Planner model
Predicts, per provider, whether API, portal, phone or fax will succeed fastest, and in what order to try them.
Four models, one shared memory of how 90,000 care sites prefer to be asked. Every retrieval makes the next one faster.
Intelligence
Most retrieval breaks slowly: a portal redesign here, a changed fax number there. Retrievant treats every attempt as training signal. The context layer remembers how each of 90,000 providers prefers to be asked, so success rates rise the longer we run.
See our coverage in your regionPredicts, per provider, whether API, portal, phone or fax will succeed fastest, and in what order to try them.
Turns scanned charts, PDFs and free-text notes into structured FHIR resources, with confidence scores on each extraction.
Merges duplicates, resolves conflicting dates and doses, and flags what it couldn't settle for a human reviewer.
A living profile of every care site: hours, preferred channel, quirks, typical turnaround. Updated after every contact.
Most retrieval infrastructure decays: a portal redesign here, a changed fax number there, a clerk who now insists on email. Retrievant treats every attempt as a training signal. When a browser agent fails on a redesigned portal, the recovery is recorded and applied to every other customer's retrievals from that provider. When a voice agent learns a practice closes at noon on Fridays, the planner stops scheduling calls then.
The result is a coverage curve that climbs with volume. Our first-attempt success rate on long-tail providers has risen from 61% to 89% over eighteen months, with no change to the underlying models, purely from accumulated provider memory.
Bring a real case with a messy history. We'll run the agents live on a 30-minute call.
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