Health HUD
Ambient health intelligence for Meta Ray-Bans. Your medical context surfaced in the moment — no phone required.
Backed and validated by
One continuous loop: capture evidence locally, model a private timeline, strip identity at the edge, then route clinical intent to the right next step.
Foundation layer of the twin. Scan, structure, and query your own record with source traceability and local-first defaults.

Escalation layer for decision-time moments. Strips PII locally, routes anonymized clinical intent, and checks medication interactions from grounded context.

Ambient health intelligence for Meta Ray-Bans. Your medical context surfaced in the moment — no phone required.
This is not a generic chatbot layer. It is a continuously updated model of your personal history: what happened, what changed, and what you should do next.
Convert scattered reports into one private, source-linked longitudinal record.
Build a usable health twin that tracks trends, baselines, and risks over time.
Deliver timely nudges and escalation paths through phone and wearable interfaces.
Most health copilots are cloud wrappers. Somach is built as an on-device twin stack first, so trust and speed are product properties, not legal promises.
Every answer cites the file it came from. No black-box summaries — the source document stays attached to the record.
Capture, structure, and recall run locally by default. Cloud routing is opt-in, not the baseline.
Each scan enriches a persistent timeline. The twin gets more useful the longer you use it.
Building in digital health, on-device AI, or longevity systems? Somach is already shipping the health twin foundation and ready for serious product and distribution partners.