering and innovation efforts. LIGO: The Laser Interferometer Gravitational-Wave Observatory (LIGO), a much vaunted project in the news, is dedicated to observing gravitational wave observations. LIGO generates about 1TB of data each night it operates. Future upgrades to LIGO as well as new planned sites such as LIGO-India will only increase these nightly data rates by several orders of magnitude. SETI@Home: The now defunct but one of the largest distributed applications (and in many regards the precursor to the sharing economy) started in the late 90s and ran for 21 years, was a distributed computing platform running on end-users’ desktops with the aim of analysing radio signal data to search for possible signs of extra-terrestrial life. Radio telescope data was aggregated to central servers and client software running on end-users’ machines, pull data from the central repository and analyze them locally. Just in 2008 SETI@Home was processing 100TB of data per year (in 2008 terms that was the size of the entire US library of congress). At its peak this distributed platform had a computing power of 668 teraflops across 5.2 million end-users running the platform. The underlying technology, built by the spaces sciences laboratory at Berkeley, was eventually open sourced as the BOINC platform – a platform for distributed computation that continues to be relevant even today in a wide variety of applications ranging from climate sciences to mathematics to biology and medicine. SDSS: The Sloan Digital Sky Survey is a project to construct a detailed 3D map of the universe. LSST: The large synoptic survey telescope is a large telescope slated to operate at very high data-rates and is equipped with a 3.2-billion-pixel camera that is capable of recording the entire visible sky twice each week. TMT: The thirty-meter telescope is an extremely large telescope (ELT) being built in Hawaii and will likely be the largest telescope ever built, is a multi-national project spanning research teams across the US, Israel, China, Canada, and India. The figure below puts these projects, their scale, and complexity in perspective. Source: Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy The ever-increasing data-rates per night of various projects over the past two decades. So, what does it take to store, index, and process data at such high-data rates? What kinds of queries can one run on this sort of data and what is the most efficient architecture for it? What pipelines are necessary to process the data and how are they integrated? What is the data model… Continue reading Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part I]

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Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part II]

Soroco Admin — Thu, 13 Jun 2024 08:03:20 +0000

Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part II] Abdul Qadir 4 june 2021 15 minute read Diving deeper into ZTF Of all the projects we have come across in astronomy, we see a strong parallel between the Zwicky Transient Facility (ZTF) and Scout. ZTF is basically Scout for the night sky. Or Scout is ZTF for the enterprise. Both systems span multiple areas of computing and at the heart of it solve a similar problem – how do you find faint patterns from noisy observational data at scale? ZTF is an automated system of telescopes that find transients (such as gamma ray bursts, comets, etc.), at Palomar/Caltech and generates ~ 4TB per night (assuming 100 observational nights in a year this is about 400TB / year). ZTF consists of a base platform, which collects, cleans, and stores the data. It is then processed through a series of successive pipelines to refine it and find patterns. Subsequently, the processed data, rich with possibilities, is then extended to address multiple astroinformatics questions. At the heart of it, ZTF is meant to find new patterns by comparing these patterns to previously known discoveries to ascertain the validity of the newly found pattern. Conceptually this is an example of what ZTF does: Source: ZTF Once a pattern has been discovered, ZTF classifies the new pattern or ‘alert’ into bins such as (“variable star”, or a false detection, etc.). Here is a snapshot of how ZTF classifies light curves or observations. Think of light curves as a particular hash or signature of an astronomical phenomenon. Here is an example of a light curve. Source: The ZTF Source Classification Project: I. Methods and Infrastructure These light curves are classified using a combination of machine learning and deep-learning. Here is a schematic of how ZTF classifies light curves. Source: The ZTF Source Classification Project: I. Methods and Infrastructure Classification uses supervised learning algorithms and sets up the classification problem as an optimization problem of minimizing the gap between a prediction and the ground truth observation. But why use any learning algorithms here at all? Besides the large volume of light curve data, it tends to be unevenly sampled, incomplete, and may be affected by biases (presumably from the equipment?). Hence, standard time series analysis may prove to be insufficient. Instead, this is where learning algorithms tend to do quite well. A whole body of prior work has demonstrated that learning algorithms tend to do well on these class of problems. Once a pattern is classified, ZTF has the potential to run several different pipelines to further validate the specific bin that the event has been classified into. For example, DeepStreaks is a component in the pipeline in ZTF that is used to identify streaking near-earth objects (NEO) (such as comets). Here is a high-level decision tree and sample results for how DeepStreaks decides if the candidate pattern is a plausible NEO, non-NEO events, or noise. Source: Matthew Graham, ZTF & Caltech Finally, all of these add up into Tails, the world’s first deep-learning based framework to assist in the discovery of comets. Tails is built on top of the base data gathering platform. Source: Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learning, Dmitry A. Duev, NeurIPS 2020 The Tails architecture which employs an EfficientDet D0-based network Tails has been online since August 2020 and produces between 10-20 NEO candidates each night. Let us examine the achievement of this particular project in a historical context. Since the first homo sapiens, the ancients have always looked up at the night sky and wondered about our place in this universe. This very act has been the source of all inspiration – religion, art, science, literature, and pretty much everything mankind has done. More specifically, cave art from 40,000 years ago reveal the ancients tracked astronomical phenomenon such as comet strikes and planetary shifts. And what we see today with ZTF, is an example of how, this very old profession of humankind has today largely been automated with advances in contemporary computing. Fritz software platform All of these advances have culminated in the ZTF team open sourcing their underlying extensible data platform – Fritz. In many regards Fritz and the entire ZTF effort echoes the architecture, thinking, and design for how we at Soroco are building the Scout platform. The point here is just through the lens of ZTF we can see an example of the incredible range of expertise that the ZTF team of astronomers and engineers have had to develop to do their scientific work — signal processing, computer vision, deep learning, machine learning, clustering algorithms, infrastructure, storage, databases, API design, parallel processing, networking, and operating systems. And architecture, system design, and system integration on top of all that. Whew! This literally is an entire undergraduate computer science curriculum worth of skillsets rolled into one team! Think of this. When was the last time you knew of a software product or project built by a small team that spanned so many different areas of computing? At Soroco, whenever confronted with technical challenges, we remind ourselves of what these ninja teams in astronomy do and that humbles us and spurs us further. If you enjoy reading this article and want to work on similar problems, apply here and come work with us! Apply to Soroco today! Reflections Some computer science purists may argue a lot of this is about application of technology vs building ‘new’ technology. But we view these distinctions as irrelevant barriers. Instead, what astronomers have shown us, time and time again, is a focus on achieving the end outcome using computation and solving any and every problem that comes their way. It is precisely this confluence of different skills, technologies spanning the stack, and collaboration across physics to computer science that births new systems advancing the capabilities of any software system. In several cases, these teams may have perhaps applied existing algorithms and technologies but they have had to figure out how to integrate disparate components together, which components to pick, scale, performance, latency, accuracy, etc. And in some instances, they have had to solve hard computing problems on their own without necessarily waiting for computer scientists to solve these problems and then publishing them. Therefore, astronomers have had no choice but to mature into excellent computer scientists and engineers themselves. They have had to design, engineer, and solve their way to actually… Continue reading Why are we, at Soroco, inspired by astronomers – the OG computer hackers? [Part II]

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Abstract Syntax Tree for Patching Code and Assessing Code Quality

Soroco Admin — Wed, 12 Jun 2024 12:17:52 +0000

Abstract Syntax Tree for Patching Code and Assessing Code Quality Abdul Qadir 4 june 2021 15 minute read Why should you care? How do we easily and scalably patch 100,000s of lines of source code? Read about how we used a simple yet powerful data structure – Abstract Syntax Tree (AST) to create a system that from one single central point, maps source code dependencies and in-turn patches all dependencies. Abstract A software system is usually built with assumptions around how dependencies such as the underlying language system, frameworks, libraries etc. are written. Changes in these dependencies may have a ripple effect into the software system itself. For example, recently, the famous Python package pandas released its 1.0.0 version, which has deprecated and changed several functionalities that existed in its previous 0.25.x version. An organization may have many systems using 0.25.x version of pandas. Hence, upgrading it to 1.0.0 will require developers of every system to go through the pandas change documentation and patch their code accordingly. Since we developers love to automate tedious tasks, it is natural for us to think of writing a patch script that will update the source code of all the systems according to the changes in new pandas version. A patch script could be parsing the source code and doing some kind of find+replace. But such a patch script will likely be unreliable and not comprehensive. For example, say the patch script needs to change the name of a function get to create wherever it is called in the code base. A simple find+replace will end up replacing the word “get” even if it was not a function call. Another example would be that find+replace will not be able to handle cases where code statements spill over to multiple lines. We need the patch script to parse the source code, while understanding the language constructs. In this article, we propose the use of Abstract Syntax Trees (AST) to write such patch scripts. And then later, we present how ASTs can be used to assess code quality. Abstract Syntax Tree (AST) Abstract Syntax Tree (or AST) is a tree representation of source code Wikipedia page. Almost every language has a way to generate AST from its code. We use Python to build several critical parts of our systems. Hence, this article uses Python to give examples and highlights, but the learnings from here can be applied to any other language. Python has a package called ast to generate ASTs. Here is a small tutorial on it.Code: Output: So, the head of the AST is a Module object, which makes sense. Let’s dig deeper in it. The ast package provides an ast.dump(node) function that returns a formatted view of the entire tree rooted at node. Let’s call it on head object and see what we get.Code: Output (prettified): Looking at the ast.dump output, we can see that the head object which is of type Module has an attribute body whose value is a list of 2 nodes – one representing var = 1 and the other representing print(var). The first node representing var = 1 has a target attribute representing the LHS var and a value attribute representing the RHS 1. Let’s see if we can print the RHS. Code: Output: So, it works as expected. Now let’s try to modify the RHS from value 1 to 2. Code: Output (prettified): We can see the value of the corresponding attribute has changed to 2. Now, we will want to convert the AST back to code to get the modified code. To do that, we will use a Python package called astunparse, for ast doesn’t provide this functionality.Code: Output: So, the modified code has statement var = 2 instead of var = 1 as expected. IntelliPatch Now that we understand ASTs and how to generate them, inspect them, modify them and re-create code from them, let’s go back to the problem of writing patch scripts to modify the code of a system to use pandas 1.0.0 instead of pandas 0.25.x. We call these AST based patch scripts as “IntelliPatch”. All the backward incompatibilities in pandas 1.0.0 are listed on this page. Let’s take the first backward incompatibility on the list and write IntelliPatch for that. Avoid using names from MultiIndex.levels In pandas 1.0.0, the name of a MultiIndex level can not be updated using = operator, instead it requires the use of Index.set_names(). Code using pandas 0.25.x: Output: The above code will raise a RunTimeError with pandas 1.0.0. For it to use pandas 1.0.0, it should be modified to the code below.Equivalent code using pandas 1.0.0: The IntelliPatch needs to do the following: Create AST of the given code and traverse it. Identify if any node represents the code of form <var>.levels[<idx>].name = <val> . Replace the identified node with the one that represents the code of form <var> = <var>.set_names(<val>, level=<idx>). Below is the IntelliPatch script that does that. intelli_patch.py Usage Example 1: Output: Usage Example 2: Output: In usage example 2, note that the code statement that is to be replaced expands to more than 1 line and is present within a function g that is present within a function f that is present within a class C. IntelliPatch handles this case as well. One can extend the patch script to take care of all backward incompatibilities in pandas 1.0.0. And then write an outer function that goes through every Python file of a system, reads its code, patches it and writes it back to disk. It is important to note that a developer should review the changes done by the IntelliPatch before committing it. For example, if code is hosted on git, then a git diff should be performed and reviewed by the developer. Impact At Soroco, we have written 5 IntelliPatch scripts so far that were ran on 10 systems. Each script successfully parsed and patched about 150,000 lines of code across 10 systems. In terms of productivity, this effort took one of our engineers three full… Continue reading Abstract Syntax Tree for Patching Code and Assessing Code Quality

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IDC Widget for Home

Soroco Admin — Wed, 12 Jun 2024 08:42:19 +0000

Analyst Brief Using AI to create a Foundation for Continous Process Improvement Download your copy Download your copy

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Building Large Scale Systems and Products with Python

Soroco Admin — Tue, 11 Jun 2024 12:42:15 +0000

Building Large Scale Systems and Products with Python George Nychis 15 April 2021 20 minute read Overview At the beginning of Soroco’s journey, we had to answer a question that many engineering organizations have had to answer before. What programming language were we going to use when building and scaling our products? The reason that each organization needs to answer the question on their own is that every product’s goals, needs, and constraints are different. However, even with our own goals in mind (which we will explain), no language we could pick would be perfect. We would want to make a decision knowing each language’s potential and shortcomings. We would plan to overcome the key shortcomings to make our technology. Here are the kinds of typical scenarios that we have encountered and the challenges we face when automating or discovering transactions in a live enterprise environment: The automated or discovered work needs to closely match what teams were already doing on the ground. That is, use the same applications, the same data, and most-often follow the same steps. Therefore, a transaction in this context is determined by the steps taken by teams which manually execute the work. And this means that the right comparator set for scale and performance is the manual work that teams execute today to get the work done. Consequently, this almost always means dealing with highly legacy (including mainframes!) and varying enterprise applications, up to 80% of which typically do not have any API interface. Each transaction typically involves accessing approximately 7500 data fields in 71 screens, executing 216 steps, and context switching between 15-890 times between enterprise applications, and takes anywhere between 5-20 minutes to execute a single transaction. Data being pulled from multiple heterogenous enterprise applications – on average each instance involves gathering data from 5-20+ applications of which 40% tend to be legacy. Reading a diverse set of complex documents (e.g. invoices, legal documents, etc.) that requires complex NLP processing to extract structure from documents as well as compare, in near real-time, the semantic similarity of multiple documents. On average each process involves reading 15 different documents. Each automated transaction needs to have the same fidelity as humans, if not better, in terms of error rates, throughput, and reliability while being more scalable. Extremely high diversity in the set of processes, their steps, and the industries that they are executed in. For example, in this post alone, the data is based on 7 different industries and nearly 20 different functions. Hence, nearly 7 years ago when we sought out to finalize our decision on a programming language, we were designing and developing our automation and process discovery products. Our automation product was to be capable of handling billions of transactions a year for a single business process. Our process discovery product would need to be able to process billions of data points to discover millions of processes. Both would be distributed systems and deployed globally. The challenges in automating or discovering processes is that these are all running a live enterprise and feature the following issues: In 2020, Soroco achieved the scale we planned for when making these decisions. Within the past 12 months, Soroco’s Scout product has discovered over 1.3 million process transactions covering up to about 12 million hours of manual work. In 2020, Soroco’s automation systems have processed over 1.2 billion enterprise transactions across multiple clients to bring our customers savings and scale to the extent of over 2M hours. Note, however, most of these automated systems ran in sync with people’s working timings and on working days. This is because typically the automation execution is triggered by an incoming email, document, or an event that populates data in an enterprise system. Furthermore, our ability to ‘scale’ more transactions per second is significantly rate-limited by the delays and slowness of legacy enterprise apps that are not built for an automated layer of software running on top. Therefore, our point is not about merely optimizing for number of transactions per second. There are many systems where Python has been optimized for this metric alone. We cannot control for this metric in an enterprise automation setting built on top of legacy systems. Rather, our point is about ensuring high-fidelity and scalable execution of automation systems in the enterprise while also meeting enterprise standards of safety and reliability. Therefore, we needed to be able to architect and design our technology carefully. Though we think of picking a programming language to meet this kind of scale as a technical decision, it is important to keep in mind that scaling technology also means being able to scale the engineering team who builds it. The easier the product is to develop, and its code is to read, deploy, secure and maintain…then the better the technology’s development could scale. In this blog post, we will describe why Soroco chose Python and what we did to ensure we could develop reliably, at scale, and securely. Many of these properties were not ‘out of the box’ with Python 7 years ago. This was at a time when it was far from the most popular language, still considered ‘slow’ and a ‘scripting language.’ Python was far from being considered a language for building large scale systems. All of that has changed today, and in this blog post we will provide guidance in all of the following dimensions which helped us build products with Python. Predicted Growth of Python: Why we picked Python to make it easier to scale our engineering team, despite many of its limitations. The global education system provided hints that Python would be one of the most widely used and known languages in a few years from when we started. PEP484 and Enforcing Typing: How we overcame the downsides of being non-statically typed (e.g., more potential errors in runtime) by supporting the growth of Python’s PEP484 for ‘gradual typing’ while it was still in development. Developing an early PyCharm plugin that enforced it (before mypy was… Continue reading Building Large Scale Systems and Products with Python

