Evidence-Driven Training
Models are built exclusively from peer-reviewed literature, clinical guidelines, and de-identified health records. A provenance layer tracks every token’s source, letting us surface real-time citations.
We’re re‑imagining health language models to be evidence‑driven, clinically validated, and privacy‑first—so patients and clinicians can trust every answer.
General-purpose language models hallucinate; their web data is noisy and opaque. Future health AI must be evidence-linked, continuously validated, and privacy-preserving — running in real time on the devices clinicians and patients already use.
Peer-reviewed papers, clinical guidelines, and de-identified EHRs — no random web facts.
Benchmarked on public medical QA sets plus our in-house clinical vignette bank.
Lightweight rules engine blocks speculative answers and adds disclaimers.
Models are built exclusively from peer-reviewed literature, clinical guidelines, and de-identified health records. A provenance layer tracks every token’s source, letting us surface real-time citations.
Each release must outperform baseline thresholds on MedMCQA, PubMedQA, and our internal vignette suite before deployment.
A compact rules engine post-processes outputs, blocks speculative diagnoses, and injects FDA-style disclaimers when confidence dips.
With explicit consent, encrypted and anonymised feedback refines models — always in full HIPAA compliance.
Quarterly benchmark reports and open-methods papers keep the community informed and accountable.
Inference runs on secure edge hardware; sensitive data never leaves the device unencrypted.
We publish research papers to advance the field.
Quarterly disclosures of performance on public datasets.
Seeking clinicians & domain experts for external audits.
Exploring strategic investment or grants? .
A privacy-first chat interface delivering evidence-linked answers — instantly, on your wrist or in the clinic.