Projects
Things I've designed, built, and shipped.
All A2A / JSON-RPC AWS Anthropic Azure AD B2C Azure AI Search Azure OpenAI Claude Claude Code ClickHouse Cosmos DB Document Intelligence EKS FastAPI Gemini GitHub Actions Go Google ADK Jira LIT LangChain LangGraph Multi-tenant Next.js OpenAI PostgreSQL Python RAGAS Realtime voice WebSockets pgvector scikit-learn
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Healthcare RAG Platform
A multi-tenant Retrieval-Augmented Generation platform for a healthcare product — tenant-isolated vector stores, secure authentication, and strict data partitioning, taken from zero to production and load-tested for 2,000+ concurrent sessions at sub-second latency.
- Python
- FastAPI
- Azure AI Search
- Azure OpenAI
- Azure AD B2C
- Document Intelligence
- LangChain
- Cosmos DB
- RAGAS
- Multi-tenant
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Clinical Simulation Platform
An LLM-powered clinical simulation platform for physician training — prompt-engineered patient personas driven by authored case content, over real-time text and voice, plus an automated feedback report that scores the trainee. Load-tested for 3,000 concurrent sessions at sub-second latency.
- Python
- Go
- AWS
- EKS
- OpenAI
- Anthropic
- Realtime voice
- WebSockets
- PostgreSQL
- LIT
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DevFlow Kit
Multi-agent SDLC automation that turns Jira tickets into production PRs with zero added infrastructure. Refinement, implementation, and Jira-sync agents decompose complex tickets into parallel subtasks and cut the ticket-to-PR cycle from days to hours.
- Python
- LangGraph
- GitHub Actions
- Claude Code
- Jira
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RegLens
Multi-agent regulatory compliance automation. Feed it a regulatory PDF and your control matrix — a compliance research agent extracts every obligation, a gap analyzer checks each against your policies via RAG and scores the risk, and a report generator produces an audit report with a human-in-the-loop approval gate. Includes a drift-detection evaluation harness.
- Python
- LangGraph
- Google ADK
- A2A / JSON-RPC
- Gemini
- Claude
- pgvector
- FastAPI
- RAGAS
- Next.js
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CostTracker
Open-source, self-hosted LLM cost tracking SDK. A drop-in instrumentation layer wraps OpenAI, Anthropic, Groq, and Bedrock clients to record usage straight to ClickHouse or PostgreSQL — real-time token metering, per-request cost attribution, and a bundled analytics dashboard.
- Python
- ClickHouse
- PostgreSQL
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Cloud Waste Hunter
Cloud-agnostic resource monitor with ML-powered waste detection. Flags idle instances, unattached volumes, and stale snapshots across providers in a unified cost-optimization dashboard — then eliminates them safely with dry-run previews, human-in-the-loop approval, and 7-day rollback.
- Python
- FastAPI
- scikit-learn
- Next.js