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White Paper Widget for Home

Soroco Admin — Tue, 11 Jun 2024 10:08:54 +0000

White Paper How AI is helping the public sector up the ante on service transformations Download your copy Download your copy

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Modernizing Object Storage for Cloud Native Deployments

Soroco Admin — Mon, 10 Jun 2024 11:20:34 +0000

Modernizing Object Storage for Cloud Native Deployments George Nychis, Vageesh Hoskere & Wolfgang Richter 3 August 2021 15 minute read Why should you care? Data storage is a universal need. Structured data goes into familiar stores like an RDBMS (PostgreSQL, MySQL, Oracle), but unstructured data can be housed in many ways. For example, object storage systems, key-value caches, document stores (if there’s some structure), and even flat files on a file system. This article details how and why your choice of unstructured storage: affects your scalability by making or breaking your cloud native capability, balloons your software maintenance cost, and limits the possible savings you could get on your cloud storage expenses. We take you on our journey from a home grown, flat-file-based object storage layer, to an off-the-shelf approach with MinIO which saved us 30% in storage costs and 90% in maintenance costs. Object Storage at Scale Soroco’s product Scout collects millions of data points every day from interactions between teams and business applications, during the natural course of a workday. From the collected interactions, Scout detects patterns in the data using various machine learning algorithms to help our customers find opportunities for operational improvement. Below, we show the flow of this information via an example using Scout events. Scout events are represented as JSON objects that are buffered in memory and then periodically stored in compressed, encrypted JSON files on disk. Compression minimizes the network bandwidth and storage requirements. Encryption protects sensitive data at rest and in flight. Buffering saves compute resources by batch processing events. These services can be in the cloud, or on premises if the customer prefers it. Scout’s data ingestion services then decrypt and decompress the JSON data after which the individual records are post-processed and stored in an RDBMS. The records can then be fetched by our various machine learning algorithms. A sketch of information flow in Scout We must store the original data though, because post-processing might transform or accidentally drop data that we find useful in the future. For example, an updated machine learning algorithm might want a re-interpretation of the features from the original samples. If we threw them away after post-processing, we could never go back to the original data to improve results. Of course, we also have to store screenshots somewhere, and our RDBMS did not seem like a good choice. A contributor to PostgreSQL benchmarked the performance of object storage in PostgreSQL as compared to disk and found a 10x slowdown in a read-based benchmark. You don’t want to store objects in PostgreSQL! A typical large scale deployment spanning 100s of teams and 1000s of users ingests approximately 2B objects equating to approximately over 130 TB per year (assuming 261 working days). The post-processed structured information stored in our RDBMS is orders of magnitude smaller because it is just the output of a feature engineering pipeline for machine learning algorithms. In addition to the storage needs, the total set of requirements we had come up with when looking for an object storage solution were: Handle our storage requirements of objects at scale Decouple storage from our local file system for reducing cost and maintenance Provide compression to reduce storage requirements Minimal maintenance requirements from our engineering team Support for detailed access control lists to protect the original data files Simple integration with cloud native storage services such as Amazon S3 and Azure Blob Storage Local storage if cloud native storage services are not available (e.g., for on-premises) A solution meeting all these requirements ought to be both – cloud-native and scalable. This would let our product handle substantial retention periods (1 year or more), on-demand random access read workloads, and all of the deployment scenarios we care about (bare metal, private cloud, public cloud). In the remainder of this blog article, we present the different approaches and trade-offs which lead to our final solution which saved us 30% in storage costs and 90% in maintenance costs. Considering our Options for Object Storage There are a few common options for object storage that we considered while evaluating different designs to meet our requirements. Filesystem-based Object Storage with References A low-complexity solution to object storage is to store objects on the disk and keep references to the available objects with any important metadata in a database or index. Git is well known for doing this and implementing a style of it called content-addressable storage (CAS). An example of this is illustrated below. As illustrated with a CAS system, objects are stored on the filesystem by their hash and any meta-data associated with them can be stored in a database or catalog. Benefits of file-based object storage are simplicity in design, and if CAS is used you will get de-duplication of objects for more efficient storage since multiple references can map to the same object on disk. No specialized systems are required to track the objects, and access to them will be as easy as filesystem reads. Downsides of the filesystem-based object storage are maintenance, lack of access control without building or using a more substantial system around it, and inaccessibility to a shared filesystem in modern cloud native deployments where services do not assume local storage. Though you could mount a network share, the performance impact of using an NFS share would likely be substantial. For these reasons, we believe that while this approach is fast and has simplicity, it does not meet a lot of our requirements. Distributed Object Storage To keep the benefits of filesystem-based object storage and overcome the limitations around access to the storage, distributed object storage systems such as Ceph and Swift were built. Their design is illustrated below, where a “storage cluster” is built by distributing objects across any number of block devices (e.g., bare metal disks). This storage is then made accessible through microservices with network accessible APIs to store and retrieve blocks, and fine-grained access control. An example distributed object storage deployment with Ceph (Credit: https://insujang.github.io/2020-08-30/introduction-to-ceph/) Benefits of distributed object… Continue reading Modernizing Object Storage for Cloud Native Deployments

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Increasing the Accuracy of Textual Data Analysis on a Corpus of 2,000,000,000 Words

Soroco Admin — Mon, 10 Jun 2024 09:53:47 +0000

Increasing the Accuracy of Textual Data Analysis on a Corpus of 2,000,000,000 Words Michael Lee Response By Engineering, SRE, and Customer Success 4 December 2021 14 minute read Introduction Natural language processing (NLP) is one of the most active subfields of machine learning today. Text classification, topic modeling, and sentiment analysis have become vital techniques in a myriad of real-world technological applications, such as search engine optimization and content recommendation. E-mails, social media posts, news articles, and other documents are constantly mined for insights on human opinions and behaviors by scientists, large and small companies, and even state actors. At Soroco, natural language processing and machine learning-based classification of text are foundational to many of our products. In some instances, we may ingest between 200,000,000 and 2,000,000,000 words over the course of model training and analysis for a single team of workers using our Scout product. In this blog post, we will address some tips and tricks which we have found to significantly increase the accuracy of our models, including appropriate processing of text for the purpose of leveraging standard techniques from machine learning. Many advanced methods for performing text classification require careful modifications so as to respect the structure of multi-field textual data for optimal performance. To illustrate the benefits of these techniques, this two-part series of blog articles will demonstrate the following ideas: We will show how to represent text in a high-dimensional vector space with applications to a toy regression problem. We will detail how to perform multi-field textual data analysis using more sophisticated neural network technologies. Challenges of Text Analysis Text analysis is complicated by the variety of nuance inherent in each application: real-world problems in NLP require analyzing complicated bodies of text, such as ones which are split into multiple fields or contain many words of natural language. Text fields in an email may include the subject, the sender and recipient, the email body, and the contents of any possible attachments. To illustrate the challenges and important aspects of modern text classification, we have included a sample e-mail where Soroco’s marketing team announced NelsonHall naming Soroco as a leader in Task Mining. This e-mail, like any other, has multiple text fields. Based on the text of the e-mail body, its subject line, and the identities of its senders and recipients, we may wish to perform some kind of classification task. For example, we may want to classify the e-mail as a “Positive Announcement,” or detect that it’s related to marketing. However, how do we properly set up a model to classify this email correctly and efficiently? Here are some potential challenges we might run into as we train a model to identify “Positive Announcement” emails: We might be tempted to take the entire text and collapse it into a single block of text for the classifier. However, that would cause all fields to be weighted with equal importance, which is not the case. For example, for this task in particular, the presence of positive words such as “Congratulations” in the email’s subject might be more pertinent than the content of the email body. What if all e-mails passed to the classifier in training always had the opening “Congratulations to the Soroco Team”, but a future email began as “Kudos to the Soroco Team”? The input to the model during training and classification (e.g., how words are vectorized) can have a significant impact on the accuracy of the classification. Once we’ve vectorized the text fields appropriately, there are many different classifier types we might use to solve the classification problem. However, not all of them will necessarily perform well with our chosen vectorization method. Some classifiers may work better with fasttext or word2vec embeddings (which tend to produce dense, high-dimensional output), whereas others might work with tf–idf mappings (which are sparse but can be of much higher dimension depending on your corpus). Some experimentation may be required to find the model architecture that performs best for our input. In Part 1 of this blog article, we are going to first address the importance of the vectorization of words and show how this vectorization has implications on the model’s accuracy and performance. In the second article, we will show further illustrate the importance of training on multiple fields and the impact to the model. Building a Model with Word Embeddings The approaches we cover here are going to rely on the concept of a latent embedding of textual data into a vector space, such as through word embeddings. Although there are simpler statistical methods for doing basic NLP, such as tf–idf, for our purposes we prefer a method where we assign some notion of semantic significance to our data. This semantic significance is the key to making the model resilient to the many ways in which the same contextual meaning can be written (i.e., like “congratulations” and “kudos” in our example above). Word embeddings provide a vector space representation of each word in a vocabulary such that words which appear in similar contexts (such as synonyms) have representations which are close together in space. This helps us to be robust against the variation in word choice we might see in real-world applications, where we are trying to analyze textual data produced by people. From fasttext.cc The above diagram from Facebook Research shows the difference between two strategies for optimizing word embeddings. In the CBOW (“continuous bag of words”) approach, we train a network to predict the embedding of a word from the sum of embeddings of all words within a fixed-size window around that word. In the sample sentence, “I am selling these fine leather jackets,” the embedding of the word “fine” is predicted from the sum of the embeddings of the words “selling,” “these,” “leather,” and “jackets.” In contrast, in the skipgram approach, we train the network to predict the embedding of a word from a random word selected from that fixed-size window, so the embedding of the word “fine” is predicted from the embedding… Continue reading Increasing the Accuracy of Textual Data Analysis on a Corpus of 2,000,000,000 Words

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Rapid Response to the Log4j CVE & Important System Design to Limit Potential Impact

Soroco Admin — Mon, 10 Jun 2024 07:28:52 +0000

Rapid Response to the Log4j CVE & Important System Design to Limit Potential Impact Article By George NychisResponse By Engineering, SRE, and Customer Success Introduction Though many of our readers will already be aware of this topic and the severity of it, we present Soroco’s response with its engineering team on the Log4j vulnerability. In this post we will describe how we learned of the vulnerability and provided rapid response to it within 24 hours and patched it across our production environments. Additionally, how our system architecture ensured that any impact of the vulnerability would be isolated due to container technology. After significant analysis of our product’s usage, we were not able to find any known impact of the Log4j vulnerability. Background On December 9th, 2021 the Log4j vulnerability was announced and recorded as CVE-2021-4422 with the highest score of 10. It allowed remote code execution which meant that an attacker could potentially execute a series of commands on a server where information was processed with the Log4j library. Those commands could, for example, read information on the server, modify information, delete data, or even attempt to extract sensitive information from it. If the reader wants a technical explanation of the vulnerability, we recommend the following post. What made this announced vulnerability so severe and the reaction to it so substantial was that it was “zero-day” and used so widely across the industry. Zero-day referred to attackers being aware of it before or at the same time as security researchers without a known patch. Its wide use across the industry included all major cloud services such as Apple, Google, Microsoft, Cloudflare, and Twitter. Additionally, around 32% of the Fortune 500 is reported to use ElasticSearch which leverages Log4j which likely contributed to the large response. How Soroco’s System Architecture Minimized Any Potential Impact Soroco’s use of Log4j was not direct, but rather indirect through our use of ElasticSearch and the ELK stack, like many others in the industry as descried above. This meant that our response was to patch a 3rd party technology’s use of the Log4j library rather than direct use of Log4j any of our products. What was most beneficial to minimize the potential impact of the vulnerability, was Soroco’s use of isolating its software systems and services with software containers. The way that Soroco deploys the ELK stack is through the modern use of software containers. Containers run individual software systems in isolation by packaging the software system and all other dependencies needed to run the software. Aside from the simplicity containers provide in running software, their isolation properties also provide security benefits since what is running inside the container is not (by default) given access to other software systems running in other containers. Even if remote code were executed using the Log4j vulnerability (which we have found no evidence of at Soroco), it could not access anything other than what was in the ELK container. At Soroco, this deployment model meant that the vulnerable ELK stack would not have access to other parts of our infrastructure such as a production database. Responding in 24 Hours and Patching the Product within 48 Hours Immediately with our knowledge of the vulnerability and understanding our use of it in our products, we began proactively notifying our customers of our acknowledgement of the published vulnerability and our expected response to it. This was received positively by our customers, as they were trying to reach out to other vendors while having received a proactive acknowledgement from us. After notification we began patching. By deploying the ELK stack through Elastic’s officially published containers for them, patching the vulnerability also became simple and would not require any substantial changes to Soroco’s product technology. We only needed to ensure our products could still run with small periods of ELK downtime while we patched them. Two changes were made to patch out the vulnerable 3rd party functionality in our production environments, as confirmed directly by Elastic to remove the vulnerable functionality. The first change that we made was to the start-up configuration for the ElasticSearch container image to include the – Dlog4j2.formatMsgNoLookups=true option which disabled the vulnerable functionality in ElasticSearch. In lieu of waiting for official fixes to LogStash so that we could react quickly, we modified Elastic’s published container for LogStash to remove the vulnerable JNDI functionality. Once these two changes were made, we immediately began updating these two containers across our production environments to remove the vulnerable functionality. Though again, our use of container technology helped isolate the vulnerable technology from major portions of our technology stack and production databases. If you enjoy reading this article and want to work on similar problems, apply here and come work with us! Apply to Soroco today! Learning from Soroco’s Response There are several things that we stressed to our teams globally from the announcement of the Log4j vulnerability and our response to it. First, we worked with our customer success team to ensure that we proactively acknowledged the Log4j CVE to all of our customers before they wrote to us looking for a response. This showed Soroco’s dedication to product security with our customers and assured them that we are continually monitoring systems for announced vulnerabilities. Second, Soroco’s engineering and site reliability engineering teams worked immediately on finding the known workarounds and patching the product within 48 hours. This showed our ability to quickly patch our product and in particular how the use of modern container technology made it simple. Lastly, Soroco’s use of container technology also limited the potential exposure from vulnerabilities by isolating the information and systems that they have access to. All of these together ensured our product and its use was safe for our customers. Content Explorer See Scout in action. Schedule your demo now! Get in Touch

