This is a submission for the GitHub Finish-Up-A-Thon Challenge
What I Built
SecureShield is a GenAI-powered health insurance claim eligibility engine built for Indian patients. You upload your policy PDF, enter your case details, and a 5-agent AI pipeline tells you exactly whether your claim is eligible - citing the specific IRDAI 2024 regulation behind every decision. No guesswork. No hallucinations. No rejected claims due to ignorance.
🔗 GitHub
Tech Stack:
Frontend → Next.js 16 (React 19, Turbopack) Backend → FastAPI + LangGraph Database → Supabase (PostgreSQL + pgvector) Auth → Supabase JWT AI Gateway → Cloudflare AI Gateway LLM Chains → Cerebras, Groq, Gemini, xAI, OpenRouter Compliance → IRDAI Health Insurance Regulations 2024
Demo
The Comeback Story
In 2024, I watched a friend's father's ₹2L surgery claim get rejected - because of a clause buried in page 34 of a 47-page policy nobody had read. That experience planted the seed.
Early 2025 - ET GenAI Hackathon: I pitched SecureShield. The idea was validated by the judges. I was motivated. Then came university exams. I had to choose - submit a half-baked prototype or protect my grades. I chose my grades. SecureShield sat abandoned: 2 rough Python scripts, local SQLite, no UI, no pipeline.
June 2026 - GitHub Finish-Up-A-Thon: The challenge felt written for me. "Finally finish what you started." So I did.
Before → After
| Before (ET GenAI) | After (Finish-Up-A-Thon) | |
|---|---|---|
| Agents | 2 rough Python scripts | 5-agent LangGraph pipeline, 18 tools |
| Database | Local SQLite | Supabase PostgreSQL + pgvector |
| UI | None (terminal only) | Premium Next.js 16 frontend |
| AI | Single provider | Multi-provider: Cerebras → Groq → Gemini → xAI |
| Deployment | Ran only on my machine | Containerized, GitHub Actions CI/CD |
| Security | None | JWT, rate limiting, HMAC-SHA256 |
| Compliance | Hardcoded rules | Semantic search over 49 IRDAI 2024 chunks |
Key design choice: The Decision Engine uses zero LLMs - all eligibility verdicts are deterministic and auditable. LLMs only generate the plain-language explanation. No hallucinations in decisions. Ever.
Copilot helped me in three meaningful ways:
- Dockerization - I had never containerized a project. Copilot walked me through multi-stage builds and secrets management. That conversation changed how I think about portability.
- Stack decisions - Copilot helped me choose pgvector-in-Supabase over a separate vector DB: fewer services, tighter auth, same SQL interface I already knew.
- CI/CD design - As the project grew (backend + frontend + 4 AI providers), Copilot generated GitHub Actions pipelines that actually matched my stack - not generic boilerplate.
My Personal Note
I'm an engineering student in India. Insurance confusion and claim rejections are real around me, every day. AI in healthcare isn't about replacing doctors. It's about making the system legible to the people it's supposed to serve.
Thanks to this challenge, SecureShield is finally out of my laptop and into the world.
Built with LangGraph, FastAPI, Next.js, Supabase, and a lot of frustration turned into motivation.
