Projects in BengaluruProjects in Bengaluru A compliance RAG copilot for the EU AI Act and GDPR
Most AI tools answer every question. In compliance, a confident wrong answer is worse than none, because someone might act on it.
So I built a system designed to say "I don't know" when the law doesn't support an answer.
Ask a question in plain English and you get one of two things:
✅ An answer where every claim links to the exact Article or Recital it came from
🟠 An honest abstention that explains why the sources can't support an answer
How it works
• A six-step LangGraph agent: plan → retrieve → rerank → generate → critique → human check
• The critic can send a weak draft back for better evidence, with hard caps on retries and token spend
• Citations are checked against the sources the model actually saw. Invented ones are removed, not trusted
• 1,437 passages from the AI Act and GDPR, with embeddings computed locally on CPU
Measured, not claimed (37-question golden set)
• 36/37 correct abstention decisions, 0 false answers
• 0.99 faithfulness to sources
• 0.97 recall@5
Runs on $0/month: free-tier hosting end to end (Cloud Run, Vercel, Neon, Upstash), with provider fallback on every model call.
Stack: Python · FastAPI · LangGraph · Postgres + pgvector · Next.js · AI SDK
🔗 Try it live: ai-lab-web-ten.vercel.app (http://ai-lab-web-ten.vercel.app) 