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Waldo: A Private Time-Series Database from Function Secret Sharing

Soroco Admin — Fri, 07 Jun 2024 12:24:28 +0000

Waldo: A Private Time-Series Database from Function Secret Sharing Talk by Emma Dauterman, Ph.D. Student in Computer Science, UC Berkeley Article by Devesh Krishnani About Our Guest Speaker Soroco invited Emma Dauterman, a current 4th year Ph.D. student studying computer science in UC Berkeley’s RISELab where she is advised by Raluca Ada Popa and Ion Stoica. Emma’s work throughout her Ph.D. has focused broadly in building secure systems using cryptography, published in IEEE and ACM conferences such as S&P (Oakland), SOSP, OSDI, and others. We invited Emma to give a talk at Soroco as her work relates very closely to technologies we build that must be both secure and scalable. Why this talk at Soroco The work that Emma presented at Soroco, called Waldo, is a private time-series database focused on how to protect the privacy of user data stored in it by cryptographically securing their data, access patterns of their data, as well the query filter values. Particularly, the work also focused on how to support multi-predicate filtering which is common in database queries. All of this was published in S&P (Oakland) in “Waldo: A Private Time-Series Database from Function Secret Sharing.” At Soroco, we build the work graph that helps organizations understand how digital work gets done. The work graph is a connected sequence of steps that teams execute. It is, in essence, a map of how teams execute digital work, and it lies at the intersection of people, work, and technology. Once discovered, the work graph enables teams to collaborate and work more effectively. Since the work graph is a sequence of steps that teams execute and sourced from the activities that the teams perform, a major role of the work graph’s design and information access is to ensure end-user privacy is protected. How we protect end-user privacy is and will always be a focus of our system design. For these reasons, we invited Emma to give the talk so that we could learn more about furthering cryptographic storage and privacy. Watch the Talk Powerful ideas from Waldo As presented in Waldo’s system design, it is focused on a time-series database design that supports write-intensive workloads that have a high ratio of updates (‘appends’ being what is supported), where multiple features and multiple predicates are supported while keeping the data cryptographically protected. There are two types of clients in the system, data producers and queriers (or clients that are both). Data producers collect real-time data and update server state with it. Data queriers query the data collected stored on the server. Distributed Trust: The author’s work around Waldo first leverages distributed trust through multiple server deployments. For example, by storing the data in three servers present in three different trust domains. Therefore, if the data is compromised in one trust domain the other two trust domains could still be used to access the correct data. Practically, that means that these servers should be deployed in different clouds and managed by different organizations. By distributing information across these servers, if a majority of the servers are honest then a single malicious server cannot learn the data contents, query filter values, or any search access patterns. Clients need to send messages directly to each of the servers, distributing the information and trust. At Soroco, We think that this follows a powerful design pattern to protect against single or malicious compromised servers. However, it does increase system design complexity and cost. Therefore, when to leverage the distributed trust framework depends on what data is important. Encrypting query at client and decrypting at server using Function Secret Sharing: Waldo suggests the use of a cryptographic technique called Function Secret Sharing (FSS) to generate FSS keys for the query on the client side and evaluate it at the server. Combining this technique with replicated secret sharing ensures that the malicious attacker is not able to determine the access pattern or the filter values used to access the result. In the experiment setting, there is a medical practitioner who queries the data across two data servers using FSS keys generated at client side and the server returns the shares of data. These shares of data are again aggregated at client side to produce the final output. Although Waldo’s complete protocol uses 3 servers, we are showing a simplified example with two below. Emma covers the complete protocol in both the paper and the recording of her talk above! Using MAC key along with FSS key: The Waldo system design further expands on the above idea by introducing the concept of using MAC keys to verify at client side whether the result from server is correct. Here the client is only checking whether the data is compromised on the server or not. In the experimental setting, the medical practitioner sends pair of keys to the server. Waldo uses MAC techniques originating in multi-party computation to provide these security guarantees. Using Function Secret Sharing To ensure that a malicious party is not able to access the access pattern of the data: let’s deep dive into the problem in Soroco’s context. Please note that this is a simplified example of the Waldo system, and we encourage reading the original paper and watching the video for further real-world applications! Suppose there is an individual in a data analyst role that wants to identify how many applications were accessed between two periods of time X and Y. The query in the work graph would be: Query = “select count(distinct application) from workgraph where time between X and Y. “ In this simplified example and strawman-based approach, we will generate secure keys for this query, e.g., K1 and K2 using a Generate method. K1, K2 <- Gen(Query). Sticking with this simplified model, we can then take these keys K1, K2 and initiate a request to the data servers using one key each as shown below. At the server level, we can then use a method Eval and iterate across the entire dataset. Eval method will either produce zero or one… Continue reading Waldo: A Private Time-Series Database from Function Secret Sharing

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Tech Talk: Building Systems that Effectively and Cryptographically Protect User Privacy

Soroco Admin — Fri, 07 Jun 2024 12:05:46 +0000

Building Systems that Effectively and Cryptographically Protect User Privacy George Nychis 22 July 2022 15 minute read About Our Guest Speaker Soroco invited Sam Kumar, a current 5th year Ph.D. student from U.C. Berkeley and prior U.C. Berkeley B.S. CS graduate, to give a talk on his and his colleague’s novel computer security and privacy research. The work that Sam presented at Soroco, which you can watch below, was published in the top Computer Science system design conferences NSDI 2020 and OSDI 2021. The latter of which was awarded best paper of that year’s OSDI conference. These two pieces of work that Sam gave his talk on were centered around how to build systems that effectively and cryptographically protect user privacy. Why this talk at Soroco Building systems that effectively and cryptographically protect user privacy is highly relevant to the work we do at Soroco. At Soroco, we are building the work graph to understand how digital work happens at the last mile. A work graph is a connected sequence of steps that teams execute to get work done. It is, in essence, a map of how teams execute digital work, and it lies at the intersection of people, work, and technology. Once discovered, the work graph enables teams to collaborate and work more effectively. Since the work graph is a sequence of steps that teams execute and sourced from the activities that the teams perform, a major role of the work graph’s design and information access is to ensure end-user privacy is protected. How we protect end-user privacy is and will always be a focus of our team. Watch the Talk Powerful ideas from Ghostor and MAGE As shown above, protecting user privacy goes far beyond simply encrypting their information (i.e., the 4th layer down – hiding the data in each object). It is important to protect what users are part of the system (i.e., who has an account, who is participating), hiding timing of object accesses, and even hiding which user makes each access. For example, although the contents or even who an object in the system belongs to can be hidden, timings of access to that file or correlating those times with other information can eventually help an adversary conclude who it belongs to even if not knowing what is in it. It is first this end-to-end thinking about protecting user privacy that we found extremely valuable in system design. Layers of Anonymity: The author’s work around Ghostor first illustrates multiple layers of protection required to provide stronger guarantees of user privacy. While many readers of this blog post may think one of the strongest aspects to providing user privacy is simply encrypting that user’s information, there are far more layers of security required to ensure user information is kept private. Verifiable Linearizability: Second, the author’s present the importance of not just protecting the contents of information and that it is valid (e.g., via a signature), but also being able to provide guarantees that it is the most up-to-date version of the information. This is because encrypting information and signing it does not necessarily guarantee that an adversary could not simply present back an older representation of that information which could no longer be valid or missing a sensitive update to it. The author’s work in Ghostor provides a simple example of a patient system in the medical field, where an adversary could return a valid older copy of data before an individual had more recent updates to their medical records that are important to protecting their health (e.g., notes of recent allergies). Ghostor is the author’s proposed system design that can provide the multiple layers of anonymity with variable linearizability. This incorporates a blockchain that could provide decentralized trust. That would hide user identity from even the servers that process the information, which we find is a powerful concept. The decentralized verifiability of the blockchain can also provide the verifiable linearizability of the data. Both the user and the server can validate that the information being processed is the most recent. Since there are concerns of overhead in updating the blockchain, it is proposed that a single hash that represents the entire system is updated every epoch. That provides verifiability of the entire system and its information. Additionally, there are several properties of the system design that are important to protecting the user anonymity along with the verifiability and anonymous properties of the blockchain. For example, ensuring there are no server-visible ACLs and no server-visible user public keys as being important. There are several others that the authors present that we encourage the reader to get more details from their paper. MAGE further builds on the principals of security and privacy by focusing on proposed improvements to Secure Computation. Secure Computation being privacy preserving technology that protects the identity of parties involved and the information being input to the cryptographic function. The only information being disclosed being the output of the function. This can allow different parties to participate in an agreed upon computation, without ever knowing the input to that function. An example being, two websites being able to use Secure Computation to collaborate and know whether a user has used the same password on their sites (which can weaken security) without knowing the actual password or the user in question. Where MAGE’s work focuses is on the system challenges in providing Secure Computation when there is substantial overhead from the underlying cryptography. The key focuses in MAGE being providing a paging system that allows MAGE to provide highly efficient virtual memory abstractions for Secure Computation. This enables Secure Computations that potentially do not fit in memory at nearly the same speed. Doing so required the authors to carefully plan the memory access to reduce paging, in what they called memory programming. That concept being quite unique and what we believe is powerful to furthering the field of Secure Computation. Making it more scalable for functions with large memory requirements. Additional Thoughts and Conclusions… Continue reading Tech Talk: Building Systems that Effectively and Cryptographically Protect User Privacy

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NBDTs and a Realistic View of Interpretability for Deep Learning

Soroco Admin — Fri, 07 Jun 2024 07:08:33 +0000

NBDTs and a Realistic View of Interpretability for Deep Learning Talk by Lisa Dunlap, Ph.D. Student, UC Berkeley Article by Tanmay Jaiswal About our Guest Speaker Soroco invited Lisa Dunlap, a 2nd year PhD Student from UC Berkeley to deliver a talk on her work in explainable AI at Berkeley Artificial Intelligence Research (BAIR) Lab. The work that Lisa discussed during her talk, which you can see below, focused on a problem as old as Neural Networks – Lack of Explainability. Lisa and her colleagues came up with a new and interesting way to combine Neural Networks with Decision Trees while keeping both their strengths and compensating for their weaknesses. Their paper on Neural Backed Decision Trees (NBDTs) was published in ICLR, 2019. Why This Talk at Soroco Soroco is building a work graph to help enterprises understand how they do digital work at the user level. Soroco’s technology Scout performs process and task discovery to find patterns in the data which represent business steps conducted by users, which helps annotate the work graph with the business context of how teams conduct processes. Soroco’s Machine Learning algorithms classify user activities into processes and tasks but when our models suggest that a set of user activities should be attributed to a particular process, it helps to understand why the models think so. Explainable insights can help us provide more accurate predictions while also empowering our customers to highlight information that can help us identify their processes better. That’s where we think the work presented by Lisa is pertinent to what we do. A deeper knowledge of the day-to-day tasks and processes enables teams to identify their pain points, bottlenecks, and discover the variations in the way processes are performed. Teams can then standardize their processes, address their system or process bottlenecks, and even automate repetitive tasks to improve their efficiency. Watch the Talk https://www.youtube.com/watch?time_continue=5&v=8LzlU1M8ToA&embeds_referring_euri=https%3A%2F%2Fsorsandbox.tempurl.host%2F&embeds_referring_origin=https%3A%2F%2Fsorsandbox.tempurl.host&source_ve_path=Mjg2NjQsMjg2NjY&feature=emb_logo Motivation Lisa highlighted a key challenge in developing and using machine learning models – understanding how and why a model makes a prediction, i.e. – explainability and interpretability. This is a problem for both developers and their users. For users, more opaque models are harder to understand and hence harder to trust. For developers, explainable models are easier to debug. Black box the models, regardless of their accuracy, are harder to work with. If a model is not explainable, it is difficult to determine if the error comes from a poor choice of model, lack of hyperparameter tuning, or poor data quality. If the data quality is poor, the developer shouldn’t have to go through every example, relabel, and retrain repeatedly. Interpretable models provide a human-in-the-loop way to dive into the data. Interpretability is for these reasons, one of the most desirable properties of models in addition to accuracy. Our most powerful and accurate models – Neural Networks – are also the least interpretable. Gradient based interpretability methods and saliency maps explain the part of the input that was most influential in making a decision, but they don’t tell us how the model made the decision. Our most interpretable models like decision trees are not nearly as powerful as Neural Networks but they provide a way to explain how the model arrived at a decision. Key Ideas from the Talk NBDTs enhance the traditional neural network architecture to make them more interpretable while retaining or even improving their accuracy. They consist of a decision tree that is created to work in tandem with a neural network to augment the explainability of the combined model. Below we describe more of their beneficial properties and what we think makes them powerful.Plug and play over a traditional neural network The most beneficial part of this approach is that it keeps the neural network almost as is. Features from the neural network itself are used to build the tree. The nodes of the fully connected layer of the neural network contain weights which describe the features that the node is looking for. Running agglomerative clustering over these nodes tells us which nodes can be grouped together. The resulting dendrogram give us the structure of the decision tree.Providing intermediate results that lead to the decision The intermediate nodes of the decision tree are still not explainable or interpretable. WordNet is a hierarchy of nouns. To assign labels to nodes, the earliest common ancestor for all leaves in a subtree is found from WordNet. The paper describes an intuitive example to help us understand this – say Dog and Cat are two categories that the original model predicts and they correspond to a node in the tree. Clustering tells us that they share a parent. To find a WordNet label for the parent, all ancestor concepts for Dog and Cat are found, like Mammal, Animal, and Living Thing. The closest shared ancestor is Mammal, so we assign Mammal to the parent of Dog and Cat. We do this recursively until all the nodes have a label. Optimizing for interpretability also optimizes for accuracyThe last and most interesting contribution of the paper is how they improve both the accuracy and the interpretability of the Neural networks by adding a Tree Supervision Loss. The tree supervision loss is a cross entropy loss that encourages the network to predict the right path in the dendrogram with a higher probability. This loss ended up improving both explainability and the accuracy of the model. Additional Thoughts and Conclusions We discussed how NBDTs or related concepts may be helpful in solving some of our problems at Soroco. Some of the key challenges with implementing such an approach in the wild is that it could be hard to find labels for intermediate nodes of the decision tree. We discussed how multimodal models like CLIP could help in such cases. We also discussed how other large language models (LLMs) may be useful in purely NLP contexts. Most importantly, the talk debunks the myth that models cannot be both highly accurate and explainable. It has had Soroco developers rethinking how we… Continue reading NBDTs and a Realistic View of Interpretability for Deep Learning

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HFS Research Widget for Home

Soroco Admin — Sat, 01 Jun 2024 05:56:06 +0000

HFS Research Report How Soroco Uses AI to Illuminate the Dark Side of the Moon Download your copy Download your copy

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DII 3

Soroco Admin — Thu, 30 May 2024 06:41:45 +0000

“Simply put! Enterprises that do not adopt to Digital Interaction Intelligence asap, are at an existential risk”

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What is Digital Interaction Intelligence?

