On-device medical AI · QVAC Hackathon

Works alone. Stronger together.

Pharos reads a medication label, normalizes it to its generic name, checks it against your saved shelf, and explains the risk in plain language — entirely on your phone. When a capable device is nearby, harder cases are delegated to a bigger model over a private peer-to-peer mesh.

Fully on-device Offline after first launch Grounded in DDInter 2.0 Built on Tether QVAC

⚕️ Educational information only — Pharos surfaces documented interactions to help you talk to a professional. It does not diagnose, prescribe, or recommend treatment.

One product, two tiers

The floor is offline. The ceiling is a mesh.

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Solo tier — any one phone

Camera → on-device OCR → generic-name normalization → DDInter interaction lookup → an on-device MedPsy model explains the result. No account, no cloud. After a one-time model download it runs in airplane mode, provable with a zero-traffic network capture.

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Mesh tier — stronger together

When a nearby anchor is present, the explanation is delegated over a Holepunch DHT to a larger MedPsy-4B running on that device — OCR and the interaction lookup stay on the phone. If the anchor is unreachable, it falls back to the on-device model.

Safe by construction

It retrieves facts — it doesn't invent them.

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Retrieve, then explain

Interactions come from the DDInter 2.0 dataset, not the model. The model only explains a fact that was already retrieved and cited.

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Abstains when unsure

If a drug can't be resolved to the dataset, Pharos says so rather than guessing. Unknown ≠ safe; no-match ≠ safe.

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Your data stays yours

The medication shelf is stored in the device keystore. Nothing about your medications leaves your phone.

Verified, not asserted

What's actually been proven