AllocateClaims that move in minutes,
decided the way your best adjusters decide.
Triage, assessment, and fraud agents trained on years of your resolved claims, running against your policy rules, with every decision logged and reviewable.
Claims triage
Classify, assess, and route every claim on arrival, trained on your adjusters' historical decisions.
Fraud review
Flag the suspicious minority with evidence attached, and clear the legitimate majority automatically.
Policy answers
Give brokers and members precise answers grounded in your own policy wording, not a generic model’s guess.
A claims model at 94.1% accuracy, up 24% since June, sharpening on every claim it handles.
Models teams route here.
Start on frontier, fine-tune the open ones on your own data.
Common questions
How does an AI claims model learn our decisions?
From your resolved claims: the historical decisions your adjusters made become training examples for a model you own. Every claim the live agent then handles, confirmed or corrected, becomes signal for the next version.
Can we audit why a claim was flagged?
Yes. Every agent action and decision is logged and reviewable, and flagged cases land with the supporting evidence attached rather than a bare score.
What happens to the edge cases?
They escalate. The design goal is not full automation; it is clearing the routine majority automatically and putting the genuinely hard minority in front of a person with context assembled.
Whose model is it after training?
Yours. Fine-tuned open-weight models stay inside your isolation boundary, belong to you contractually, and are exportable if you leave.
What does it cost to run?
Token-metered usage with hard caps per route. Idle time costs nothing; put your claim volumes through the price comparison tool for a forecast.