Soroco Admin — Wed, 29 May 2024 08:29:00 +0000

What is Digital Interaction Intelligence? Learn how AI and Interaction Data is a generational change from old school process intelligence and task mining. Download Complete Playbook Download 2025 DII PEAK Matrix Defining Digital Interaction Intelligence (DII) Digital Interaction Intelligence (DII) refers to any software designed to: Create Interaction Logs Utilize advanced AI models to capture human-machine interactions and associated metadata. This includes recording keystrokes, mouse clicks, activity screenshots, and application object IDs across desktops to generate comprehensive UI logs. Generate Digital Interaction/ Work Graphs: Produce detailed digital interaction graphs that map out various process variants. These graphs depict the sequence of tasks/steps, users involved, and the applications utilized. Extract Business Insights and Opportunities Derive relevant business insights, identifying opportunities for automation and AI integration.DII software, like Soroco’s Scout AI model, offers a fact-based, technology-driven method to comprehend user interactions across various applications, particularly productivity tools. By leveraging machine learning models, DII connects human-machine interaction data with key performance indicators (KPIs) and business outcomes. Unlike process mining solutions that focus on processes, DII emphasizes a user-centric perspective, leading to a more detailed understanding of current processes. Why You Should Download The DII Playbook The Digital Interaction Intelligence (DII) Playbook empowers enterprises at various stages of their transformation journeys. It provides insights, methodologies, and practical advice to achieve best-in-class outcomes from digital interaction intelligence. Get Introduced to Interaction Data Understand the invaluable benefits of harnessing human-machine interaction data. Embrace Digital InteractionIntelligence (DII) Discover how DII surpasses traditional task/process mining with advanced AI capabilities, across industries and functions. Get Access to Step-by-Step Implementation Guide Follow a detailed guide to launching and accelerating your DII journey. Learn How to Setup a DII CoE Understand the pivotal role of a Centre of Excellence (CoE) in driving DII success. Customer CaseStudies Gain inspiration from real-world case studies of successful DII implementations. The Digital Interaction Intelligence (DII) Playbook empowers enterprises at various stages of their transformation journeys. It provides insights, methodologies, and practical advice to achieve best-in-class outcomes from digital interaction intelligence. Get Introduced to Interaction Data Understand the invaluable benefits of harnessing human-machine interaction data. Get Introduced to Interaction Data Understand the invaluable benefits of harnessing human-machine interaction data. Embrace Digital Interaction Intelligence (DII) Discover how DII surpasses traditional task/ process mining with advanced AI capabilities, across industries and functions. Get Access to Step-by-Step Implementation Guide Follow a detailed guide to launching and accelerating your DII journey. Learn How to Setup a DII CoE Understand the pivotal role of a Centre of Excellence (CoE) in driving DII success. Customer Case Studies Gain inspiration from real-world case studies of successful DII implementations. Embrace Digital Interaction Intelligence (DII) Discover how DII surpasses traditional task/ process mining with advanced AI capabilities, across industries and functions. Get Access to Step-by-Step Implementation Guide Follow a detailed guide to launching and accelerating your DII journey. Learn How to Setup a DII CoE Understand the pivotal role of a Centre of Excellence (CoE) in driving DII success. Customer Case Studies Gain inspiration from real-world case studies of successful DII implementations. Get Additional Access To Infinite Applications Explore the practical applications and benefits of DII across industries and functions. Market Data Gain insights into the market size, growth trends, and adoption rates of DII solutions. Infinite Applications Explore the practical applications and benefits of DII across industries and functions. Market Data Gain insights into the market size, growth trends, and adoption rates of DII solutions. Best Practices Learn best practices for data security, talent management, and change management during DII adoption. The Capability Maturity Model Access resources like the DII Capability Maturity Model (CMM) to evaluate key capability areas in your enterprises’ DII journey. InfiniteApplications Explore the practical applications and benefits of DII across industries and functions. Market Data Gain insights into the market size, growth trends, and adoption rates of DII solutions. Best Practices Learn best practices for data security, talent management, and change management during DII adoption. The CapabilityMaturity Model Access resources like the DII Capability Maturity Model (CMM) to evaluate key capability areas in your enterprises’ DII journey. Infinite Applications Explore the practical applications and benefits of DII across industries and functions. Market Data Gain insights into the market size, growth trends, and adoption rates of DII solutions. Best Practices Learn best practices for data security, talent management, and change management during DII adoption. The Capability Maturity Model Access resources like the DII Capability Maturity Model (CMM) to evaluate key capability areas in your enterprises’ DII journey. Download the playbook now to transform your enterprise with Digital Interaction Intelligence (DII) and stay ahead of the competition! Download the Everest Group Playbook Download the playbook by sections Section 1 What is Digital Interaction Intelligence (DII)? How is it different from Task Mining? Key components of DII Impact of AI in these solutions Importance of DII in measuring work Download Section Section 2 DII Applications, Benefits, and Use Cases Challenges faced by different stakeholders Key applications of DII Benefits of DII solutions Use cases across industries and business functions Download Section Section 3 Discover your Digital Interaction Intelligence Journey Understand the current state Build a business case Identify all determinants What is the capability maturity model? Download Section Section 4 Understanding the Market Adoptionof DII Understand state of the market Adoption of DII by market segments Download Section Section 5 DII Customer Success Stories Understand the DII journey of BT Group Understand the DII journey of The Wonderful Download Section Meet the authors Amardeep Modi Vice President Santhosh Kumar Practice Director Harpreet Makan Practice Director Upshant Saini Senior Analyst Shreepriya Sinha Senior Analyst Amardeep Modi Vice President Everest Group Santhosh Kumar Practice Director Everest Group Harpreet Makan Practice Director Everest Group Upshant Saini Senior Analyst Everest Group Shreepriya Sinha Senior Analyst Everest Group Amardeep Modi Vice President Everest Group Santhosh Kumar Practice Director Everest Group Harpreet Makan Practice Director Everest Group Upshant Saini Senior Analyst Everest Group Shreepriya Sinha Senior Analyst Everest Group Four Years in a Row: Soroco Tops Everest Group’s… Continue reading What is Digital Interaction Intelligence?

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Use AI to Create a Foundation for Continuous Process Improvement

Soroco Admin — Fri, 24 May 2024 06:19:09 +0000

Analyst Brief Use AI to Create a Foundation for Continuous Process Improvement Discover the Future of Work with AI-Powered Process Excellence Unlock the full potential of your business operations with our comprehensive report on how AI is transforming process excellence. Authored by Maureen Fleming, Program Vice President of Worldwide Intelligent Process Automation Market Research and Advisory Service at IDC, this report provides invaluable insights into the latest advancements and practical applications of AI in improving business processes.  Who Should Read This Report?  Process Intelligence and Automation Leaders  CTOs, CIOs, Heads of Transformation, and AI Leaders  Operations Heads and Line-of-Business Leaders  Download This Report to Use AI in Process Improvement Strategies Learn how AI-powered process excellence improves planning accuracy and drives tangible improvements in KPIs and financial performance. Leverage Deeper Insights for Continuous Improvement Discover how integrating AI and Interaction Data with operational metrics enables proactive optimization of costs, productivity, and customer experiences. Lean on Data for Decision Making Understand how an operational intelligence layer supports proactive decision-making by analysing human-machine interaction data for better planning and performance management. What You Will Learn The importance of planning and continuous improvement in process excellence Strategies for creating an operational intelligence layer to support continuous improvement  How AI-powered process excellence tools enhance task discovery and documentation, identify inefficiencies, and improve user interfaces Real-world examples of how organizations are applying these insights to financial operations, supply chain, procurement, and customer support Meet the author: Maureen Fleming Program Vice President, IDC Don’t miss out on this opportunity to transform your business operations with the power of AI. Fill out the form to download the full report and start your journey towards continuous process improvement.  Get Your Free Copy Today See Scout in action. Schedule your demo now! Get in Touch

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Scout helped a Big 4 Consulting firm in achieving a remarkable 40% reduction in time-to-close for a G500 CPG leader through process optimization and automation

Soroco Admin — Wed, 01 May 2024 08:45:04 +0000

CPG Big 4 Accounting Firm cuts month-end closures by 40% for a F100 CPG giant with Scout Email it to me The Challenge One of Big 4’s notable client’s, a Fortune 100 CPG giant, having 3.4 billion daily customers in over 190 countries and a global workforce of over 120K, encountered critical challenges within its global supply chain finance function. To address these challenges, the client sought the Big 4’s expertise to automate and standardize processes while reducing the head count for third-party operations. The identified challenges included increased workloads and knowledge turnover resulting in errors, posing risks to service level agreements (SLAs). Additionally, it also contributed to heightened dependency risk, concerning rise in operational costs and increase in time required to close month-end activities. The Big 4 urgently needed to address these challenges to safeguard its client’s financial stability and operational efficiency. Industry CPG Location 50+ Countries 120K+ Employees Attempted Solution before Scout Business leaders needed to investigate over 2000 activities across more than 15 sub-processes within 2 months Finance leaders of the CPG company attempted to study the processes by interviewing various teams and by examining existing documentation. However, they soon realized that expert assistance and technology-driven analysis from the Big 4 were essential to investigate over 2,000 activities across more than 15 sub-processes within a tight two-month timeline. Here’s when the Big 4 accounting firm was brought in to do an in-depth process study that would reveal: The underlying as-is process Areas of manual touchpoints, Process inefficiencies, and Opportunities for technology led interventions Here’s when the Big 4 consulting firm was brought in to do an in-depth process study that would reveal: The underlying as-is process Areas of manual touchpoints, Process inefficiencies, and Opportunities for technology led interventions Enter After considerable evaluation, the accounting firm onboarded Scout and leveraged their domain expertise to achieve a comprehensive understanding of the processes. They amplified their traditional consulting approach with insights from Scout to eliminate potential human biases, providing a holistic 360-degree view. Scout was quickly put into action as it didn’t need complicated integration with current systems and was able to cater to the customer’s application stack. The Scout AI model analysed the interaction data of finance teams across various applications, mapping out how and why work happens the way it does within the supply chain finance function. This led the model to discover a key insight: significant effort was spent on non-core apps (MS Excel, Outlook) across processes. Within 2 weeks, Scout discovered a key insight- significant effort was spent on non-core apps across processes. Scout to “find and fix” Step 1: Find As the first step, Scout decoded the work patterns of the supply chain finance function and connected them with business activities by analysing interactions between the team and their software. It then automatically classified these work patterns as either core process activities or non-core activities. Within two weeks, the following insights were shared: 3000+ Excel files were used, with approximately 20% of the effort involving repetitive and predictable data entry. Harvard Business Review Do You Know How Your Teams Get Work Done? Read article Going further, Scout also inferred the reasons as to why teams spent so much time on non-core activities: information silos and disconnected systems. 20% of the user’s day (~27000 hours per year) was spent toggling between apps, logging into various applications, and managing files and folders, ultimately resulting in a loss of productivity and context. Based on this analysis and the functional expertise of the Big 4, the client validated the data, getting complete visibility into its As-Is processes. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Step 2: Fix Further to these insights, the AI model recommended two levels of fixes: Quick Fixes No-code’ fixes based on standardization and user training. Standardization Standardization By leveraging Scout and their domain expertise, the accounting firm recommended standardizing data across multiple MS Excel sheets to save time and effort in organizing and validating data. Deep Fixes Systemic and long-term fixes planned across the organization. Deploy RPA BOTS Deploy RPA BOTS Scout highlighted the root cause of all issues: there were significant digital gaps in the overall process. These were predominantly because significant manual effort was spent on processing information from various excel sheets, removing duplicates and maintaining sheets across several folders associated with multiple countries. The accounting firm then identified and presented a prioritized list of processes that needed to be automated. This was aimed at enhancing process efficiency while also reducing manual effort and costs. Design an integrated system: Design an integrated system: Scout also provided detailed insights into the costs associated with unintegrated systems, applications, and disconnection debt within the organization. With the goal of unifying user experience across multiple applications and documents, the accounting firm developed a roadmap for a digital transformation initiative with these insights. It also implemented an integrated system to seamlessly gather inputs, validate data, and process requests efficiently. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all these recommendations, privacy was of utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues below: Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Reducing time to close for financial operational activities Strategic Payoff In summary, the project was able to drive the following business outcomes 25% Reduction in manual touchpoints by automating 15+FTEs 40% Reduction in time to close for financial operational activities 7% Discovery of excess capacity in processes 14% Increase in financial reporting accuracy How AI connects interaction data to business outcomes Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue… Continue reading Scout helped a Big 4 Consulting firm in achieving a remarkable 40% reduction in time-to-close for a G500 CPG leader through process optimization and automation

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How Tech Mahindra used ScoutAI to Streamline Operations fora UK Telecom Giant, ReducingProcessing Effort by 34%

