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Tushar Mudgal reposted thisTushar Mudgal reposted thisClassroom to Startup Movement Today marks a defining milestone in our journey of transforming education into innovation. I am proud to officially declare that 39 startups have successfully emerged and are growing under the umbrella of Bharati Vidyapeeth's College of Engineering - New Delhi — a testament to the power of ideas nurtured within classrooms. These ventures are not just startups; they are outcomes of a vision — the “Classroom to Startup Movement” — where learning goes beyond theory, and students evolve into creators, innovators, and job providers. What started as a mission to bridge the gap between academia and real-world application has now become a thriving ecosystem of entrepreneurship. Each of these 39 startups represents courage, creativity, and the spirit of building something meaningful from scratch. This is just the beginning. Our goal is not only to create startups but to build a culture where every student believes: 👉 “My classroom can be my launchpad.” Gratitude to all mentors, faculty members, industry partners, and most importantly, our student founders who dared to dream and act. A special thanks to Dhruvi Jain who designed this starup wheel. A special thanks to Subhash Malik and Creator of the 'Malik Transformation Model' to make this journey successful and Dr. Dharmender Saini to provide us all kind of support. The future belongs to those who build. #ClassroomToStartup #BVCOE #StartupIndia #Innovation #Entrepreneurship #StudentStartups #MakeInIndia
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Tushar Mudgal reposted thisGreat conversations at the CTO & Tech Leaders Forum hosted by Crosslake Technologies and Gresham House Ventures at AWS HQ (expertly hosted by Brandon Maddick &Rohit Mathur) — a timely discussion on navigating the AI-driven growth curve. Our co-founder and CEO Apoorva Kumar and co-founder and COO Cyril Treacy presented how Disseqt AI is building — an OS for agentic governance focused on testing and contextual simulation to make agents production-ready. Great to see such strong conversations emerging around the AI-driven growth curve — especially with CTOs and tech leaders who are now grappling with a very real question: How do we take agentic AI from demos to dependable production systems? At Disseqt AI, we strongly believe that the last mile to production is not model capability — it’s assurance, testing, and governance. As autonomous agents become more prevalent, enterprises need confidence that these systems behave predictably, safely, and in alignment with business and regulatory intent. What resonated deeply in this forum: 1. Agentic AI needs contextual simulation and rigorous testing, not just monitoring. 2. Governance must be designed into the OS layer, not bolted on post-deployment. 3. Responsible scale is possible only when observability, validation, and control move upstream — before production. Great to see ecosystems coming together — cloud platforms, operators, and governance-first builders — to push AI forward responsibly. Manish Atri Anurag Ranjan Tushar Mudgal Dr. Shiva Prasad .S Akanksha Patel Abhishek Gupta Sharad Verma Yash Kedia Shubham Patel Tushar Dogra Rhea Kumar Zoya Rai Ruth Millar #AgenticAI #RAIOps #EnterpriseAI #ResponsibleAI #AgenticGovernance #DisseqtAI #OS4AgenticGovernance #MLOpsTushar Mudgal reposted thisGreat event in AWS HQ yesterday hosted by Crosslake Technologies and Gresham House Ventures CTO & Tech Leaders Forum Navigating the AI-driven growth curve Expertly hosted by Brandon Maddick & Rohit Mathur. Myself and Apoorva Kumar presented what we are building at Disseqt AI - an OS for Agentic Governance focussing on testing and contextual simulation to make agents production ready. Great conversations afterwards with attendees on the need for assurance and governance as the last mile for production deployments in enterprises . We also heard great insights from Amit Lulla on how AWS new AI capabilities can help AI Trailblazers scale responsibly . #agenticAI #RAIOps #MLOps #OS4Agenticgovernance #disseqtai
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Tushar Mudgal reposted thisTushar Mudgal reposted thisWhat’s the hardest AI incident you’ve ever debugged? Not a demo fail. Not a bad prompt. A real production issue where: Users were impacted Metrics looked “fine” But something was off! Maybe it was: A silent model update A RAG pipeline pulling the wrong docs A guardrail that failed only on edge cases An agent slowly going rogue AI rarely fails loudly. It fails politely. And that’s the problem. If you’ve shipped LLMs, copilots, or agents to prod, you’ve seen this. Drop your war story 👇 What broke and how did you finally catch it? Apoorva Kumar Manish Atri Cyril Treacy Anurag Ranjan Tushar Mudgal Dr. Shiva Prasad .S Akanksha Patel Abhishek Gupta Sharad Verma Yash Kedia Shubham Patel Tushar Dogra Rhea Kumar Zoya Rai Ruth Millar #ResponsibleAI #AIEngineering #AIObservability #MLSystems #AIDebugging #AIReliability #DisseqtAI #BuildAI
