HA5H gives clinical AI tools persistent memory of patient charts from a single local SQLite file — SimHash + FTS5 retrieval, no cloud vector database, no embeddings API. PHI stays inside your four walls, so there's no third-party data processor to add to a BAA — and you can prove it.
The whole chart memory is one SQLite file on the device. No vector DB to stand up inside your compliance boundary — or breach.
Retrieval runs in-process: zero network calls. The on-prem build fuses outbound connections off, so chart text physically cannot reach an external API.
Every recall shows its per-facet score (SimHash · keyword · salience) — auditable retrieval, not an opaque embedding service.
Drop the open-source HA5H library inside your existing compliance boundary. One SQLite file per patient or per workspace. MIT licensed, no per-seat fees, no vendor in the PHI path.
The same engine with egress fused off, for PHI that legally cannot leave the device. What we use to validate the privacy guarantee — and what your security team can test.
# one file per patient — PHI never leaves the box pip install ha5h from ha5h import Crystal m = Crystal.open("charts/mrn-0942231.ha5h") m.crystallize("metformin 500 mg BID; penicillin allergy", salience=5) m.recall("current metformin dose") # local, <1ms, no network
Demo content is fictional and synthetic — no real patient data or PHI. The in-browser demo shows HA5H's actual ranker output (curated queries use exact precomputed scores; free-typed queries are scored client-side over the same file) and makes no network calls after load. HA5H is software, not legal advice; your HIPAA compliance depends on your overall deployment.