Soroco Admin — Tue, 30 Apr 2024 12:59:34 +0000

Telecom How Tech Mahindra used Scout to streamline operations for a UK telecom giant, reducing processing effort by 34% The Challenge One of Tech Mahindra’s leading telecom customers in the UK, having a global presence, was grappling with inefficient and variable order fulfilment, ITSM, Sales Ops, and billing processes. This inefficiency led to increased time expenditure, revenue leakage, reduced process accuracy by over 50%, a suboptimal end-user experience, and eventually high cost of operations with severe operational issues impacting the morale of several employees. The company had already used a myriad of discovery tools to identify end-to-end opportunities for reducing the cost of operations but had not yet achieved the desired level of optimization. Location UK Industry Telecom 12 Subsidiaries 85,000+ Employees Attempted Solution before Scout Consultants requested an additional 1,200 hours for Manual Discovery Workshops The company engaged multiple discovery tools to overcome the operational shortcomings. They went about this in a traditional manner, but these tools operated in silos, leading to fragmented insights that failed to provide a holistic view of process inefficiencies. This traditional approach lacked the integration and sophisticated analytics needed to pinpoint critical bottlenecks and did not account for the nuanced variations in workflows across different teams and locations. Enter The business heads of the company then worked with Tech Mahindra’s BPS Team for evaluating and onboarding Soroco to find and fix these problems. Soroco’s Scout was brought into play, offering a suite of automation and standardization recommendations tailored to tackle the specific challenges faced. Scout AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analysed how their customer teams interact with various applications, mapping out how and why work happens the way it does within the said processes. This led Scout to discover a key insight: there was excessive manual intervention in order pre-validation, generation, and validation processes. Within 6 weeks the Scout AI model uncovered a critical insight – the problem lay with high manual processing effort. Scout to “find and fix” Step 1: Find As the first step, AI decoded the work patterns of the different teams and connected them to business activities, simply by analysing interactions between the team and their software. It then automatically classified these work patterns as either core business activities or non-core activities. Based on this analysis, within two weeks, the Scout AI model provided the following insights: 40% time was spent on core business apps and activities. Contrary to popular belief, only 40% of the team’s effort was spent on core business apps and activities. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work was being done and how work should actually be done. 60% time was spent on non-core activities such as pre-validation, managing workflow, manual order creation, duplicate billing note generation, one-off adjustments, sales ops validation activities, and more Harvard Business Review Do You Know How Your Teams Get Work Done? Read article The AI further helped Tech Mahindra in determining the following: Key Observation Impact Managing spreadsheets and custom reporting The team was manually managing data across 100+ spreadsheets and creating custom reports for management leading to ~10,000 hours of manual work Application switch A common trend was identified between different teams as they spent so much time on non-core activities – information silos and disconnected systems, forcing the team to incessantly toggle between different applications ultimately resulting in manual inefficiency and loss of productivity to the tune of 18500 hours annually Manual verification and authentication Scout identified that the team was spending close to ~20,000 hrs annually i.e. 16% of process effort just for verifying & authenticating the right customers Composing similar emails The Scout AI model was able to identify similar topics across several email chains and found a high number of email topics which if automated could help to save ~8,000 hours annually Step 2: Fix Based on the above insights, Scout AI model recommended the following fixes: Quick Fixes Identified 100+ user training opportunities Produced As-Is & To-Be process snapshots for different processes Produced 70+ L5 process-designed documents/training documents to drive standardization in training Created conformance view – gold standard (what’s known to the business) vs what standard users follow (unknown to the business) Introduce an excel-based template to take notes which can be used across teams and reduce time to analyze issues if it is not resolved in first time Deep Fixes Build RPA bots across Customer order, validation, and ITSM processes Build RPA bots across Customer order, validation, and ITSM processes By introducing RPA-based query resolution across different processes, the company was able to increase the self-serviceability of online portals and reduce manual effort. This led to expedited process efficiency of the team’s bandwidth, allowing them to focus on core business activities. Standardise workflows Standardise workflows Leadership also made a concerted effort to standardise excel-based templates and introduce chatbot-based self-serviceable channels to handle simple queries from customers and later scale up in other areas. IVR based customer validation was introduced to free up users’ bandwidth and improve case handling & call wait time. Self-serviceable channels was also introduced to drive correct routing of workflows within teams, saving them collaborative ~50,000+ hours. Standardise data inputs Standardise data inputs The leadership enforced standardisation of data inputs, which reduced the effort and time spent on organising and validating the input data received by different teams. Centralized Governance Centralized Governance The company focused on centralised governance to track proactive process variation anomalies & followed up with customers by dedicated teams to reduce overall wait time. The root cause of all issues was that there were significant digital gaps in the overall process, and these were predominantly because systems did not talk to each other, and because data was duplicated/fragmented Scout also provided detailed insights into the cost of unintegrated ERP systems and, discovered revenue leakages in the organisation. Based on these insights, the leadership kicked off… Continue reading How Tech Mahindra used ScoutAI to Streamline Operations fora UK Telecom Giant, ReducingProcessing Effort by 34%

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How LNER uncovered inefficiencies in its Accounts Payable process and reduced payments processing time by 75% using AI

Soroco Admin — Tue, 30 Apr 2024 07:14:06 +0000

How LNER uncovered inefficiencies in its Accounts Payable process and reduced payments processing time by 75% using AI For four decades, some processes at London North Eastern Railways have remained unchanged. At this pioneering train company, the lack of comprehensive documentation has made transforming these areas particularly challenging. Now, with a firm commitment to improving the experience for both customers and employees, LNER is ready to embark on this transformation journey with Scout. Watch Adrian Varma talk about this journey and the business outcomes achieved with the help of AI and interaction data. Scout to “find and fix” Within weeks of implementation, Scout’s AI model uncovered inefficiencies in their Accounts Payable & Payroll function and solved some of their biggest challenges. 45% increase in overall Accounts Payable and Payroll efficiency 30% reduction in payment processing time 100% improvement in payroll accuracy Summary Watch how LNER, a leading UK train company, transformed its operations with digital innovation. Adrian Varma, Head of Business Transformation at LNER, shares how focusing on customer and employee experience led to significant business improvements. LNER’s journey started with a commitment to exceptional customer service. To tackle industry challenges, they built a machine learning team and adopted AI technologies to streamline processes and boost customer service. Partnering with Soroco and ABP, they used the Scout AI model for process optimization and workflow efficiency. This helped automate tasks, standardize processes, and achieve operational excellence. The results? Reduced delays, increased productivity, and greatly improved customer experience. By fostering a culture of continuous improvement, LNER saw remarkable benefits across the board, setting new standards for efficiency and performance. LNER’s success story showcases the power of AI and Interaction Data for business growth and customer satisfaction. Watch this video to learn more about LNER’s transformative journey. Explore more Transformation Stories https://www.youtube.com/watch?v=AAhaNYw3h8wUnlocking post-merger synergies at Bayer with Scouthttps://www.youtube.com/watch?v=jrSRokN1WAoMorgan Sindall reverses reputational risk with Scout Industry recognition Everest Group Leader in PEAK Matrix® Assessment: Digital Interaction Intelligence, 2024 NelsonHall Leader in NEAT Assessment: Process Understanding, 2024 HFS Research Enterprise Innovator in HFS Horizons: Process Intelligence Products, 2023 Forrester Strong Performer in The Forrester Wave™: Process Intelligence Software, Q3 2023 Soroco rated 4.9/5 in Gartner Peer Insights by our customers.  5/5

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How Morgan Sindall Transformed its Payroll Operations and Reduced Employee Attrition by 26% using AI

Soroco Admin — Tue, 30 Apr 2024 06:34:11 +0000

How Morgan Sindall Transformed its Payroll Operations and Reduced Employee Attrition by 26% using AI Morgan Sindal, a construction major in Europe was grappling with severe payroll operational issues impacting over 6000 staff members across 4 subsidiaries. The already overworked payroll team was experiencing increased stress each day, leading to attrition, and creating reputational risk for the organization. Scout to “find and fix” Within just 2 weeks of implementation, Scout’s AI model uncovered critical insights and solved some of their biggest challenges Significantly improve how employees experience work and improve their morale 95% reduction in digital gaps 100% improvement in payroll accuracy 26% reduction in employee attrition Summary Discover how Morgan Sindall, a UK construction and infrastructure leader, transformed its operations with Steven Still, Head of Finance and Transformation, at the helm. Partnering with Soroco, they modernized their shared services center and optimized key processes. Steven’s vision focused on aligning people, processes, and technology. By identifying improvement opportunities and updating their technology, Morgan Sindall made significant strides. With Soroco’s data-driven insights and the Scout AI model, they enhanced critical functions like payroll and purchase-to-pay (P2P), finding many opportunities for automation. This led to better decision-making and meaningful change. The results? Improved staff morale, higher productivity, and streamlined processes. Embracing continuous improvement and technology, Morgan Sindall saw a major shift in operations. Watch this video to learn how strategic partnerships and data-driven decisions can drive excellence and growth. Explore more Transformation Stories https://www.youtube.com/watch?v=AAhaNYw3h8wUnlocking post-merger synergies at Bayer with Scouthttps://www.youtube.com/watch?v=fgXjjFaPB2oTransforming customer & employee experience at LNER with Scout Industry recognition Everest Group Leader in PEAK Matrix® Assessment: Digital Interaction Intelligence, 2024 NelsonHall Leader in NEAT Assessment: Process Understanding, 2024 HFS Research Enterprise Innovator in HFS Horizons: Process Intelligence Products, 2023 Forrester Strong Performer in The Forrester Wave™: Process Intelligence Software, Q3 2023 Soroco rated 4.9/5 in Gartner Peer Insights by our customers.  5/5

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Soroco Admin — Mon, 29 Apr 2024 10:25:34 +0000

Discover AI investments in your organization to improve business outcomes

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Soroco Admin — Mon, 29 Apr 2024 10:23:15 +0000

Streamline IT migration

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Bayer reduced cost of operation by 30% through post-merger transformation using AI

Soroco Admin — Thu, 25 Apr 2024 10:39:29 +0000

How Bayer reduced cost of operation by 30% through post-merger transformation using AI Bayer, a global 500 pharmaceutical company grappled with post-merger challenges, striving to achieve synergy targets, particularly in streamlining supply chain operations. The delay in realizing critical assumptions increased pressure on the CFO and management to fulfill commitments made to the board. Scout to “find and fix” Scout’s AI model was quickly put into action across, 7 teams in 12 countries. The AI analyzed how supply chain teams interact with various applications, mapping out how and why work happens the way it does within the supply chain operations which led to: 30% Reduction in Operational Costs: Streamlining processes and reducing inefficiencies. 10% Improvement in Cash Flows: Reflecting a more efficient and reliable supply chain process. 25% Reduction in Delays and Errors: In order processing, enhancing reliability and customer satisfaction Summary Join us to explore Bayer’s transformative journey with Scout insights and discover how leveraging technology can drive operational efficiency and strategic alignment in your organization. Watch Radovan Simic, Bayer’s Head of Digital Transformation, as he explains how Bayer improved its planning processes using Scout insights. Scout identified inefficiencies, showing too much time was spent on manual tasks in MS Excel instead of the ERP system. These insights led to streamlined processes, boosting productivity and employee satisfaction. Looking forward, Bayer plans to use Scout insights for their SAP S4/Hana implementation, aiming to streamline operations and prepare for future upgrades. Bayer’s experience with Scout shows the importance of continuous improvement, bridging the gap between human skills and AI, and fostering a culture of innovation. Watch how Bayer’s use of AI and Interaction Data helped their organization’s operational efficiency and strategic alignment. Explore more Transformation Stories https://www.youtube.com/watch?v=jrSRokN1WAoMorgan Sindall reverses reputational risk with Scouthttps://www.youtube.com/watch?v=fgXjjFaPB2oTransforming customer & employee experience at LNER with Scout Industry recognition Everest Group Leader in PEAK Matrix® Assessment: Digital Interaction Intelligence, 2024 NelsonHall Leader in NEAT Assessment: Process Understanding, 2024 HFS Research Enterprise Innovator in HFS Horizons: Process Intelligence Products, 2023 Forrester Strong Performer in The Forrester Wave™: Process Intelligence Software, Q3 2023 Soroco rated 4.9/5 in Gartner Peer Insights by our customers.  5/5

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Generative AI: How it learns and works | Soroco Tech Talk

Soroco Admin — Thu, 18 Apr 2024 07:30:11 +0000

Generative AI: How it learns and works 7th May 2024 6:30 PM IST | 9:00 AM EST | 1:00 PM GMT About this Session Since the introduction of ChatGPT in November 2022, the promise and impact of generative AI have become increasingly clear. This talk will provide a deeper understanding for how large language models are trained and work, including discussion on their broader impact. No matter whether you are in a tech or non-tech role – this session will help you better understand generative AI and its relevance to all of us. About the Speaker Teddy Svoronos is a Senior Lecturer of Public Policy at the Harvard Kennedy School, where he teaches courses in using statistical methods to improve public policy. His primary interest lies in the use of technology to replicate the dynamics of small classes on a large scale. To this end, Teddy develops fully online courses and blended learning modules that he uses to teach residential students, as well as civil servants abroad. He is also a cofounder of Teachly, a web application focused on creating effective and inclusive learning environments. Teddy received his PhD in Health Policy from Harvard University and his Master’s in Public Health from Columbia University. Senior Lecturer of Public Policy at Cofounder of About this Session A regular series by Soroco, Tech Talks are expert-led technical sessions that deep dive into a specific area of technology and provide engineers valuable insights and tools. It also examines fascinating research, use cases and facilitates larger conversations around cutting-edge tech. Registration is now closed for this Tech Talk See Scout in action. Schedule your demo now! Get in Touch

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How AI is helping the public sector up the ante on service transformations

Soroco Admin — Wed, 17 Apr 2024 09:50:02 +0000

White Paper How AI is helping the public sector up the ante on service transformations. Today, public sector and government organizations are facing growing pressure to streamline their operations and lower operating costs. Furthermore, there is an increasing public demand for efficient and dependable services, prompting a continuous review of processes and the integration of digital technologies. This white paper explains how CGI, one of the largest IT and business consulting services firms in the world, is leveraging Scout AI model to help the public sector and government organizations with their digital transformation and automation by analysing their business processes in real-time, identifying bottlenecks, inefficiencies and redundancies slowing down their operations. Read more in the white paper on the top five use cases demonstrating how the power of AI can bring in unprecedented value in the public sector. Meet the author: Simon Greenwood, Intelligent Automation and Future of Work Advisor, CGI Simon works closely with CGI clients across the UK advising them on the technology enabled business change that is driving the 4th Industrial Revolution. A firm believer that technology should free people from risky, mundane highly repetitive work to allow workers to enjoy more interesting, fulfilling and mentally rewarding work, Simon advises organisations to increasingly embrace Intelligent Automation and Artificial Intelligence to build hybrid workforces of humans and bots, working seamlessly together. He regularly assists senior managers onto their long term automation journeys, helping them to Think Big, Start Small, Scale Quickly to deliver the future of work and hybrid workforces. Simon is also a member of The Whitehall Industry Group (WIG) Technology and the Future of Work panel as well as of techUK AI & Cyber Security working group. See Scout in action. Schedule your demo now! Get in Touch