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Tushar Mudgal reposted thisTushar Mudgal reposted thisHad a great in-person discussion at the InfosysMysore campus with Ravi Joshi, Jagadish Babu and the team. I was joined online by our co-founder & CEO Apoorva Kumar. We spent time exchanging perspectives on how enterprises are approaching Responsible and Agentic AI, and the practical challenges around testing, validation, security, and governance as these systems move from experimentation to production. We walked through Disseqt AI ’s Lean Agentic Enterprise assurance approach and discussed potential synergies with Infosys’ RAI initiatives—especially around scalable validation, red-teaming, and production monitoring for GenAI and agentic workflows across enterprise use cases. Encouraging conversations and clear next steps ahead. Looking forward to continuing the collaboration and exploring how we can jointly help enterprises operationalize AI with reliability, safety, and speed. Manish Atri Cyril Treacy Tushar Mudgal Dr. Shiva Prasad .S Abhishek Gupta Sharad Verma Yash Kedia Akanksha Patel Shubham Patel Tushar Dogra Rhea Kumar Ruth Millar Zoya Rai #AIGovernance #RAIOps #ResponsibleAI #AITrust #EnterpriseAI #GenAIGovernance #AgenticAI #AIValidation #AIAssurance
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Tushar Mudgal reposted thisGrateful to CIOandLeader for spotlighting a truth many enterprises are now confronting. Enterprise AI isn’t stalling because of weak models—it’s stalling because human-led operations don’t scale. Agentic AI changes the math: AI now runs AI. At Disseqt AI, we’re seeing how Agentic-AI Ops transforms AI from a cost center into a compounding asset—cutting operational overhead while unlocking real productivity gains. Thank you CIOandLeader for driving this important conversation. Manish Atri Cyril Treacy Anurag Ranjan Tushar Mudgal Zoya Rai Mini Gautam Abhishek Gupta Sharad Verma Yash Kedia Tushar Dogra Shubham Patel Ruth Millar Dr. Shiva Prasad .S Shubam Sharma Akanksha Patel Rhea Kumar #AgenticAIops #DisseqtAI #CIOandLeaderTushar Mudgal reposted this“𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 𝐚𝐫𝐞 𝐰𝐞𝐚𝐤. 𝐈𝐭’𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐡𝐮𝐦𝐚𝐧𝐬 𝐜𝐚𝐧’𝐭 𝐬𝐜𝐚𝐥𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬.” “Agentic AI changes the math—AI now runs AI.” — Apoorva Kumar, Co-founder & CEO, Disseqt AI Despite massive AI investment, 74% of enterprises still struggle to scale real value. The blocker isn’t innovation—it’s operations. Agentic-AI Ops breaks the linear cost trap by replacing human-led monitoring, QA, incident response, and compliance with autonomous AI agents. Early adopters are already reporting up to 70% lower operational costs and nearly 80% higher productivity—without expanding IT or governance teams. This is the shift from AI as an expense to AI as a compounding asset. Read more: https://lnkd.in/g7aPuWS8 #AgenticAI #EnterpriseAI #AIOps #CIOAgenda #DigitalTransformation #AIAdoption #CIOLeader Vikas Gupta | Sachin Mhashilkar | Vandana Chauhan | Giridhar Rajagopalan | Jatinder S. | Hafeez Shaikh | Rajiv Pathak | Shokeen Saifi | Ekta Srivastav | Sourabh Dixit | Subhadeep Sen | Jagrati Rakheja | Musharrat Shahin | Aanchal G.
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Tushar Mudgal reposted thisTushar Mudgal reposted thisWe’re Hiring: Senior Platform & SRE Engineer Location: Bengaluru (Hybrid) | Full-time At Disseqt AI, we’re building an enterprise-grade platform that helps IT and DevOps teams test, monitor, audit, and make real-time decisions across modern AI apps and infrastructure. We’re looking for a Senior Platform & SRE Engineer who can own and scale the infrastructure powering all our AI services across cloud, hybrid, and fully on-prem environments. What You’ll Work On * Architect end-to-end infrastructure for cloud and on-prem customers * Build scalable deployments for AI/ML and microservice workloads * Own SRE responsibilities: uptime, SLOs, incident response, postmortems * Build CI/CD, GitOps, and automated release pipelines * Implement observability stacks (OpenTelemetry, Prometheus, Grafana) * Design secure, compliant infrastructure: IAM, secrets, isolation * Create reusable Terraform, Helm, and Ansible modules * Collaborate with backend and ML teams on platform-level decisions What You Bring * 5+ years in Infrastructure, SRE, or DevOps roles * Strong Kubernetes, Terraform, and Helm experience * Deep experience with on-prem setups (VMs, networking, firewalls) * Strong Linux fundamentals, networking, Docker internals * Experience running distributed systems in production * Expertise in observability tools and operational debugging Bonus: * Experience with AI/ML workloads and model serving * Familiarity with open-source LLMs and CPU-based inference * Experience with air-gapped/on-prem enterprise deployments * Security, compliance, or SOC2 experience * Performance engineering or load testing background Why Join Us This is one of the most critical roles at Disseqt AI. You will shape: * Our cloud and on-prem enterprise architecture * Product reliability, uptime, and SLAs * Customer deployment experience * Developer velocity and overall platform scalability If you're excited about building foundational infrastructure for mission-critical AI systems, we’d love to connect. Apply here: https://lnkd.in/gfT8VdSpSenior Platform & SRE Engineer (On-Prem & AI Systems) at Disseqt AI LIMITEDSenior Platform & SRE Engineer (On-Prem & AI Systems) at Disseqt AI LIMITED
