🛡️ My Neighbour's ₹2 Lakh Insurance Claim Was Rejected. I Built an AI to Make Sure It Never Happens Again.

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GitHub “Finish-Up-A-Thon” Challenge Submission

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.