Bridging ML model outputs → operational CS workflows → measurable ROI

 

I am an AI CX Automation Specialist with 8+ years building and running enterprise customer success operations at HP Inc. and Replicon (Deltek).

My edge sits at a specific intersection the market is now actively naming: the AI-Human Workflow Specialist — someone who ensures AI actually improves how CS teams operate, rather than adding complexity on top of existing chaos. I take ML model outputs (sentiment scores, churn predictions, classification results), map them to real operational problems, and wire them into workflows that stakeholders can see, trust, and act on.

The market reality: 90% of organisations now use AI in operations. Only 9% have reached AI maturity. That gap — between having the tools and actually operationalising them inside CX workflows — is exactly where I work.

Built with: Next.js 14 · FastAPI · Groq (Llama 3.3 70B + Whisper large-v3) · Supabase · Clerk · Railway · Vercel · Hybrid RAG (BM25 + LLM reranking) · Dodo Payments

⚠️ This is a private repository — full codebase is proprietary. The live product is publicly accessible at getqualityos.com.

Pricing: Starter $79/month · Team $199/month · Enterprise custom · Founding member pricing active

Built with: LangChain · ChromaDB · Groq / Llama 3.1 · HuggingFace Embeddings · Streamlit · Python

Agent 1 — Pre-Briefing Pipeline: Takes a ticket ID → retrieves full customer history → runs churn risk analysis against CS policy via RAG → generates a structured 30-second agent brief before the ticket is opened. Targets 3–5 minutes of AHT reduction per ticket.

Agent 2 — Resolution Quality Analyser: Takes a closed ticket → scores resolution quality 1–10 against policy standards → flags churn risk based on pattern signals → recommends one specific team lead action. Catches bad resolutions before customers escalate.

User Input (Ticket ID)
      │
      ▼
LangChain Agent (orchestrator)
      │
      ├── Tool 1: get_ticket_details()
      ├── Tool 2: get_ticket_history()
      ├── Tool 3: check_churn_risk_signals()
      ├── Tool 4: search_knowledge_base()    ← ChromaDB RAG
      └── Tool 5: score_resolution_quality()
      │
      ▼
Structured Brief / Quality Analysis

Business case: At 200 tickets/day, the pre-briefing agent alone saves 10–16 hours of senior agent capacity daily. One Enterprise account caught before churn covers months of salary.

Built with: RAG · OpenAI API · Sentiment ML · JavaScript · 183KB single file · Zero backend

2016 ──────────────────────────────────────────────────────► 2026

HP Inc. (7 Years)                  Replicon / Deltek      AI Upskilling +
Senior Tech Support L2             Service Analyst        3 Live Products
Team Captain                       Fortune 500 CS Ops     Built & Shipped

▼ Outcomes                         ▼ Outcomes             ▼ Now
• 25% AHT reduction                • 98% SLA compliance   • Remote-global
• 50+ agents mentored              • CEO-level accounts   • AI/Ops roles
• Multiple MVP Awards              • Workflow design       • Open immediately

AI Orchestration & Agents:
  - LangChain            # Multi-tool agent orchestration — built ClarityCS
  - RAG Architecture     # Deployed in NexusCS, ClarityCS, and QualityOS
  - ChromaDB             # Local vector store for KB retrieval
  - Hybrid RAG           # BM25 + LLM reranking — production approach in QualityOS
  - Groq / Llama 3.1/3.3 # Free-tier LLM — ClarityCS and QualityOS
  - Groq Whisper large-v3 # Audio transcription — QualityOS
  - OpenAI API           # GPT integration — NexusCS Ops Assistant
  - HuggingFace Embeddings # Local embeddings — ClarityCS

Automation & Workflow:
  - N8N                  # Self-hosted automation — workflow design
  - Make.com             # SOC 2 Type II compliant — enterprise deployments
  - Zapier               # 5,000+ platform integration — QualityOS
  - Webhook Design       # Event-driven automation patterns
  - REST API Integration # Freshdesk, Zendesk, JIRA, Salesforce connectors

SaaS Infrastructure (QualityOS):
  - Next.js 14 + TypeScript  # Frontend
  - FastAPI on Railway       # Backend
  - Supabase (PostgreSQL)    # Database
  - Clerk                    # Production auth
  - Dodo Payments            # Subscription billing (works from India)
  - Vercel                   # Frontend deployment
  - Cloudflare               # Domain + DNS

Data & Analytics:
  - Python (Pandas · NumPy · Scikit-learn · Matplotlib)
  - SQL
  - Power BI
  - Jupyter Notebooks

CS/CX Platforms:
  - JIRA Service Management  # Primary depth — 7 years live experience
  - Freshdesk / Freddy AI    # Automation, triggers, SLA policies
  - Zendesk                  # Triggers, automations, SLA
  - Salesforce               # Agentforce awareness
  - Replicon                 # Deep — data extraction, API, auth, integrations

On Make.com vs n8n: Make.com deliberately chosen for SOC 2 Type II compliance — a real procurement requirement in Fortune 500 CS environments. Not a default; a defensible architectural decision.

🏅 IBM Data Science Professional Certificate — Coursera · Aug 2025 – Jan 2026 6 courses: Machine Learning with Python · Data Science Methodology · Python for AI & Development · Databases & SQL for Data Science · Tools for Data Science · Python Project for Data Science

🎓 B.Tech, Electronics & Communication Engineering — Dr. Sudhir Chandra Sur Institute · 2016

The 2026 AI job market has a well-documented structural problem:

┌──────────────────────┐     ┌──────────────────────┐     ┌──────────────────────┐
│    ML Engineers      │     │    CS Operations     │     │     Executives       │
│                      │     │                      │     │                      │
│  Build the models    │     │  Run the workflows   │     │  Want the outcomes   │
│  Can't translate to  │──►  │  Can't read model    │──►  │  Currently see       │
│  operational teams   │     │  outputs or ROI      │     │  neither layer       │
└──────────────────────┘     └──────────────────────┘     └──────────────────────┘
                                        ▲
                                        │
                               ┌────────┴────────┐
                               │   I live here   │
                               │ AI-Human CX     │
                               │ Workflow Spec.  │
                               └─────────────────┘

What this looks like in practice:

• Sentiment model scores a ticket as high-distress → LangChain pipeline routes it with context already attached → CSM receives an alert with suggested action, not just a raw ticket number
• Churn risk score crosses threshold → Make.com multi-step workflow fires → executive health dashboard flags the account → report is generated before the QBR
• RAG assistant handles tier-1 query → deflection is logged → cost-to-serve reduction is quantified → finance gets a number they can act on

Most CS Ops professionals can execute step one. Most ML engineers don't track what happens after step one. The person who architects all three steps and presents the outcome in a board meeting is what the market now calls an AI-Human Workflow Specialist.

QualityOS, NexusCS, and ClarityCS are working demonstrations of exactly that capability.

Open to remote-first senior roles in:

• AI CX Automation Specialist
• CS Operations Lead with AI mandate
• AI Workflow & Integration Architecture
• AI Platform Implementation / Agent Ops

Target regions: Remote First · Global · Australia · India (senior bands)

_{Built with clarity of purpose. Open to the right opportunity.}