# Projects by Swarnava Dutta

> Case studies covering multi-agent workflows, RAG platforms, copilots, and automation, with the recorded outcomes for each project.

## Enterprise GraphRAG Knowledge Platform - Relationship-aware enterprise knowledge retrieval

Leading the design and delivery of a Knowledge Graph and GraphRAG platform that connects structured and unstructured enterprise knowledge. The solution models entities and relationships, builds graph ingestion and retrieval workflows, and combines graph traversal with semantic search to answer complex multi-hop questions with grounded context. The work covers architecture, evaluation, guardrails, observability, production hardening, and technical leadership across the delivery team.

- Technologies: GraphRAG, Knowledge Graphs, RAG, LangGraph, Python
- Highlights: GraphRAG relationship-aware retrieval, Hybrid graph + vector search

## Multi-Agent Operations Copilot - Multi-agent automation with JIRA, SharePoint & live web search

Built a multi-agent system that turns untracked email intake into a single auditable, session-persistent channel. Specialized agents use tool calling into JIRA and SharePoint plus dynamic web search to gather context, ask clarifying questions, classify risk, and raise tickets automatically - with human-in-the-loop approval checkpoints before anything ships. What used to take teams 2+ days from intake to ticket now lands in under 10 minutes, with parallelized Redis-backed workers compressing P95 pipeline latency by 85%.

- Technologies: LangGraph, JIRA & SharePoint Tools, Web Search, Redis
- Highlights: 98% intent accuracy, <10 min intake to ticket

## Adaptive Medical Sales Coach - Adaptive sales enablement for regulated medical teams

Built a persona-adaptive sales enablement platform for medical technology teams that need faster onboarding without losing regulatory context. The system combines RAG, MCP-style content updates, and skill-gap analysis so each rep can practice against role-specific objections, product narratives, and compliant messaging. It turns static enablement material into an interactive coaching workflow that helps managers see where reps are stuck and gives new hires a clearer path from training to confident field conversations.

- Technologies: RAG, MCP, LangGraph, Real-time AI
- Highlights: 65% faster onboarding, 50K+ monthly sessions

## Voice-to-SQL Analytics Copilot - Voice-first analytics for teams that live in data

Created a voice-enabled data copilot that lets analysts and business users ask questions naturally instead of writing SQL by hand. The workflow captures spoken intent, translates it into structured database queries, and returns usable answers from Snowflake-backed data sources. LangGraph coordinates the voice, reasoning, SQL generation, and response steps so users can move from a business question to a data-backed answer in seconds while keeping the interaction simple enough for non-technical teams.

- Technologies: LangGraph, Snowflake, Voice AI, SQL Gen
- Highlights: 200+ daily users, 8 business teams

## Interview Question Recall Engine - Interview recordings turned into clean question lists

Built a SaaS workspace for candidates who want a clear record of the questions asked in their interviews. Users upload an interview recording, the app transcribes the audio, extracts only the interviewer questions, and presents them as a clean list. The workflow is focused on question recall so candidates can review what was asked and prepare for future interviews with a more accurate question bank.

- Technologies: Audio Transcription, Question Extraction, Interview AI, SaaS
- Highlights: 1-click audio upload, Qs extracted list

## Supply Chain Copilot - Hybrid RAG and SQL routing over Databricks Delta Tables

Led development of a multi-agent enterprise chatbot that routes each request to a RAG agent, a SQL agent, or both. The SQL agent queries Databricks Delta Tables through Genie MCP, while the RAG agent retrieves supporting knowledge context. The platform handles more than 10,000 monthly queries and cut analyst turnaround time by 42%.

- Technologies: Multi-Agent AI, RAG, SQL Agents, Databricks, Genie MCP
- Highlights: 10K+ monthly queries, 42% faster analyst turnaround

## LinkedIn Content Intelligence Engine - AI content intelligence for building a credible LinkedIn voice

Built a local browser app that discovers live GenAI discussions from Reddit, Hacker News, Dev.to, and Stack Overflow, scores story relevance, and turns the strongest ideas into LinkedIn-ready posts. The LangGraph pipeline runs discovery, aggregation, analysis, selection, hook generation, content transformation, sanitization, quality control, and final JSON export with live server-sent progress events in the UI. It supports Fast and Deep modes, configurable audience, goal, tone, source selection, worker limits, output folders, and Claude token budgets so content can be tuned without touching code.

- Technologies: LangGraph, Content AI, A/B Testing, Automation
- Highlights: 20+ hrs saved weekly, A/B content optimization

## Evidence-Grounded Resume Screening Copilot - Recruiter intelligence for faster resume screening

Built a recruiter-facing screening assistant that turns a pile of resumes into structured hiring signals. It uses LlamaIndex and Pinecone to extract skills, experience, role fit, and searchable candidate context, giving recruiters a faster way to compare applicants without reading every document from scratch. The product focuses on practical hiring workflows: resume ingestion, skill matching, evidence-backed summaries, and shortlists that still leave the recruiter in control of the final judgment.

- Technologies: LlamaIndex, Pinecone, FastAPI, OpenAI
- Highlights: 1000+ resumes monthly, 90%+ screening accuracy

## Private Legal Review Copilot - A private document assistant for faster legal review workflows

Designed a polished legal AI assistant for Swiss document workflows, focused on making contract review, clause summarization, and legal Q&A feel fast, controlled, and professional. The experience presents a clean chatbot interface over a privacy-conscious RAG flow, helping users move from dense legal text to practical answers, draft language, and review-ready summaries. The project emphasizes trust signals that matter in legal operations: source-grounded responses, clear document context, GDPR-aware handling, and a calmer workflow for teams that need accuracy without slowing down routine review.

- Technologies: RAG, GDPR, Legal Tech, Semantic Search
- Highlights: 45% faster turnaround, GDPR privacy aligned

## HR & Finance Operations Copilot - One assistant for HR, finance, policy, and payroll questions

Integrated HR and finance knowledge into a shared AI assistant for routine internal support. The chatbot answers policy questions, surfaces payroll information, explains process details, and routes users toward the right operational context without forcing them through multiple systems. Built with LangChain, Azure OpenAI, Databricks, and ETL pipelines, it is shaped around the kind of questions employees ask repeatedly and the kind of auditability support teams need when answers involve people, money, and policy.

- Technologies: LangChain, Azure OpenAI, Databricks, ETL
- Highlights: 40% routine query share, 200+ hrs saved monthly