End-to-end AI agents for property claims, from loss report to estimate. Backed by Y Combinator.
About the role
Every claim we process generates hundreds of structured data points: line items, quantities, unit prices, room geometry, equipment counts, what was submitted and what the carrier approved. Years of this history sit in reports and carrier portals that nobody has ever built models on. Your job is to turn that raw sprawl into a data asset and ship the first models on top of it.
What they're looking for
- Build the claims data platform from scratch: ingestion, schema design, feature pipelines, storage and orchestration that a growing team inherits
- Ship the first estimation models to production and iterate against live claim outcomes
- Design the labeling and evaluation strategy for data where the label is a negotiated number, because getting the metric wrong here is worse than getting the model wrong
- Wire model outputs into the agents running live claims, working with our forward deployed team during real go-lives
- Build the tooling that lets a one/two-person ML effort move like ten: training pipelines, experiment tracking, serving, monitoring
- Extend into the industry-first layer as the foundation solidifies: scope prediction from field photos and call transcripts, supplement and leakage detection
More about this role
Cozmo is the AI operating system for property claims. When a pipe bursts in someone's home at 2am, our agents answer the call, capture the loss, enter the claim into Xactimate and Cotality, dispatch the right contractor against SLA, chase acceptances before breach and draft the carrier-ready estimate from field photos. We run both halves of a claims operation: everything the customer touches and everything that happens behind the desk.
Our customers are restoration franchisors, TPAs and adjusting firms whose boards have told them to become AI-native and who have no way to do it themselves. Our anchor is one of the largest restoration franchisors in the US.
Every claim we process generates hundreds of structured data points: line items, quantities, unit prices, room geometry, equipment counts, what was submitted and what the carrier approved. Years of this history sit in reports and carrier portals that nobody has ever built models on. Your job is to turn that raw sprawl into a data asset and ship the first models on top of it.
The pipeline is the hard part and the moat. Reports are messy, schemas drift across franchise locations, carrier responses arrive in half a dozen formats...
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