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A leading mortgage servicing company improves STP with Scout AI

Soroco Admin — Tue, 16 Apr 2024 08:23:16 +0000

Banking and Finance Improving straight-through processing by 40% for a leading mortgage servicing company with Scout Email it to me The Challenge A leading mortgage servicing company in the U.S., with 2 subsidiaries and more than 6,000 employees, faced critical challenges in its underwriting function. The workload for lending underwriters varied significantly, leading to overwork, errors, miscalculations, missed SLAs, and, consequently, customer dissatisfaction. These issues adversely affected the company’s reputation in the competitive market. Industry Financial Services Location United States 6,000 Employees 2 Subsidiaries Attempted Solution before Scout About 1,000 hours required from the business teams for discovery workshops The firm appointed a SWAT team to “find and fix this problem,” but their initial solutions fell short. Despite deploying a process mining solution and conducting ‘discovery workshops’ to understand the team’s work patterns, these methods did not provide a comprehensive view of the underlying issues. The extensive time required from the business teams for these workshops—about 1,000 hours—further strained the already overburdened staff, leading to increased stress and attrition. Enter The Head of Process Excellence decided to implement Scout, to find and fix the firm’s challenges. Scout was deployed swiftly, integrating seamlessly without the need for complex adjustments to the existing systems. Scout revealed crucial insights within the first week Scout’s AI to “find and fix” Scout’s AI to “find and fix” Step 1: Find Immediately after deployment, Scout revealed crucial insights within the first week: Only 60% of the underwriters’ time was dedicated to core underwriting tasks. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more The startling statistic that underwriters toggled between applications and documents 800-1,000 times a day, due to unintegrated systems and fragmented data, pointed to a massive work recall gap highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. The analysis also found a heavy reliance on manual processes and “shadow IT” systems, like Excel spreadsheets, which were inefficient and time-consuming. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Armed with these insights, the firm quickly implemented targeted solutions: Quick Fixes Training sessions were conducted to elevate the entire team’s performance to the level of the most experienced members A central repository for underwriting calculators and templates was created Deep Fixes The leadership initiated a digital transformation initiative to design an end-to-end workflow system, reducing the disconnection debt by integrating disparate systems and streamlining processes. An intelligent underwriting assistant was also introduced, further enhancing productivity and accuracy. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read more Strategic Payoff With Scout’s intervention, the mortgage firm achieved remarkable improvements: Straight-through processing improved by 40% Productivity of lending underwriters increased by 30% Operational costs were reduced by 15% Key operating principles Scout’s deployment was driven by principles that are essential to every success story: Empathy at the Core: It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e., getting teams to work together across functions, fixing disconnected systems, and simplifying and optimizing work processes. Continuous Visibility and Improvement: Scout was an integral part of this transformation, providing continuous visibility and enabling ongoing improvements. How AI connects interaction data to business outcomes Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes. We call this lighting up the ‘dark side of the moon’. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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Transforming CX for Asia’s largest private bank with Scout

Soroco Admin — Fri, 12 Apr 2024 08:24:37 +0000

Retail and Corporate Banking Transforming Customer Experience, and a 30% enhancement in NPS for Asia’s Largest Private Bank with Scout Email it to me The Challenge Asia’s largest private retail bank, with over 8,000 branches and 150,000 employees, was at a crossroads. Faced with fierce competition from digital-first banks, it struggled with missed customer service level agreements (SLAs), high operating costs, and an outdated customer experience framework. The bank handled nearly 10 million customer requests annually, with a significant portion—70% of customer instructions like address changes, contact updates, and new checkbook issuances—being processed manually through its extensive branch network. This not only strained the bank’s resources but also jeopardized its reputation for customer service excellence. Industry Retail and Corporate Banking Location APAC 150,000 Employees 8,000 Branches Attempted Solution before Scout Demanded approximately 3,000 hours to conduct in-depth discovery workshops and interviews The bank’s leadership initiated a digital transformation effort, aiming to overhaul its customer service operations. This included partnering with a Systems Integrator (SI) and deploying traditional process mining tools, which ultimately failed to capture the complete picture of the bank’s operational inefficiencies. Furthermore, attempts to conduct in-depth discovery workshops and interviews to understand team workflows demanded approximately 3,000 hours from the business teams, exacerbating their workload and leading to increased stress and attrition among the staff. Enter In search of a more effective solution, the bank’s Head of Digital Strategy and Banking Operations decided to implement Scout, to find and fix this problem within their Central Processing team. This team was responsible for managing the bulk of the customer service requests, numbering around 1 million annually. Scout’s deployment was swift and seamless, requiring no intricate integration with the bank’s existing systems and fully adhering to the strictest data security and privacy standards. Within just 2 weeks of deployment Scout’s analysis unearthed several critical insights Scout’s AI to “find and fix” Scout’s AI to “find and fix” Step 1: Find Scout’s analysis unearthed several critical insights within just two weeks of deployment: A significant discovery was that 80% of branch requests were not related to debit cards, indicating a potential area for enhancing digital self-service options. Another alarming issue was that 70% of the requests handled by the central processing unit were placed on hold due to incomplete or incorrect information from the branches. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more The analysis also showed that the team spent about 30% of their time toggling between the core banking application and the document tracking system, leading to inefficiencies and delays. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Step 2: Fix Armed with Scout’s insights, the bank swiftly implemented several quick and deep fixes: Quick Fixes Enhancements to the net banking and mobile app interfaces for non-debit card related services were rolled out Increased marketingefforts to promote digital channel usage Enhancements to the net banking and mobile app interfaces for non-debit card related services were rolled out Deep Fixes The bank initiated a project to integrate the core banking system with the form tracking system, reducing the need for manual toggling and to reduce the disconnection debt. It also developed a combined cognitive automation and OCR-based solution for the service request workflow, significantly improving straight-through processing rates. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read more It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout was able to drive the following business outcomes 30% improvement in end-customer NPS 20% reduction in operational costs, streamlining the bank’s expenses 80% improvement in straight-through processing of customer instructions, demonstrating the efficiency gains from the implemented solutions. AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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Happiest Minds SEM LP

Soroco Admin — Wed, 10 Apr 2024 12:44:39 +0000

Ram Mohan, CEO, Infrastructure management and security services Happiest Minds Happiest Minds has always believed in forging the right kind of strategic partnerships in the industry to deliver cutting-edge technology and superior digitally engineered platforms. We foresee a long-standing association with Soroco on our Automation and AI-led services. We hope together, we innovate and deliver futuristic solutions and services that will help our customers transform their technology ecosystem and meet their business goals.

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Digital Transformation Lead sem lp

Soroco Admin — Wed, 10 Apr 2024 12:42:48 +0000

Radovan Simic, Digital Transformation Lead Bayer Scout highlighted the ‘unknows’ of our processes. This was not possible through any other means!

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Everest illuminate sem lp

Soroco Admin — Wed, 10 Apr 2024 12:37:40 +0000

Amardeep Modi, Everest Group Everest Group Soroco has reinforced its position as a Leader for the second consecutive year on Everest Group’s Task Mining Products PEAK Matrix® 2023, owing to its strong vision, depth & breadth of product capabilities, and continuous focus on product innovation and thought leadership. It also emerged as a Star Performer due to a strong year-over-year growth in its market impact and capabilities. Product vision & roadmap, ability to identify automation and process enhancement opportunities, and data protection & privacy are some of the key strengths indicated by its clients.

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A Global Pharma Company Reduced Cost of Operation by 30% through Post-Merger Transformation with Scout

Soroco Admin — Thu, 21 Mar 2024 06:01:46 +0000

Pharmaceutical A Global Pharma Company Reduced Operational Costs by 30% through Post-Merger Transformation with Scout Email it to me The Challenge A Global 500 pharmaceutical company was struggling with the aftermath of a significant merger and acquisition. The management faced the daunting task of realizing synergy targets committed to the board, particularly in consolidating processes, teams, and systems within the supply chain operations. These critical assumptions had yet to materialize, putting the CFO and the entire management under pressure to deliver on their promises. Industry Pharmaceutical Location Global Attempted Solution before Scout Demanding 12,000- 15,000 hours of business users involvement was unsustainable The CFO, Head of Supply Chain, and Chief Digital Officer, along with their internal digital transformation team and system integrator, embarked on a mission to rectify the post-merger challenges. They deployed a process mining solution and organized manual ‘discovery workshops’ to understand the work patterns of their teams. However, these methods proved to be time-consuming and inadequate, demanding an unsustainable 12,000-15,000 hours of business user involvement across 66 processes and 13 countries, without providing the insights needed. Enter The CFO and Chief Digital Officer then evaluated and onboarded Scout to find and fix these problems. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how supply chain teams interact with various applications, mapping out how and why work happens the way it does within the supply chain operations. This led Scout to discover a key insight: the root of the problem was not just within the supply chain teams but due to poor visibility of key process information and lack of clear SOPs. Scout’s AI model could be quickly put into action across 5 clusters in 13 countries, because it didn’t need complicated integration with current systems. Scout to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the supply chain teams and connected them to business activities, simply by analyzing interactions between the teams and their software. It then automatically classified these work patterns as either core or non-core supply chain operational activities. Based on this analysis, within two weeks, Scout’s AI model provided the following insights: Contrary to popular belief, only 58% of the team’s effort was spent on core supply chain applications and activities. This surprising statistic pointed to a significant work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more The AI also determined that the remaining 42% of the time was spent manually managing multiple spreadsheets, organizing and feeding data into various supply chain systems, addressing queries and coordinating with regional and central supply chain teams and suppliers on communication apps. It also pointed out regional discrepancies, like teams in Southeast Asia spent an average 30% of their time on order management in Excel spreadsheets whereas the teams in India, Bangladesh and Sri Lanka spent 70% of their time on Excel spreadsheets. Scout’s AI model further uncovered operators 1200 context switches per operator per day, due to toggling between applications due to information silos and disconnected systems. These bottlenecks were forcing the team to incessantly toggle between different applications, ultimately resulting in a significant loss of productivity. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read more Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article Step 2: Fix Based on the above insights, Scout’s AI model recommended two levels of fixes: Quick Fixes These were ‘no-code’ fixes based on standardization and user training Deep Fixes Systemic and long-term fixes planned across the organization It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff The strategic implementation of Scout’s recommendations led to: 30% Reduction in Operational Costs: Streamlining processes and reducing inefficiencies. 10% Improvement in Cash Flows: Reflecting a more efficient and reliable supply chain process. 25% Reduction in Delays and Errors: In order processing, enhancing reliability and customer satisfaction. AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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Building a Healthy Transformation Pipeline through Hybrid Process Mining

Soroco Admin — Tue, 27 Feb 2024 09:57:31 +0000

White paper Building a Healthy Transformation Pipeline through Hybrid Process Mining According to Everest Group, only 20% of organizations have scaled their process transformation initiatives from pilots to enterprise-wide programs. The #1 barrier to success? Identifying opportunities and sustaining a pipeline. Amardeep Modi Practice Director at Everest Group The key to scaling transformation initiatives is building and maintaining a healthy pipeline of opportunities. This can be achieved by following a four-step approach. Each aspect of a healthy pipeline feeds the next, helping create a continuous cycle of opportunity discovery, prioritization, and value realization. Download the white paper to learn why enterprise leaders are adopting emerging hybrid process mining solutions to enable a heathy pipeline and the advantages they provide over existing approaches. See Scout in action. Schedule your demo now! Get in Touch

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ISG Provider LensTM ranks Soroco as a Leader for Process Discovery and Mining

Soroco Admin — Tue, 27 Feb 2024 07:51:34 +0000

Analyst Report ISG Provider Lens™ ranks Soroco as a Leader for Process Discovery and Mining ISG evaluated the capabilities of 20 process discovery and mining providers across eight criteria. Soroco ScoutTM stands out as a “Leader” for its robust process and task discovery, intelligent information extraction, and comprehensive process mining capabilities in the U.S. “SOROCO is driving an exceptional narrative of information mining through an enterprise-wide process landscape to demystify the data that lies outside of the system of records. The work graph enunciates the correlation of activities irrespective of the enterprise system. This is a unique capability and approach to simplify complex information flow.” Ashwin Gaidhani Lead Analyst at ISG Key strengths identified in the ISG Provider Lens™ report include: Innovative process and task discovery Intelligent information extraction    Comprehensive process mining capabilities Find out why Soroco was named a Leader by ISG and how organizations can leverage process discovery and mining to accelerate their transformations. See Scout in action. Schedule your demo now! Get in Touch

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Happiest mind with Soroco

Soroco Admin — Wed, 21 Feb 2024 08:20:19 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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Tech M partnership with Soroco

Soroco Admin — Tue, 20 Feb 2024 12:15:31 +0000

Soroco is strategically important on our approach providing important process discovery capabilities that help us and our clients to detect earlier the right opportunities for automation.