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Tushar Mudgal reposted thisTushar Mudgal reposted thisDisseqt AI conducted an immersive, hands-on bootcamp session with the HCLTech team In Noida, diving deep into Responsible AI, LLM validation, red-teaming frameworks, and our Lean Agentic Enterprise approach. Apoorva Kumar, our founder & CEO and Tushar Mudgal, our Founding Member & Engineering Lead were there in person for the sessions. We also had the opportunity to meet and interact with some of HCLTech’s key leaders across engineering, delivery, and architecture. Their clarity of thought, focus on real enterprise-scale challenges, and openness to co-creating AI solutions made the discussions even more impactful. A big thank you to the HCLTech leadership and engineering teams for the enthusiasm and collaboration. Looking forward to exploring joint GTM motions and customer use cases. Manish Atri Cyril Treacy Dr. Shiva Prasad .S Abhishek Gupta Sharad Verma Yash Kedia Shubham Patel Rhea Kumar Zoya Rai Ruth Millar #DisseqtAI #HCLTech #ResponsibleAI #AgenticAI #EnterpriseAI #GenAI #Bootcamp
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Tushar Mudgal reposted thisTushar Mudgal reposted this🚀 We’re Hiring: Senior Software Engineer (SD3/SD4) At Disseqt AI we’re building an enterprise-grade platform that helps IT and DevOps teams test, monitor, audit, and make real-time decisions across modern AI apps and infrastructure. We’re looking for a Senior Software Engineer who can take ownership of backend architecture and help us build the systems that drive our platform. You’ll design scalable services, build reliable distributed systems, and shape core engineering decisions as we grow. What we’re looking for: -> Strong backend experience (Go/Python/Node/Java /) -> Deep understanding of distributed systems & microservices -> Solid database design fundamentals -> Experience with cloud infrastructure & production-grade deployments -> A passion for building clean, maintainable, high-impact systems If you’re excited about shaping foundational engineering at an early-stage AI startup, we’d love to meet you. 📩 Apply via Dover: 👉 https://lnkd.in/gV8GJyrm #hiring #softwareengineering #backendengineer #distributedSystems #microservices #cloudengineering #devops #ai #startupjobs #careers #joinusSenior Software Engineer (SD3 / Senior Engineer) at Disseqt AI LIMITEDSenior Software Engineer (SD3 / Senior Engineer) at Disseqt AI LIMITED
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Tushar Mudgal reposted thisTushar Mudgal reposted this🚀 We’re Hiring: Senior Frontend Engineer (SD3/SD4) Disseqt AI is building enterprise-grade agentic AI for IT & DevOps teams powering smarter testing, monitoring, auditing, and decision-making. We’re looking for a Senior Frontend Engineer to lead our frontend architecture, drive design systems, and build high-quality, scalable interfaces for our AI platform. If you have deep experience in React, Next.js, TypeScript, performance optimization, and modern frontend tech, we’d love to meet you. 📩 Apply via Dover: 👉https://lnkd.in/g-jY_sDJ Let’s build responsibly. Join Disseqt.ai #hiring #frontendjobs #frontenddeveloper #reactjs #nextjs #typescript #softwareengineering #engineerjobs #productengineering #ai #startupjobs #techcareers #joinus
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Tushar Mudgal liked thisTushar Mudgal liked thisLet's be Honest. In the ai era where we are shipping a thousands of lines of code a day or even in hour. We literally don't understand what and how many bugs we are shipping with features. After a long feature planning when we code with AI and ship it . We have little less of knowledge of the edge cases we have missed . That is what happening with any vibe coded app build with any vibe coding platform not just IDE's . On IDE you have level of control but the apps which asks you to give prompt and they will code everything behalf of you is real concern for the security to business logic and as well as user data. To be honest , I have myself created 100+ apps with vibe code platforms to make my web apps and deploy them and also have created 100+ apps with Vibe coding IDE's . But when you are developer you have all the knowledge of how the apis should work, what should not be exposed , what we might not think is exposed but is actually exposed which a developer can easily find. For example : You created app showed the content but the content coming from api and you are just displaying on the UI. Some fields are missing which will only be visible if some condition is met . Let's say those fields are in paid subscription. "But what if I tell you they might be visible without even the subscription." All thanks to Data coming from API's without being validated properly. This the thing that a normal user might not notice. But even if some curios one ask to chatgpt or cluade can I get some data without even buying subcription they can get these hacks. And of course the millions of developers knows all this and you will be loosing money worth millions . This problem I have seen a lot . And even if you are not developer or just a builder who want to not deep dive in the tech, you should know about these types of flaws or bugs which can happen and damage your business.
