Healthcare decisions should be provable, replayable, and owned by policy, not by whichever model answered that day. LogicPearl exists to make that the default, starting with prior auth, denials, appeals, and revenue recovery.
The founding observation was an experiment. We gave frontier AI models the exact text of a published payer policy and the same patient packet: the identical inputs, nothing withheld. Each model produced a determination.
Across 8 model groups and 24 recorded determinations, 20 said approve and 4 said deny. Rerun the same model, and the rationale drifted. On one packet and one policy, the answer swung on which model you happened to ask. At scale, that variance is rework, leakage, and audit exposure.
Models are remarkably good at reading messy evidence. The conclusion was narrower and more useful: the decision itself has to come from the policy, deterministically, with a receipt. The model can do everything else. Read the full experiment.
LogicPearl was founded by Ken Erwin. He trained frontier AI models and ran the HPC clusters they train on at Meta, and has spent years working in healthcare. Knowing what these models do well, and what they can't promise, is why LogicPearl keeps the decision with the policy.
The engineering conversation happens at whatever depth your team needs, from the denial workflow down to the runtime your security review will ask about.
Every result carries the rule, the verbatim policy excerpt, the chart evidence, and a hash of the rule set, so it can be replayed in an audit later. The engine can run in your environment with no calls home. For pilots on patient records, where data is processed, who can access it, and when it is deleted are agreed in writing under a BAA before anything is shared. You own the results and the readout.
The demos run in your browser; the pilot runs on your cases.