TL;DR: Designed and prototyped an AI-native claims processing platform using a multi-agent architecture that reduces claim cycle time from 14+ days to minutes, prevents leakage, and achieves 65% touch time reduction — while building a trust-first adoption model for enterprise buyers.
Context & Strategic Framing
The Market Moment
The insurance claims processing industry is at an inflection point. Three waves of technology have each failed to solve the fundamental problem:
- Wave 1 — RPA: Executes rigid "if X then Y" rules. Fails on exceptions, which in claims are the norm, not the edge case.
- Wave 2 — Chatbots / OCR: Can summarize and extract text, but can't act. The human is still the integration layer.
- Wave 3 — Agentic AI (Now): Systems that reason and take action. This is the window.
Why Insurance Claims Specifically?
I chose this vertical because it has three properties that make it ideal for agentic AI:
- High document complexity: Multiple unstructured inputs (emails, PDFs, photos, police reports)
- High decision complexity: Requires cross-referencing across policy documents, external APIs, vendor rate cards, and fraud databases
- High cost of both false positives and false negatives: A missed fraud costs millions; a wrongly denied legitimate claim destroys trust
Users & Insights
User Research Approach
I mapped the ecosystem of users affected by claims processing — not just the primary operator:
Primary User: Sarah — The Claims Adjuster
- Clears 50 claims per day; hates repetitive "stare and compare" work
- Spends 40% of her day on manual data entry and cross-referencing