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Tushar Mudgal reacted on thisTushar Mudgal reacted on thisHad a wonderful time visiting Disseqt AI’s partners at Harbinger Group in their Pune office! It was energizing to connect in person with their incredibly talented cross-functional teams and experience the culture and energy they've built. A big thank you to the leadership and Shrikant Pattathil for the warm hospitality and for making us feel right at home. Many thanks to Aparna Joshi for such a nicely organized day and for driving the agenda across functions. The conversations were rich, the strategy sessions were insightful, and we walked away with a sharper, more aligned vision for what we're building together. Partnerships like these — grounded in trust, shared goals, and open dialogue — are what truly move the needle. Excited for what's ahead! 🚀 Apoorva Kumar Manish Atri Cyril Treacy Subodh Bhide Umesh Kanade Vicky Thorat #Partnerships #Strategy #HarbingerGroup #Collaboration #Disseqt # RAIOps
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Tushar Mudgal reacted on thisTushar Mudgal reacted on thisMy latest thoughts on some of the challenges enterprises face in adopting AI post Easter 2026.... Disseqt AI Apoorva Kumar Manish Atri #AI #ResponsibleAI #TrustInAI #AIHallucination #DecisionMaking #Leadership #disseqtaiAI is not a thinking business partner, it's a sycophantic sidekick.AI is not a thinking business partner, it's a sycophantic sidekick.Cyril Treacy
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Tushar Mudgal reacted on thisProud moment for us at Disseqt AI. We’re honored to be featured in HCLTech's Responsible AI Transparency Report as a strategic partner alongside Microsoft and OpenAI. This is a strong validation of the category we’re building: AI governance that actually works in production environments. At Disseqt, our belief is simple—trust in AI isn’t a principle, it’s a system. It has to be engineered, enforced, and continuously evaluated. That’s exactly where we focus: 🚀 Governance embedded by design across data, models, and infrastructure 📊 Real-time policy evaluation and enforcement, not static compliance frameworks 🎯 Event-driven architecture that turns AI activity into actionable controls and alerts 🚀 Enterprise-grade scalability, enabling organizations to operationalize Responsible AI—not just report on it. This is deeply complementary to HCLTech’s Responsible AI strategy—especially their emphasis on scaling governance, aligning with global standards, and translating principles into measurable, enterprise-wide outcomes. Together, this is about moving the industry forward—from AI principles → to AI systems you can trust at scale. Thank you for Heather Domin, PhD and Apoorv Iyer for the partnership and leadership in pushing Responsible AI from theory into execution.The future of AI will belong to companies that can prove trust, not just promise it. Full report here - > https://lnkd.in/g74VGsz2 Manish Atri Cyril Treacy Anurag Ranjan Tushar Mudgal Zoya Rai Abhishek Gupta Sharad Verma Yash Kedia Akanksha Patel Tushar Dogra Sumit Das Shubam Sharma Nischay Mehta Shubham Patel Ruth Millar Rhea Kumar Mini Gautam #ResponsibleAI #AIGovernance #TrustInAI #EnterpriseAI #RAIOpsTushar Mudgal reacted on this🚨 Trust is what will define the future of AI. Our Responsible AI Transparency Report outlines how we design and deploy AI that is ethical, secure and ready for enterprise scale. Key insights: ✅ Scaling governance by design ✅ Aligning to global standards ✅ Strengthening AI red-teaming ✅ Driving responsible change ✅ Building AI literacy worldwide Explore how organizations can build AI that delivers real business value while strengthening trust in this report. 🔗 Read it here: https://lnkd.in/g74VGsz2 #AIthatDeliversROI #ResponsibleAI #AIGovernance
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Tushar Mudgal liked thisThank you ManishTushar Mudgal liked thisA recent MIT study just put a name to something many of us building with LLMs have felt but couldn't fully articulate. Researchers at MIT confirmed that large language models sometimes learn the wrong lessons entirely. Not hallucinations. Not bias. Something more subtle — and arguably more dangerous. LLMs can respond to questions by pattern-matching sentence structure rather than actually understanding what's being asked. Give a model a grammatically familiar but completely nonsensical question, and it will still confidently give you the "right" answer — because the shape of the sentence felt familiar, not because it reasoned through it. Researchers said it plainly: LLMs are fragile in ways that aren't obvious until something breaks in production — and end-users have no reason to expect this. This is why I keep coming back to two non-negotiables when building LLM-powered products: 🔬 Testing LLM-based applications and agents is not a nice-to-have. It is quintessential. You cannot assume a model that passed your evals yesterday will reason correctly across every input variation it encounters tomorrow in the wild. ✅ And testing alone is still not enough. Every single output an LLM generates must be validated before it is shown to an end customer — every time, no exceptions. A response, a summary, a recommendation, an agent action — unvalidated output is not a feature. It is a liability waiting to surface. In traditional software, a bug causes an error. In LLM-powered products, an unvalidated output can mislead a patient, misguide a financial decision, or silently bypass a safety system. The models are impressive. The discipline to deploy them responsibly is what separates products that scale from ones that fail publicly. Deploying LLM apps to production? Let's talk. 🤝 We help teams test, validate and ship LLM-powered applications with confidence — before your customers ever see an output. 👉 Book a demo: www.disseqt.ai/#cal #LLM #AITesting #GenerativeAI #AIAgents #ResponsibleAI #AISafety #MLOps #ProductEngineering #AIReliability Apoorva Kumar Cyril Treacy Anurag Ranjan Ruth Millar Rhea Kumar Tushar Mudgal Sharad Verma Yash Kedia Tushar Dogra Shubham Patel Sumit Das Shubam Sharma Akanksha Patel Dr. Shiva Prasad .S Abhishek Gupta https://lnkd.in/gAFBUuyh
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Tushar Mudgal liked thisTushar Mudgal liked thisHappy to share that I received the Super Crew Award – Q4 FY26 at LTM! 🏆 Grateful for the recognition and thankful to my team for all the support. Looking forward to what's next! 🙂 #LTM #SuperCrewAward #DataEngineering #LTIMindtree