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launches-first-ever-task-mining-playbook-by-everest-group

Soroco Admin — Mon, 19 Feb 2024 12:32:12 +0000

Analyst Report Soroco Launches First-Ever Task Mining Playbook By Everest Group Accelerate your digital transformation and automation journey with task mining The first-ever Task Mining Playbook demonstrates the impact of task mining and automation for boosting business objectives and provides industry trends and best practices that serve as both a guide and tool for your organization. The Playbook is designed to be your roadmap and provides a step-by-step methodology to start task mining journey for your organization: It contains a Capability Maturity Model (CMM) Framework to accelerate digital transformation and scale automation. Guidance on building a business-aligned approach to process optimization, driving operational transparency and steps to achieve automation success with task mining. Identify automation potential and use cases at both macro and micro levels and determine the cost savings through automation opportunities. See Scout in action. Schedule your demo now! Get in Touch

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analyst2

Soroco Admin — Fri, 16 Feb 2024 13:05:57 +0000

NelsonHall Soroco was positioned as a Leader in NelsonHall’s 2023 Process Understanding NEAT evaluation in the Task Mining market segment due to its ability to create work graphs from human–computer interactions to support process transformations; this goes beyond simple RPA and into workflow automation, IDP, email templatization, and conversational AI. Soroco’s platform also remains one of the few task mining platforms to support the ingestion of process mining data. Mike Smart, NelsonHall Mike Smart, NelsonHall

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analyst1

Soroco Admin — Fri, 16 Feb 2024 13:00:43 +0000

Everest Group “Soroco has reinforced its position as a Leader for the second consecutive year on Everest Group’s Task Mining Products PEAK Matrix® 2023, owing to its strong vision, depth & breadth of product capabilities, and continuous focus on product innovation and thought leadership. It also emerged as a Star Performer due to a strong year-over-year growth in its market impact and capabilities. Product vision & roadmap, ability to identify automation and process enhancement opportunities, and data protection & privacy are some of the key strengths indicated by its clients.” Amardeep Modi, Everest Group Amardeep Modi, Everest Group

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analyst3

Soroco Admin — Fri, 16 Feb 2024 12:01:20 +0000

HFS Research AI can only be truly effective for a business if it captures all human-to-machine interactions across the enterprise, not just the typical systems of record, and Soroco’s platform is having a real impact with its clients. Soroco has developed a unique capability to generate real context and understanding on how a business operates. Phil Fersht, HFS Research Phil Fersht, HFS Research

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Telecom company use case solved by scout

Soroco Admin — Thu, 15 Feb 2024 07:00:04 +0000

Telecom A leading multinational telecommunications giant improved its payment collection rate by 35% with Scout. Email it to me The Challenge A leading telecommunications giant was grappling with operational inefficiencies in its Collections division. The team faced a daunting task: managing 260k cases of unsettled credit bills annually, leading to significant impacts on revenue recognition, cash flow, and key financial metrics. The collections team had to constantly follow up with customers for payment and were often met with undesirable cases such as contact but no payment confirmation; voicemail, no response; redirected to 3rd parties. They also had to deal with cases in which customers disputed the charges or requested payment extensions. The team was spending a chunk of their time researching and settling such cases. As a result, Mid-Management was unhappy as key business metrics such as Past Dues & Average Days Delinquent were impacted, which in turn were affecting key financial metrics. Leadership was unhappy as Revenue Recognition; Cash flow and Revenue Forecasting were impacted since less than 25% of overdue payments were getting completed. Collections teams were dissatisfied as they could not meet their key performance targets such as Collections Settlement Rate, Positive Outcome Rate & Payment Realization Rate since their best attempts at collection were hampered by upstream issues and negative customer responses. Industry Telecom Location Europe Attempted Solution before Scout The consultants followed the traditional approach of interviewing subject matter experts which was a manual effort: time intensive and subjective The company engaged a top finance transformation consulting firm to optimize the Accounts Receivable process. The consultants followed the traditional approach of interviewing subject matter experts (SMEs) from the Collections team, which was a manual effort, time intensive and subjective in nature. Moreover, SMEs were already overstretched and had no time to answer detailed questions and provide extensive data which was needed by the consultants. As a result, the leadership could not achieve their desired business outcomes and the problems persisted for leadership, management and collections team. This manual approach didn’t add any real value and intensified the problem. Enter The CFO and Head of Digital Transformation turned to Scout to find and fix problems in their Account Receivable operations. Deployed within hours and without needing complex integration, Scout focused on team-based insights while ensuring user anonymity and transparency. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how collections teams interact financial accounting systems and other applications, mapping out how and why work happens the way it does within the collections function. Scout’s AI model decoded the work patterns of the collections team and connected them to business activities Scout to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the collections team and connected them to business activities, simply by analyzing interactions between the team and their software. It then automatically classified these work patterns as either core account receivable activities or non-core activities. Based on this analysis, within two weeks, Scout’s AI model provided the following insights: 60% time was spent on the core financial system Scout’s analysis revealed that only 60% of the Collections team’s time was spent on the core financial system. A significant portion of their time was dedicated to low-priority cases like ‘no payment confirmation’, ‘voicemail’, ‘no response’, ‘redirected to 3rd parties’ and cases with upstream issues (ex. erroneous case creation, payments made but not reflecting in system). Due to the lack of case categorization to identify and de-prioritize such cases, ~50% of cases they worked on, remained unsettled. Harvard Business Review Do You Know How Your Teams Get Work Done? Read more 40% time was spent on manual communications The AI also determined that the remaining 40% of the time was wasted on manual and repetitive tasks, document handling, and toggling between applications. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Harvard Business Review Do You Know How Your Teams Get Work Done? Read more Step 2: Fix Based on these insights, the management team devised a strategic action plan: Quick Fixes These were ‘no-code’ fixes based on standardization and user training Email Templatization Email Templatization Standardizing the content and format of frequent communication to streamline customer interactions. Document Consolidation Document Consolidation Centralizing collections-related documents to reduce time spent on file searching and toggling. Note Generator Application Note Generator Application Explore tools to save time on manual note-taking and documentation. Deep Fixes Systemic and long-term fixes planned across the organization Scout also provided detailed insights into the cost of unintegrated payroll systems and, also the disconnection debt in the organization, leading to deep fixes across the function. Common Case Management System Common Case Management System Implementing a system to log, categorize, and prioritize customer collections cases effectively. RPA for Manual Validations RPA for Manual Validations Several customer validations, verifications and research in the dispute and payment extension processes could be automated to perform the manual activities with rule-based automation. New Notification Channels New Notification Channels Introducing a mobile app and text notifications to nudge customers towards timely payments and self-service options. All notifications to remind customers of overdue payments could be sent through the Business App, providing them with the option to request a callback. Automated calls could be used as reminders, specifically targeting groups that tend to pay over the phone (e.g.DD/Payment Details provided) Note Generator Application First Contact via Business App Encouraging customers to schedule callbacks, reducing the time spent on unsuccessful contact attempts. This also acts as a nudge for the customer towards self-serve (via Business App) by providing incentives associated with reduction in late payment fees Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read more It is important to note… Continue reading Telecom company use case solved by scout

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Everest illuminate Quotes

Soroco Admin — Wed, 14 Feb 2024 12:37:44 +0000

Amardeep Modi,Everest Group Everest Group Soroco continues to demonstrate strong execution of its vision to unlock sustainable and scalable business value for its clients. The depth and breadth of product capabilities and use cases, its investments in foundational AI models for interaction data, and a strong market presence in the DII space have helped Soroco emerge as a Leader and Star Performer on Everest Group’s Digital Interaction Intelligence Products PEAK Matrix® 2025. Its product vision and roadmap, ease of use and intuitive interface, the platform’s strong AI foundation, quality of insights, and responsive customer support are some of the key strengths indicated by its clients.

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HFS illuminate-quotes

Soroco Admin — Wed, 14 Feb 2024 12:36:37 +0000

Phil Fersht, HFS Research HFS Research AI can only be truly effective for a business if it captures all human-to-machine interactions across the enterprise, not just the typical systems of record, and Soroco’s platform is having a real impact with its clients. Soroco has developed a unique capability to generate real context and understanding on how a business operates

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Hear from people-Mohi

Soroco Admin — Tue, 06 Feb 2024 04:55:41 +0000

“I am immensely proud to be a part of Soroco. What sets Soroco apart is not just the groundbreaking work we do, but the brilliant minds that make up the organization. The people here are not just colleagues; they are mentors, collaborators, and friends.” Mohi Shukla, Head of Customer Growth – AMS Mohi Shukla, Head of Customer Growth – AMS

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Hear from people-Cliff

Soroco Admin — Tue, 06 Feb 2024 04:53:49 +0000

“What I love about working at Soroco is the blend of startup mentality with the resources and focus to accomplish big things. Our product offering solves problems for some of the most impactful companies in the world, yet we are still nimble enough to “skate to where the puck is going”. We are solving a problem that will improve every digital workforce in the world!” Cliff Roscow, Regional Sales Director Cliff Roscow, Regional Sales Director

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Hear from people-Shreyas

Soroco Admin — Tue, 06 Feb 2024 04:51:49 +0000

“I joined Soroco as an intern and have grown through the ranks over the last 7 years. I have been mostly involved with product, design, and engineering, while ensuring that our customers find our product valuable. Seeing the product grow from a 2-person project, to now being the face of our company has been an exhilarating ride!” Shreyas Karanth, Senior Product Manager Shreyas Karanth, Senior Product Manager

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FMCG company use case solved by soroco

Soroco Admin — Mon, 05 Feb 2024 09:53:32 +0000

FMCG Scout improved agent productivity and enhanced overall efficiency by 30% for a global beverage producer Email it to me The Challenge A global Fortune 500 beverage manufacturer with 100+ brands, 15+ recent acquisitions faced unexpected operational inefficiencies in order processing, leading to delays, errors, and ultimately, a jeopardized customer experience and lost sales. Despite investments in top-tier CRM and Order Management systems, the order management teams were struggling with high attrition, low morale, and an inability to meet business outcomes, putting the company at risk of significant reputational harm and revenue challenges. Location Global Industry FMCG 100+ Brands 15+ Acquisitions Attempted Solution before Scout Team requested for an additional 1,400 hours for Manual Discovery Workshops The Leadership mandated their internal Digital Transformation team along with their System Integrator to rectify the situation, who undertook a two-pronged approach. They deployed a process mining solution and attempted manual ‘discovery workshops’ to understand team workflows. However, these methods proved time-consuming as they requested 1400 hours from business teams to complete these discovery workshops. These teams were also working out of different locations across the globe, which added to the complexity. This turned out stressful for the already overburdened teams, and ultimately failed to provide the holistic insights needed. Enter In response to the ongoing issues, the Head of Customer Experience and the Head of Automation introduced Scout to find and fix the problem. Scout’s AI model could be quickly put into action across 7 teams in 12 countries, because it didn’t need complicated integration with current systems. The AI analyzed how operations management teams navigated across various applications, mapping out how and why work happens the way it does within the entire order management function. Scout’s AI model could be quickly put into action across 7 teams in 12 countries, because it didn’t need complicated integration with current systems. Scout to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the order management team and connected them with business activities, by analyzing interactions between the team and their software. It then automatically classified these work patterns as either core order management activities or non-core activities. Based on this analysis, within two weeks, Scout’s AI model provided the following insights: 30% time was spent on core Operations Management and CRM applications Contrary to existing assumptions, the analysis revealed that only 30% of the team’s time was spent on core Operations Management and CRM applications. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review Do You Know How Your Teams Get Work Done? Read article 70% time was spent on manual tasks The AI also determined that the remaining 70% of the time was spent in manual tasks like email communications, spreadsheet management, and report creation. This high volume of manual work led to errors and delays, exacerbated by significant variations in order processing across different brand teams. Scout’s AI model then further inferred the reason why the order management team spent so much time on non-core activities – information silos and disconnected systems. These bottlenecks were forcing the team to incessantly toggle between different applications, ultimately resulting in a loss of productivity for all the brand teams. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Step 2: Fix Based on the above insights, Scout’s AI model recommended two levels of fixes: Quick Fixes These were ‘no-code’ fixes based on standardization and user training Provide focused training on existing Order Management – CRM system to improve adoption Provide focused training on existing Order Management – CRM system to improve adoption Leadership realized that order management teams and customers were accustomed to the business-as-usual ways of working over emails. Thus, they initiated a targeted awareness campaign for the OM – CRM system, aimed at familiarizing customers and order management teams with the new features and benefits of the system. This effort helped increase system adoption and reduce bespoke manual activities. Improve self-service capabilities for customers: Improve self-service capabilities for customers: Post the awareness campaign, management’s goal was to empower customers with more control over their orders. To achieve this, customers were provided with tools and training to place orders, make changes to orders, track the status of their orders, and have real time visibility into the order process. Standardization of management reporting: Standardization of management reporting: Leadership realized that they were part of the problem by insisting on several customized reports. They recognized that the customized reports required time investment and decided to standardize and automate them, allowing them to focus more on upselling and cross-selling activities Deep Fixes Systemic and long-term fixes planned across the organization Scout also provided detailed insights into the cost of unintegrated order management systems and, also the disconnection debt in the organisation. Based on these insights the leadership kicked off a digital transformation program to Create an end-to-end workflow system integrating information inputs, information validation, business process rules, transaction processing & reporting, and compliance. Study variations across how teams worked, standardized, and optimized processes. Automate various functions like time-consuming manual validations in data maintenance activities such as contract activation, inactivation, and rebate settlement across different automation technologies like IDP, RPA, chatbots, etc. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff The strategic implementation of Scout’s recommendations led to: 25% Reduction in Delays and Errors: In order processing. 30% Improvement in Agent Productivity: Enhancing overall efficiency. 10% Improvement in Cash Flows: Reflecting… Continue reading FMCG company use case solved by soroco

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Using Artificial Intelligence to Illuminate the “Dark Side of the Moon”

Soroco Admin — Mon, 05 Feb 2024 08:27:24 +0000

Analyst Report Using Artificial Intelligence to Illuminate the “Dark Side of the Moon” In this exclusive report, HFS Research explains how Soroco’s flagship product, the Scout AI model, interprets human-machine interaction data within companies to reveal what often remains unseen —the hidden challenges enterprise teams face at work and how these affect business outcomes. Scout’s AI then provides data-based recommendations to address these challenges, paving the way for unprecedented business outcomes. This is called illuminating the “dark side of the moon”. What to expect in this paper: Discover the “Dark Side of the Moon” in Enterprises: Uncover hidden challenges affecting your team and business outcomes. Stay Ahead with AI-Powered Process Intelligence: Elevate your understanding of process evolution driven by AI. Maximize Business Outcomes: Learn how AI seamlessly connects interaction data with macro-objectives—cost optimization, revenue realization, and enhanced customer experiences. Who should dive in: Process Intelligence and Automation Leaders CTOs, CIOs, Heads of Transformation, and AI Leaders Operations Heads and Line-of-Business Leaders Meet the authors: Phil Fersht CEO and Chief Analyst HFS Research Reetika Fleming Executive Research Leader HFS Research Hridika Biswas Senior Analyst HFS Research See Scout in action. Schedule your demo now! Get in Touch

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Improve Customer Experience

Soroco Admin — Thu, 18 Jan 2024 12:08:30 +0000

Improve customer experience by alleviating operational bottlenecks

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Identify STP bottlenecks

Soroco Admin — Thu, 18 Jan 2024 12:07:31 +0000

Solve for what is preventing STP in your org

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Onboard developers quickly

Soroco Admin — Thu, 18 Jan 2024 12:06:59 +0000

Open source material and documented API to quickly onboard developers

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Bring your favorite language

Soroco Admin — Thu, 18 Jan 2024 12:06:32 +0000

Support for Python, Rust, and pretty much anything else!

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Integrated with BI tools

Soroco Admin — Thu, 18 Jan 2024 12:05:39 +0000

Use PowerBI and Qlik to build new apps on the work graph.

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Streamline app migrations

Soroco Admin — Thu, 18 Jan 2024 12:05:00 +0000

Baseline app use, before migration.

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Optimize Tech and Data

Soroco Admin — Thu, 18 Jan 2024 12:04:29 +0000

Reduce costs of stale data and technology.

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Disconnection Debt

Soroco Admin — Thu, 18 Jan 2024 12:03:33 +0000

Discover how un-integrated systems hurt your business outcomes.

