Almanac Health
Clinical AI platform built around Almanac Copilot, which the company describes as a Level 1 autonomous EHR agent: it retrieves and summarizes patient data, drafts notes, places orders, and surfaces prioritized alerts, dynamically selecting and chaining tools from a defined set including FHIR functions, a browser, clinical calculators, a Python interpreter, and the EHR database. As a Level 1 agent it acts only on explicit clinician command and requires review and approval of every action. Grounded in peer reviewed evidence in a vector database rather than an unconstrained model, and stated to be free of pharmaceutical advertising, a deliberate contrast with ad supported clinical reference products. On the EHR QA benchmark of 300 common EHR tasks it reported a 74 percent completion rate, matching much larger models. Founded by physician researcher Cyril Zakka MD, whose Stanford work introducing retrieval augmented generation to clinical medicine became one of NEJM AI's most cited papers. Important scope note carried from the source record: the company characterizes the platform as validated through research rather than shipped as a finished product, and it is undergoing clinical validation in academic medical center settings. $10 million seed in April 2026 led by F-Prime with General Catalyst and Lightspeed.
Capability Axes
The agent is the product: retrieval, summarization, note drafting, order placement, and alerting, with the model selecting and chaining its own tools to complete a request. Nothing remains without it.
The most precisely specified autonomy position in the index, because the company names its own level. Almanac Copilot is described as a Level 1 autonomous agent: it acts only on explicit clinician command and requires the clinician to review and approve every action before it takes effect. Publishing a stated autonomy tier, rather than describing capability and leaving the ceiling implicit, is a practice more vendors in this index should adopt.
Unusually specific for an early stage company. The tool set available to the agent is enumerated (FHIR functions, browser, clinical calculators, Python interpreter, EHR database), the grounding approach is stated as retrieval from a vector database of peer reviewed evidence rather than unconstrained generation, and performance is reported against a named public benchmark, EHR QA, at 74 percent task completion across 300 tasks. Reporting against an external benchmark with a stated task count is rare here.
Research pedigree is genuine, with the founder's Stanford work introducing retrieval augmented generation to clinical medicine among NEJM AI's most cited papers, and benchmark performance is reported against EHR QA. But the company characterizes the platform as validated through research rather than shipped as a finished product, and states it is still undergoing clinical validation in academic medical center settings. Benchmark completion is not clinical outcome evidence, and buyers should treat this as an early platform with strong provenance rather than a proven deployment.
Two governance commitments stated that most peers do not make: the platform is stated free of pharmaceutical advertising, a direct contrast with ad supported clinical reference products including one indexed here, and institutional controls are stated to keep health systems in authority over how AI generated intelligence is used. Held back from A because no bias evaluation or third party audit was retrieved.
No public pricing, no free tier, no self serve plan. Contact the vendor. Sold to health systems under institutional agreements while clinical validation is ongoing.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
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Contact the vendor
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Institutional agreements with health systems | — | — | Vendor Published |
No public pricing, no free tier, no self serve plan. Sold to health systems under institutional agreements while clinical validation is ongoing in academic medical center settings. Buyers should treat commercial terms and deployment scope as research collaboration adjacent rather than standard SaaS procurement.