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Tushar Mudgal liked thisTushar Mudgal liked thisA recent MIT study just put a name to something many of us building with LLMs have felt but couldn't fully articulate. Researchers at MIT confirmed that large language models sometimes learn the wrong lessons entirely. Not hallucinations. Not bias. Something more subtle — and arguably more dangerous. LLMs can respond to questions by pattern-matching sentence structure rather than actually understanding what's being asked. Give a model a grammatically familiar but completely nonsensical question, and it will still confidently give you the "right" answer — because the shape of the sentence felt familiar, not because it reasoned through it. Researchers said it plainly: LLMs are fragile in ways that aren't obvious until something breaks in production — and end-users have no reason to expect this. This is why I keep coming back to two non-negotiables when building LLM-powered products: 🔬 Testing LLM-based applications and agents is not a nice-to-have. It is quintessential. You cannot assume a model that passed your evals yesterday will reason correctly across every input variation it encounters tomorrow in the wild. ✅ And testing alone is still not enough. Every single output an LLM generates must be validated before it is shown to an end customer — every time, no exceptions. A response, a summary, a recommendation, an agent action — unvalidated output is not a feature. It is a liability waiting to surface. In traditional software, a bug causes an error. In LLM-powered products, an unvalidated output can mislead a patient, misguide a financial decision, or silently bypass a safety system. The models are impressive. The discipline to deploy them responsibly is what separates products that scale from ones that fail publicly. Deploying LLM apps to production? Let's talk. 🤝 We help teams test, validate and ship LLM-powered applications with confidence — before your customers ever see an output. 👉 Book a demo: www.disseqt.ai/#cal #LLM #AITesting #GenerativeAI #AIAgents #ResponsibleAI #AISafety #MLOps #ProductEngineering #AIReliability Apoorva Kumar Cyril Treacy Anurag Ranjan Ruth Millar Rhea Kumar Tushar Mudgal Sharad Verma Yash Kedia Tushar Dogra Shubham Patel Sumit Das Shubam Sharma Akanksha Patel Dr. Shiva Prasad .S Abhishek Gupta https://lnkd.in/gAFBUuyh
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Tushar Mudgal liked thisBig milestone for us 🚀 Proud to see Disseqt AI featured as a strategic partner alongside global tech leaders on HCLTech’s alliances page. This is a strong validation that Responsible AI governance is no longer optional — it’s becoming core to enterprise AI adoption. More to build. Just getting started. #ResponsibleAI #AIGovernance #DisseqtAI Apoorva Kumar Cyril Treacy Anurag Ranjan Ruth Millar Rhea Kumar Tushar Mudgal Sharad Verma Yash Kedia Tushar Dogra Shubham Patel Sumit Das Shubam Sharma Akanksha Patel Dr. Shiva Prasad .S Abhishek GuptaTushar Mudgal liked thisAt Disseqt AI, our vision has been clear: Responsible AI governance moving from a "nice to have" to a boardroom imperative — and we are to be the platform that will make that transition possible. That conviction is paying off. Disseqt AI is now a listed strategic partner on HCLTech's official alliances page — sitting alongside Microsoft, Salesforce, and some of the most valuable technology companies on the planet. Disseqt AI is enabling enterprise clients with a single governance layer across their entire AI stack, from pre-production testing to live monitoring, so they can move from POC to production with confidence, at speed. The work is just beginning. 🔗 https://lnkd.in/ekCKWXcK Apoorva Kumar Manish Atri Cyril Treacy #ResponsibleAI #AgenticAI #AIGovernance #HCLTech #DisseqtAI #Partnership #StartupLife
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Tushar Mudgal liked thisTushar Mudgal liked thisBharati Vidyapeeth has once again reaffirmed its commitment to promoting academic excellence and supporting meritorious students through its scholarship initiative “Dr. Patangrao Kadam Scholarship Scheme”. In a significant step towards empowering higher education, scholarships worth ₹31,90,000 have been awarded to 130 deserving students of BVCOE New Delhi. This initiative reflects the vision of the institution to ensure that financial constraints do not hinder talented students from pursuing quality education. The scholarships have been granted based on merit and academic performance, encouraging students to strive for excellence in their respective fields. The distribution of scholarships was carried out under the guidance of the Bharati Vidyapeeth administration, maintaining transparency and fairness in the selection process. The beneficiaries expressed their gratitude, stating that such financial support plays a crucial role in enabling them to focus on their studies and future aspirations. Bharati Vidyapeeth continues to take proactive steps in nurturing talent and fostering an environment of academic growth, reinforcing its position as a leading educational institution committed to student welfare and holistic development. We extend our heartfelt gratitude to the respected Principal, Dr. Dharmender Saini, Sir and Dr.S B KUMAR , Scholorship Head, for their invaluable support and guidance in making this initiative a success. Dr. Dharmender Saini Dr. Abhishek gandhar Prof. Prakhar Priyadarshi Kirti Gupta Sushil Kumar Deepika Kumar, Ph.D. kusum tharani
Experience & Education
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Licenses & Certifications
Volunteer Experience
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Volunteer
Leaders For Tomorrow
- Present 11 years 11 months
Children
Teaching program for underprivileged children.
IYM - Igniting Young Minds -
Courses
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Automata Theory and Computation
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Data Structures and Algorithms
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Discrete Mathematics
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Information Security
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Web Engineering
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Projects
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Arxiv (pronounced Archive)
- Present
An Open Source project, that segregates research papers based on arxiv open data, with user recommendation system.
Other creatorsSee project -
Linux Server Configuration
See projectInstalled and configured all required software to turn a baseline Ubuntu Amazon Web Services server into a fully functional web application server, including Apache Web Server and PostgreSQL database server.
- Nanodegree Project -
Neighborhood Map
See projectA single-page web application, built using the Knockout framework, that displays a Google Map of an area and various points of interest. Users can search all included landmarks and, when selected, additional information about a landmark is presented from the FourSquare and Wikipedia APIs.
