Payers · providers · trials
Healthcare is already data-intensive; adding generative models without a residency story is how privacy officers lose sleep. SoverAI builds agents where OCR, retrievers, and summarizers share the same scope as the rest of your EHR, claims, and trial systems — and produce evidence your IRB and CISO can interrogate together.
PHI
Data class as first control
US / UK / IN
Patterns we map in pilots
BAA
Context for covered workloads
HITL
Clinical gates where required
Regulators expect the same level of control over AI as for core systems — with clearer lineage and data boundaries than most cloud-native stacks default to.
They need to be processed where your BAAs and DPA say they live — not in a public endpoint because it was the fastest to wire.
Narrative generation must be consistent with your policy engine and your appeal process, with line-of-sight to the exact policy version and channel.
Cohort selection and safety surveillance need consent boundaries that survive statistics and privacy review — at the same time you want modern NLP.
A single control plane: jurisdiction-first routing, agent guardrails, and evidence-grade audit you can hand to a supervisor without re-architecting your estate.
Patterns we see in diligence and architecture reviews with regulated customers — not exhaustive, but designed to be concrete for internal steering committees.
Mapped to the frameworks your legal and second-line teams already use — with exportable evidence bundles per region.
Regulatory context: HIPAA, CMS / ONC expectations, NHS DSPT-style patterns in the UK, and emerging national health privacy regimes in India and the Middle East — as a living matrix, not a one-time slide for the board offsite.
We will map your data classes, model catalog, and regulator expectations to a deployable control-plane design — with exportable artifacts for legal and security stakeholders.