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Interrogate the work graph

Soroco Admin — Thu, 18 Jan 2024 12:02:12 +0000

Interactive interface to discover what is hurting your teams and business.

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Connect to Business Objectives

Soroco Admin — Thu, 18 Jan 2024 06:01:07 +0000

Find up to 30% optimization opportunities and see how they map to teams' KPIs and business objectives.

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Insurance company use case solved by soroco

Soroco Admin — Wed, 10 Jan 2024 11:24:27 +0000

Insurance Scout enhanced policy booking times and improved underwriter productivity with a 30% increase in straight through processing Email it to me The Challenge A prominent Fortune 500 insurance company, with over 34,000 employees, was facing critical challenges in its underwriting functions. The issues included increased workload leading to errors and miscalculations, high customer dissatisfaction due to missed SLAs, and looming reputation risks that threatened significant revenue loss. Industry Insurance 34,000 Employees Attempted Solution before Scout The manual discovery process resulted in up to 40% miscalculations The company established an internal task force to identify automation and standardization opportunities. However, their manual discovery process, limited by a small sample size, failed to capture all workflows accurately, resulting in up to 40% miscalculations. Enter The EVP and Head of Operations introduced Scout to find and fix these challenges, and to gain a comprehensive understanding of the on-ground realities. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how underwriting teams interact with various applications, including the core workbench underwriting application, mapping out how and why work happens the way it does within the underwriting team. The AI decoded the work patterns by analyzing interactions between the underwriting team and their core workbench application Scout to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the underwriting team and connected them to business activities, by analyzing interactions between the underwriting team and their core workbench application. It then automatically classified these work patterns as either core underwriting activities or non-core activities. Based on this analysis, within two weeks, Scout’s AI model provided the following insights: 30% of the team’s effort was spent on core underwriting workbench application and activities. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review Do You Know How Your Teams Get Work Done? Read article Scout’s AI model then further inferred the reason why the underwriting team spent so much time on non-core activities – information silos and disconnected systems. These bottlenecks were forcing the team to incessantly toggle between 50+ applications and 100+ spreadsheets, ultimately resulting in a loss of productivity and context. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Step 2: Fix Based on the above insights, Scout’s AI model recommended two levels of fixes: Quick Fixes These were ‘no-code’ fixes based on standardization and user training Train the entire team to write instructions clearly using a defined template The first initiative was to train the 100+ Underwriters on writing instructions clearly. This helped save ~15% of the team’s bandwidth which was blocked in Outlook and Teams conversations and ~500 application/context switches per team member per hour. Creation of a document repository for underwriting calculators and templates: There was an initiative to create a repository for all documents regarding underwriting rules, underwriting calculators and templates. This led to an immediate improvement in underwriting quality and helped free up approximately 7% of the bandwidth. Within the first 4 weeks of implementing the above, the underwriting team gained 22% more time to focus on their core activity and on building robust relationships with agents, brokers, and customers. Deep Fixes Systemic and long-term fixes planned across the organization Design an end-to-end workflow system: Scout also provided detailed insights into the cost of unintegrated underwriting systems & applications and, also the disconnection debt in the organization. With these insights and aiming to unify the experience across multiple applications and documents, the leadership kicked off a digital transformation initiative to create an end-to-end workflow system integrating information inputs, information validation, business process rules, transaction processing and reporting and compliance. This improved productivity by 30% Deploy an Intelligent Underwriting Assistant: The leadership also deployed an intelligent internal chatbot to assist underwriters, underwriting assistants and processors. This had integrations available with training repositories, policy specific rules and regulations as well as the capability to automatically trigger policy actions based on the roles accessing the system. This helped improve underwriting quality, freed teams’ bandwidth by 20% and brought down the time to book a policy by 30%. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout was able to drive the following business outcomes Significantly improve how employees experience work and improve their morale 33% Reduction in Time to Book a New Policy 30% Improvement in Underwriter Productivity $3.75M Estimated Annual Savings How AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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Construction company use case solved by soroco

Soroco Admin — Mon, 08 Jan 2024 11:45:49 +0000

Construction Scout transformed payroll operations for a F500 construction company and reduced employee attrition by 26% Email it to me The Challenge A leading construction company in Europe was grappling with severe payroll operational issues impacting the morale of employees. Delayed salaries and erroneous transfers across subsidiaries impacted over 6000 staff members across 4 subsidiaries. The already overworked payroll processing team was experiencing increased stress each day, leading to attrition within the team, and creating reputational risk for the organization. Location Europe Industry Construction 4 Subsidiaries 6000+ Construction Attempted Solution before Scout Consultants requested for an additional 1,200 hours for Manual Discovery Workshops The company engaged one of the top consulting companies to overcome the operational shortcoming. The consulting firm went about this in a traditional manner, conducting discovery workshops through manual interviews. These workshops demanded an additional 1200 hours from the already overworked business teams, leading to attrition. This manual approach didn’t add any real value and intensified the problem. Enter The CFO and CHRO then evaluated and onboarded Scout to find and fix these problems. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how payroll teams interact with various applications, mapping out how and why work happens the way it does within the payroll team. This led Scout to discover a key insight: the root of the problem was outside the payroll team. Within just 2 weeks Scout’s AI model uncovered a critical insight – the problem lay outside the payroll team Scout to “find and fix” Step 1: Find As the first step, the AI decoded the work patterns of the payroll team and connected them to business activities, simply by analyzing interactions between the team and their software. It then automatically classified these work patterns as either core payroll activities or non-core activities. Based on this analysis, within two weeks, Scout’s AI model provided the following insights: 40% time was spent on core payroll apps and activities Contrary to popular belief, only 40% of the team’s effort was spent on core payroll apps and activities. This startling statistic pointed to a massive work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done. Harvard Business Review Do You Know How Your Teams Get Work Done? Read article 60% time was spent on manual follow-ups The AI also determined that the remaining 60% of the time was spent on manual follow-ups, managing data across 1500+ spreadsheets, and creating custom reports for management. Scout’s AI model then further inferred the reason why the payroll team spent so much time on non-core activities – information silos and disconnected systems. These bottlenecks were forcing the team to incessantly toggle between different applications, ultimately resulting in a loss of productivity to the tune of 3300 hours annually. Harvard Business Review How Much Time Does Having Too Many Apps Really Waste? Read article Step 2: Fix Based on the above insights, Scout’s AI model recommended two levels of fixes: Quick Fixes These were ‘no-code’ fixes based on standardization and user training Submit timesheets on time Submit timesheets on time Leadership kicked off a focused awareness and training program for everyone across the organization to ensure that people submitted their timesheets on time.This led to 100% timely submission of timesheets which significantly reduced the stress on the payroll team’s bandwidth, allowing them to focus on core payroll activities. Standardize data inputs Standardize data inputs The leadership enforced standardization of data inputs, which reduced the effort and time spent on organizing and validating the input data received by payroll teams. Standardize management reporting Standardize management reporting Leadership also made a concerted effort to standardize management reporting which resulted in a reduction of time spent on generating customized reports. Deep Fixes Systemic and long-term fixes planned across the organization Build an integrated Payroll system Build an integrated Payroll system The root cause of all issues was that there were significant digital gaps in the overall process; and these were predominantly because systems did not talk to each other, and because data was duplicated/fragmented. Scout also provided detailed insights into the cost of unintegrated payroll systems and also, the disconnection debt in the organization. Based on these insights, the leadership kicked off a transformation program, to build an end-to-end workflow system by integrating information inputs, business process rules, and reporting & compliance. This led to a 95% reduction in digital gaps, 100% payroll accuracy and a 26% reduction in employee attrition in the payroll team. Harvard Business Review What’s Lost When Data Systems Don’t Communicate Read article It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e. Getting teams to work together across functions Fixing disconnected systems Simplifying and optimizing work processes Strategic Payoff In summary, Scout was able to drive the following business outcomes Significantly improve how employees experience work and improve their morale 95% reduction in digital gaps 100% improvement in payroll accuracy 26% reduction in employee attrition How AI connects interaction data to business outcomes Scout lights up the ‘dark side of the moon’. Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it’s cost optimization, revenue growth, customer or employee experience, or business continuity. The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes. Forbes The ‘Dark Side Of The Moon’ In Enterprises Read more Download this customer success story Enter Business Email ID

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Privacy Compliance with a PII Dashboard

Soroco Admin — Mon, 08 Jan 2024 10:32:16 +0000

Provides a single pane continuous view to privacy filter outcomes, ensuring data integrity and confidentiality.

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Enhanced Security Protocols through Self Service PII policies

Soroco Admin — Mon, 08 Jan 2024 10:31:47 +0000

Flexible PII configuration capability, to have your data encrypted, stored, and processed securely.

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Process Modelling

Soroco Admin — Mon, 08 Jan 2024 10:30:12 +0000

Choose from up to 5 different actions to model your work according to your business needs.

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Take action

Soroco Admin — Mon, 08 Jan 2024 10:29:22 +0000

Halve the time to act - generate data-backed business documents, and summaries to get you started on implementing recommendations.

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Opportunity Tracking

Soroco Admin — Tue, 10 Oct 2023 06:43:18 +0000

Monitor your optimization opportunities and make decisions to improve the business objectives.

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Get incrementally better everyday

Soroco Admin — Tue, 10 Oct 2023 06:42:55 +0000

Track the impact of your interventions, including suggested 10+ AI/automation opportunities discovered by Scout.

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Identify where AI / Automation can transform your business outcomes.

Soroco Admin — Tue, 10 Oct 2023 06:20:03 +0000

Get 10+ recommendations for deploying automation / AI capabilities, to improve business outcomes.

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Simulate success, within your context

Soroco Admin — Tue, 10 Oct 2023 06:19:26 +0000

Double the success rate of your transformation - curate and forecast a change journey tailored to your priorities.

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Birds’ eye to worms’ eye

Soroco Admin — Tue, 10 Oct 2023 06:15:19 +0000

Gain visibility into teams' work patterns, for everybody - from a CXO to an associate.

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Discover Bottlenecks

Soroco Admin — Tue, 10 Oct 2023 05:39:48 +0000

Unearth 20+ bottlenecks hurting your teams' work.

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Hear from people-Era

Soroco Admin — Mon, 25 Sep 2023 05:53:19 +0000

"I joined Soroco as their first non-engineer team member. I wore multiple hats not only to own the role I was in, but also to enforce the culture, to train newcomers, to scale existing operations, guide the product roadmap and to build great customer success stories.

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Hear from people-Wolf

Soroco Admin — Mon, 25 Sep 2023 05:48:01 +0000

"I have had the privilege of watching Soroco grow from 4 people to about 400 now. Along this journey I have watched our technology, our people, our offerings and our processes evolve into a sustainable and scalable business.

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home page customer stories – 1

Soroco Admin — Fri, 22 Sep 2023 06:51:17 +0000

Construction Scout transformed payroll operations for a F500 construction company and reduced employee attrition by 26% Read more Insurance Scout reduced time to book new policies and increased underwriter productivity with higher straight through processing by Read more FMCG Scout improved agent productivity and enhanced overall efficiency by Read more

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Soroco named Star performer in Everest Group’s PEAK Matrix® for Task Mining Technology Provider 2023 and a leader for the second consecutive year.

Soroco Admin — Thu, 21 Sep 2023 09:07:19 +0000

Analyst Report The evaluation was based on specific criteria that analyzed the company’s flagship product, Scout along with 19 other vendors for their market impact, vision, and capability. The evaluation was based on specific criteria that analyzed the company’s flagship product, Scout along with 19 other vendors for their market impact, vision, and capability. Soroco leads the category because of these reasons as outlined in the report. Innovative Process Understanding and Optimization Soroco’s Scout AI model revolutionizes how enterprises discover and optimize processes. By analysing user interactions and highlighting inefficiencies, it identifies bottlenecks and maximizes employee potential. Advanced Machine Learning and Analytical Capabilities Leveraging both supervised and unsupervised learning techniques, Soroco’s transformer-based ML model classifies team interactions and annotates process maps with business context. Strategic Partnerships Soroco has built collaborations and partnerships with leading players in the industry, to accelerate digital adoption and employee training. This has led to increase in market share plus deeper engagements with existing clients. “Soroco has reinforced its position as a Leader for the second consecutive year on Everest Group’s Task Mining Products PEAK Matrix® 2023, owing to its strong vision, depth & breadth of product capabilities, and continuous focus on product innovation and thought leadership. It also emerged as a Star Performer due to a strong year-over-year growth in its market impact and capabilities.” Product vision & roadmap, ability to identify automation and process enhancement opportunities, and data protection & privacy are some of the key strengths indicated by its clients.” Amardeep Modi, Everest Group Amardeep Modi, Vice President at Everest Group See Scout in action. Schedule your demo now! Get in Touch

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Soroco listed as a leader in NelsonHall’s 2023 Process Understanding NEAT

Soroco Admin — Tue, 19 Sep 2023 08:54:02 +0000

Analyst Report Soroco listed as a leader in NelsonHall’s 2023 Process Understanding NEAT Download the NelsonHall 2023 Process Understanding report: Discover why Scout® is a strategic imperative for Enterprises Explore the Process Understanding market outlook, size, and growth Learn about the key players in the sector and their points of differentiation Soroco was positioned as a Leader in NelsonHall’s 2023 Process Understanding NEAT evaluation in the Task Mining market segment due to its ability to create work graphs from human–computer interactions to support process transformations; this goes beyond simple RPA and into workflow automation, IDP, email templatization, and conversational AI. Soroco’s platform also remains one of the few task mining platforms to support the ingestion of process mining data. Mike Smart Senior Analyst and Operations Officer, NelsonHall Soroco leads the category because of these reasons as outlined in the report. Work Graphs: Empowering Process Transformation Beyond RPA: Soroco’s core strengths are its ability to create work graphs from human–computer interactions to support process transformations beyond simple RPA and into workflow automation, IDP, email templatization, and conversational AI. Scout Platform: Streamlining Process Transformation for Executives and Developers: Soroco has developed its Scout platform to present these ideas for process transformations in a form that is easily digestible for executives within the client organization and for these ideas to enable developers using these digital technologies to expedite transformation. See Scout in action. Schedule your demo now! Get in Touch

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George Nychis

Soroco Admin — Fri, 15 Sep 2023 08:21:07 +0000

A distributed and networked systems enthusiast, George loves solving challenging problems. He also happens to be a semi-professional Greek dancer who has graced the stage at events across the U.S.

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Rohan Narayana Murty

Soroco Admin — Fri, 15 Sep 2023 06:26:08 +0000

A strong believer in transformative technology, Rohan founded Soroco in 2014. He has served as a Junior Fellow in the Society of Fellows at Harvard and is known for his love of ancient Indian literature.

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