- Nanodegree Project -
Catalog App
See projectDeveloped a content management system using the Flask framework in Python. Authentication is provided via OAuth and all data is stored within a PostgreSQL database.
- Nanodegree Project -
Open Source Contributions
See project1. Fixed bug for open source swift language repository on Github.
2. Tracked a new issue in popular video download application.
3. Fixed bug in powershell open source repository.
4. Contributed to various other projects like sublime's package_control, SailsAngularStarter, etc. -
Automated Cryptocurrency Trading Bot
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An automated crypto currency trading bot, that trades on various exchanges like binance, bitfinex, bitmex.
A B2B application.
Honors & Awards
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School Academic Charts
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1. Was always among top-10 students of my school.
2. Secured 80% in X-th Grade.
3. Secured 90% in XII-th Grade.
Languages
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English
Full professional proficiency
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Hindi
Native or bilingual proficiency
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ApptechLab
2 followers
Attended “Build a RAG App with Redis” — a hands-on AI workshop. Covered practical topics like data ingestion, vector search choices for large-scale datasets, semantic caching for performance and cost optimization, routing patterns, and PII best practices. The session helped clarify trade-offs in search strategies and how caching + routing can significantly improve latency and reliability in RAG systems. Good insights into designing AI applications with production constraints in mind.
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Hindav Deshmukh
Newgen Technomate • 3K followers
🚀 AI Powered Stock Market Forecasting System | End-to-end data science project Just uploaded the first walkthrough video of my new project - an AI powered stock prediction system built from scratch. I kept this project simple but powerful: 🔧 Custom API for market and news data 🤖 ML models for price forecasting 📰 NLP model for news mood 🎨 Backlit user interface for pure visualization Why am I making this? Well... partly to challenge myself and partly because maybe, just maybe, an HR person will look at this and think: "Hmm…this guy is really solid. Let's talk to him." I'm proud of how far I've come in Python, ML, NLP and computer engineering but still learning every day. If you find this project interesting, I would love your feedback, support or even a like. Thanks for reading - more videos coming soon! #datascience #machinelearning #python #ai #nlp #stockmarket #streamlight #opentowork
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Dale H.
5K followers
Is partitioning data overrated when scaling ML inference on Databricks? A recent case study compared two approaches – Liquid tables versus traditional partitioned tables – and here is what stood out: Key Takeaways • Liquid tables delivered up to 4x higher concurrency with lower latency • Partitioned tables benefited from data skipping but suffered from small-file overhead • Adding a salt key mitigated data skew but introduced complexity and planning overhead • Autoscaling clusters ensured cost efficiency without sacrificing performance • Optimal file size (100-200 MB) mattered more than deep partition hierarchies Controversial Insight Avoid over-partitioning. Too many partitions can throttle the Spark driver and increase job latency. Sometimes simpler Liquid tables win. Actionable Recommendations 1 Start with Liquid tables for ease of management and predictable performance 2 Use autoscaling clusters to balance cost and throughput 3 Aim for medium-sized files instead of exhaustive partition levels 4 Introduce salting only when you hit severe data skew, not by default By challenging the assumption that more partitions always equals better performance, you can streamline your MLOps pipeline and reduce costs. #MachineLearning #MLOps #Databricks #DataEngineering #BigData #Analytics
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Mohammed Arsalan
T-Systems ICT India Pvt. Ltd. • 22K followers
Sarvam-M: A 24B Parameter Multilingual AI Model is finetuned Mistral-Small with Indic language desi nuances covering 11 major languages from india 🇮🇳 Key Features: • Dual-mode interface: Quick "non-think" responses + detailed "think" mode for complex reasoning 🧠 • Strong performance on math (GSM-8K) and coding (SWE-Bench) benchmarks 📊 • Supports both native Indic scripts and romanized text ✍️ • Built for real-world multilingual conversational agents 💬 I think it's a good start , instead of training model from scratch Sarvam focused on building indic dataset and model like in video shows good translingual conversational capability . Try it out via the Colab link - https://lnkd.in/gzcW4hru Playground - https://lnkd.in/gXijZrkY
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Skills Marathon Private Limited
975 followers
