The Problem
An enterprise insurance company was processing 12,000+ claims per month using a team of 40 human reviewers. Each claim required extracting data from PDFs, cross-referencing policy terms, and applying complex adjudication rules a process taking 4–8 hours per claim on average.
Errors in manual review were leading to a 6.2% re-adjudication rate, costing significant remediation overhead and eroding customer trust.
The Solution
We built a multi-agent AI pipeline using LangChain and GPT-4 with a structured decision framework:
- Document Intelligence Agent — extracts structured data from claim PDFs using Azure Document Intelligence + custom post-processing
- Policy Matching Agent — semantic search via pgvector embeddings to surface relevant policy clauses for each claim
- Adjudication Agent — a chain-of-thought reasoning model applies extracted facts against policy rules with confidence scoring
- Human-in-the-loop escalation — claims below 90% confidence automatically routed to human reviewers with pre-populated summaries
- FastAPI backend with streaming responses; React frontend with real-time status updates via WebSockets
The Result
The platform processed its first 1,000 claims in week 3 of deployment:
- 85% of claims fully automated end-to-end with zero human review required
- 97.3% accuracy on fully automated decisions — exceeding the 95% human accuracy baseline
- $400K annual savings in operational costs in year one
- Average claim processing time reduced from 5.2 hours to 8 minutes
- Re-adjudication rate dropped from 6.2% to 0.9%