Skip to Main Content

Claim Cipher Insurers use algorithms to deny claims. We built a self-improving agent that learns each insurer's hidden behavior, catches wrongful denials, and fights back with evidence-based appeals, without ever showing patient data to the AI. Built at the MongoDB Harness Engineering & Model Wrangling Hackathon, Saturday Sept 26, 2026. Billing operations only: no diagnosis, no treatment, no medical advice. All data is synthetic.

Team

sak0514chinmayee41203+1

Placement

Participant

More from The Harness Engineering & Model Wrangling Hackathon

Ripple designs circuit boards from plain English, grades them against hidden checks, and evolves its own harness from the failures, going from 0/4 to 3/4 on…

Ripple project preview

Offload learns the work you repeat, so next time you can hand it over. Its engine, REM (Replay · Evolve · Merge), is an agent harness with a day/night cycle…

Offload project preview

Recursive Computer Use is a local-first desktop automation harness that enables AI agents to execute tasks on your computer, verify outcomes locally, and…

Morgadarsha project preview

Da Vinci is a recursive improvement CAD harness designed to support physical engineering tasks through specialized AI agents, programmatic CAD and automated…

Da Vinci: Recursive Improvement CAD Harness project preview

CyclopsDiary gives factory workers wearing Meta AI glasses a shared memory of the floor. Every worker's glasses feed one MongoDB memory that keeps what was…

CyclopsDiary project preview

A harness that teaches itself what context to show Jev, TypeSafe's fast decision model, proves the result on a storm it never saw, and can't cheat because…

www.loom.com/…