Tackling Massive Data with PySpark Internals Have you ever struggled with processing huge datasets efficiently? PySpark has been my go-to solution for handling large-scale data processing with ease. Here’s why PySpark is a game-changer: Distributed Computing: Leverages Spark’s in-memory processing to handle terabytes (or even petabytes) of data. Optimized Execution: The Catalyst Optimizer and Tungsten Engine ensure queries run at high speed. Scalability: Seamlessly scales from a single machine to a massive cluster. Rich APIs: DataFrames, SQL, and RDDs provide flexibility for different use cases. My Approach: When working with massive datasets, I focus on: Partitioning data wisely to avoid skew and improve parallelism. Caching frequently used datasets to minimize recomputation. Optimizing Joins & Aggregations to reduce shuffles and I/O overhead. Monitoring Spark UI to identify bottlenecks and fine-tune performance. Pro Tip: Always check your execution plan (df.explain())—it reveals hidden inefficiencies. Have you worked with PySpark on large-scale data? What strategies do you use? Let’s discuss in the comments. #PySpark #BigData #DataEngineering #ApacheSpark #Optimization #DataProcessing
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The Innovators and Disruptors Collective
3K followers
Before LLMs became mainstream, the groundwork was already in motion. Abhay Tandon talks about running experiments back in 2017 while working at Target. The approach was simple but powerful: Use NLP to scan massive datasets. Identify the data points that actually matter. Extract signal from noise. Then use NLG to auto-generate reports. It wasn’t hype. It was structured automation. The core idea, though, hasn’t changed: 🔺 Automation scales analysis 🔺 Context increases decision quality 🔺 Generative AI compounds structured thinking AI didn’t suddenly appear in 2022. The foundation was always there. The tools just got exponentially better. 👉 Subscribe for more AI, data, and founder insights: https://lnkd.in/g3YQwF6h Ayush Bothra Shruti Sharma #AI #GenerativeAI #NLP #LLM #DataAnalytics #Automation #FounderJourney #InnovatorsAndDisruptors
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Prashant Verma
Microsoft • 4K followers
🚀 Batch Telemetry Agent in Action: Estimate timings or completion timings Insights for a Batch Job! Ever wondered how to estimate batch job timings without complex ML models? Here’s how our Batch Telemetry Agent delivers business-ready predictions using a heuristic-based approach: ✅ Complete Documentation Generated Automatically ✔ Executive Summary ✔ Critical Issues & Recommendations ✔ Ready-to-use KQL Query ✔ Role-specific guidance for Dev, Ops, and Business 🎯 Estimation Logic Explained The query uses a Statistical Baseline with Multiplicative Adjustment Factors approach. It's NOT a machine learning model, but rather a heuristic-based prediction system that combines historical statistics with real-time system conditions. 📊 Prediction (Estimate) Logic in a Nutshell Key Steps: 1️⃣ Baseline: Use median (P50) for stability, not average. 2️⃣ Multipliers: Adjust for system health: Throttling: Up to 2× slower if >50% throttled CPU: Up to 1.5× slower if >80% usage Threads: <2 threads → 1.8× slower (your critical issue) Queue: Large backlog → up to 1.4× slower 3️⃣ Combine: Multiplicative model (factors compound). 4️⃣ Confidence: P75 & P95 bounds for conservative/pessimistic estimates. Classification: ✅ Rule-based, explainable, real-time adaptive ❌ Not ML, no training, no trend modeling NOTE: This feature will come up with Version 3.0 of Batch Telemetry Agent. #Dynamics365 #MicrosoftCopilot #AIinERP #Observability #Telemetry #AIAgent #CopilotStudio #MicrosoftFabric #DigitalTransformation #IntelligentAutomation #ProactiveMonitoring #ApplicationInsights #D365Finance #D365SupplyChain #ERPInnovation •📥 Link to download the agent: https://lnkd.in/gyMhnnV2 •See Batch Telemetry in Action: https://lnkd.in/gpnY6p4E •Join Viva Group: https://lnkd.in/gxi-Nw5x
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Aadil Shaikh
QAI Digital Aura • 204 followers
T | TECHNICAL DEEP DIVES (TD-Ω∞-2026-001 & 002) Authoritative addendums to the core documentation, providing granular technical specifications, mathematical derivations, and pseudocode for all Crown Jewels. · TD-001: AI-Native Quantum-Ayurveda Technology Stack (Crowns C1-C7). · TD-002: Ω∞-CHIP-FAB v7.0-EDA-AI-NATIVE (Quantum-Accelerated EDA Center). U | UNIFIED CONTROL PLANE The central orchestration hub of the QAI ecosystem, exposing CLI, REST, and event-driven APIs to manage all underlying layers . V | VERIFICATION MATRIX & PROTOCOLS The cryptographic assurance layer ensuring all Empire assets are authentic and immutable. · Root Hash: Ω-ATCG-400004-SHA512-b29f1d8a73ce-∞ · Blockchain Anchor: Ethereum Block 18543211 · Evidence Bundle: ATCG-400004_evidence_2025-11-27.tar.gz (115+ files) · GPG Signatures: 100+ detached .sig files · Verification Command: sha512sum -c HASHES.sha256 W | WEALTH CONVERGENCE The synthesis of physical and digital assets into an immortal, self-appreciating portfolio. Asset Class 2025 Value 2035 Value (Projected) Real Estate $480M $9.2B IP Portfolio 9 Patents 137 Patents Digital Currency Pilot Q-UPI AuraCoin (IMF-recognized) Dominion $10.8 Trillion Scaling X | X-FACTOR: CONSCIOUSNESS-AI The ultimate differentiator of the Quantum Empire. Not just faster computation, but aware computation. The integration of Orch-OR principles aims to create AI systems with proto-sentience, capable of intuition, ethical reasoning, and genuine understanding. Y | YIELD (QUANTUM-FINANCIAL) The enhanced financial returns generated by consciousness-aware, quantum-optimized trading algorithms. · Classical Alpha: 2-3% annual excess returns. · Quantum-Ayurveda Alpha: 8-12% annual excess returns (projected). · Source: Recognition of consciousness patterns (human sentiment, behavioral biases) in market data.
