AI that cannot invent
your patient's record.
Dasyante turns unstructured clinical reports into a structured, longitudinal patient record. Every fact is anchored to its exact source sentence. Every link is made by deterministic code, not a model's guess. Every entry is approved by a clinician.
"Interval increase in the right lower lobe mass, now measuring 4.1 × 3.3 cm (previously 3.8 cm)."
Medicine's most important data is trapped in prose.
Decades of diagnoses, findings, and outcomes sit in free-text reports no system can use — so every clinician starts from zero, reading years of notes against the clock. Generative AI promises to read it all, but hospitals can't build records on a system that occasionally makes things up.
An intelligence layer that separates reading from record-keeping.
Dasyante lets AI do what it's good at — understanding language — and nothing else. Identity, verification, and linking are owned by deterministic code that behaves the same way every time. The result is a living, longitudinal patient record the institution can actually trust.
From free text to a verified record, in three moves.
Extract with an anchor.
The model extracts each clinical finding as a structured fact — and must anchor it to the verbatim sentence it came from. No anchor, no fact.
Prove it, then approve it.
A deterministic gate checks every anchor against the source text, and a clinician approves each fact before it's committed. Unsupported facts aren't filtered out — they're impossible by construction.
Connect across years.
Deterministic code connects verified facts across years of reports into longitudinal tracks — how a tumor evolved, how disease responded to treatment. Same input, same output, every time.
Built on three refusals.
Provenance, by construction.
Every fact in the record traces to the exact sentence that produced it. Audits take a click, not a committee — ready for regulators, and for the evidence standards of clinical research.
Sovereignty, by default.
Dasyante runs on-premise or in the hospital's own cloud; patient data never leaves the institution. It's model-agnostic — run any LLM, swap it freely, no lock-in — and aligned with India's DPDP and the EU AI Act from day one.
Learning, without forgetting.
Every clinician correction makes the system permanently better — through a mechanism that's auditable, revertable, and can never silently regress.
The loop ↓A system that gets smarter, safely.
When a clinician corrects Dasyante, the correction doesn't vanish into a retraining queue. It becomes a proposed rule — a small, human-readable change to the deterministic layer. The rule must pass a frozen regression gate: fix the new case, break nothing that already works. Only then is it merged.
Because every rule is atomic and revertable, there is no catastrophic forgetting and no silent drift. No black-box retraining. Accuracy compounds with use — and every step of that improvement is inspectable, reversible, and approved by a human.
Most AI systems age. This one accrues.
One record, two audiences.
A longitudinal view of the whole disease course.
Every patient's history in one place, with a review workspace where every fact can be inspected at its source and approved in seconds.
The same verified record, given back.
The same trusted history, rendered as something patients can understand and carry with them across every institution they visit.
The technology and the law arrived in the same decade.
Large language models can finally read clinical language at scale — and regulators, in the same moment, are demanding that medical AI be explainable, auditable, and sovereign. Most systems can satisfy one demand or the other. Dasyante's architecture was built for both.
Proven in architecture. Now proving in the clinic.
Dasyante is in clinical validation with a leading Indian cancer centre. Our beachhead is oncology, where longitudinal truth matters most: disease response is measured across years of imaging, and a missed or invented fact changes treatment.
Early-stage · oncology beachhead · clinical validation underway
We're raising our seed round.
We're looking for partners who believe the winners in clinical AI will be the ones institutions can trust. If that's you, we'd like to talk.