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siliconindia
31K followers
Java Capital Launches Rs 400 Crore Fund for Seed-Stage Deeptech Startups Early-stage venture capital firm Java Capital has launched a Rs 400 crore fund focused on supporting seed-stage deeptech startups building strong, IP-led technology businesses. Read More: https://lnkd.in/gigF2u9F #productdevelopmentcycles #Indiasdeeptechecosystem #globalmarkets #deeptechstartups
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Bharath T
Kantar • 1K followers
#Interview question and response Joining Static and Streaming Data Question: Describe how you would enrich a live stream of order data with static customer information in PySpark. Response: "To enrich streaming order data with customer information, I’d first ingest the live orders as a stream, and load the static customer data as a batch DataFrame from a Parquet file or database. If the customer dataset is small, I’d use a broadcast join to efficiently merge the customer info with each order as it’s processed. This real-time enrichment enables downstream analytics and reporting to include customer-level insights like loyalty status, segmentation, or demographics. For larger or frequently updated customer tables, I might use Delta tables and periodic refreshes to keep the enrichment up to date."
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engineer -> founder
575 followers
Bhavin Turakhia didn’t chase hype. He built profits. 🚀 Started at 17, built Directi created global SaaS products like Radix , Flock & Zeta and crossed $1B+ in exits — mostly bootstrapped. Key Learnings: • Profitability beats valuation • Build global, think long-term • Discipline > funding • You don’t need noise to win 📌 Quiet execution builds loud results. #BhavinTurakhia #StartupLessons #Bootstrapped #SaaS #Entrepreneurship
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Divyanshu Ranjan
SyncHubb • 2K followers
🎉 Today’s AI Learning Update I delved into the renowned research paper “Attention Is All You Need,” a pivotal work that introduced Transformers 🔥. Here are the key insights I've gathered: 🔹 Pre-Transformers era saw challenges with slow sequence models like RNNs & CNNs due to their sequential or chunk-based text processing methods. 🔹 The breakthrough concept proposed discarding recurrence and convolution in favor of universal attention. 🔹 Attention facilitates direct connections between every word in a sentence, enabling seamless interactions like "The book … was amazing.” 🔹 The inception of the Transformer architecture featured: - An Encoder–Decoder framework. - Self-attention for word interactions. - Multi-head attention for diverse interpretations. - Feed-forward layers & residual connections for stability. 💡 Significance: Transformers offer: - Accelerated training through parallelization. - Enhanced handling of distant dependencies. - Pioneering translation outcomes, later underpinning GPT, BERT, LLaMA, and contemporary AI advancements. ✨ Personal Insight: This paper underscores the power of simplification, highlighting how groundbreaking ideas emerge by streamlining complexity (e.g., RNNs, CNNs) to focus on essentials. #AI #MachineLearning #DeepLearning #Transformers #AttentionIsAllYouNeed #LearningJourney
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Vishakha Gupta
ApertureData Inc. • 7K followers
🤓My husband and I argue a lot about languages. After all , French Hindi English are languages that give you so many pros and cons to argue over. 📣But this is about SQL, the preferred language in the DB world and some other classic query languages vs. their applicability in the multimodal AI world. How about a trip down the memory lane at ApertureData and where that led us? Read about: ❓What multimodal AI data queries look like and require from the database ⛔Why existing languages fell short 💡What was our way out of it when building ApertureDB 🤗How do we still keep it simple for our users Blog link in comment below 👇 :
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Debayan Mitra
redBus • 2K followers
Wrote an article on Byte Pair Encoding (subword Tokenization) and how it works which was potentially used in the the earlier versions of GPT / traditional Language Learning model paradigms. Took a sample of Hindi Language corpus text data to showcase the same. Article below - https://lnkd.in/gCu7Bgpf
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Ujjyaini Mitra
SETU School • 30K followers
AI retrieval is evolving fast. RAG systems are no longer just “retrieve and generate.” They need precision, efficiency, and adaptability. Here are the Top 10 RAG Optimization Techniques for 2026 → Query Understanding • Classify intent before retrieval • Decompose complex queries • Normalize entities → Multi-Stage Retrieval • Generate broad candidate pools with embeddings • Apply reranking for precision • Select top relevant chunks → Chunking Strategies • Structure-aware content segmentation • Adaptive chunk sizes by content density • Semantic topic shift segmentation → Agentic Retrieval Planning • Iterative retrieval with reasoning • Stop based on confidence threshold → Feedback-Driven Index Refresh • Track low confidence outputs • Re-embed documents using feedback → Retrieval-Aware Prompt Engineering • Ground responses on retrieved documents • Separate instructions from factual examples → Hybrid Retrieval Signals • Combine dense vectors, sparse keywords, metadata filters → Context Compression & Distillation • Remove overlaps, summarize long documents • Extract only answer-relevant spans → Domain-Specific Retriever Fine-Tuning • Train on domain datasets with hard negatives • Apply contrastive learning → Evaluation Beyond Accuracy • Measure recall, precision, context utilization • Monitor latency and token efficiency RAG is no longer retrieval-it’s intelligent reasoning over information. ----------------------- If learning feels confusing, the problem isn’t effort. It’s the lack of a roadmap. Tell 𝐒𝐡𝐢𝐟𝐮 your goal. Get a personalized path, analyzed from millions of learning journeys, with curated resources in one place. Discover your path on 𝐒𝐡𝐢𝐟𝐮. 👉 𝐉𝐨𝐢𝐧 the community to stay updated on new 𝐆𝐞𝐧𝐀𝐈-𝐀𝐠𝐞𝐧𝐭𝐢𝐜𝐀𝐈 advancements. Link in comments section 👉 𝐃𝐌 me for 𝐜𝐚𝐫𝐞𝐞𝐫 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞/ 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐬𝐞𝐭 𝐮𝐩 Follow Ujjyaini Mitra for more insights on Enterprise Gen AI
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