Healthcare
Clinical AI that survives the compliance review.
Provider AI pilots stall when PHI can't go to a public model — and no one can prove which system touched which record. We put generative AI on your patients' data inside your perimeter, and produce the audit trail your compliance team needs to sign off.
Cloudera Premier Partner · WSO2 Partner · regulated-industry delivery
The compliance gate
Why provider AI pilots stall in 2026
The technology works in the demo. It dies in compliance review — for four reasons that all got sharper this year.
Public models are off-limits
Consumer AI tools aren't HIPAA-eligible for PHI. Clinicians paste it in anyway — the shadow-AI problem your security team can't see.
The data can't travel
California, Washington, New York and others now require PHI to stay in-state absent explicit patient consent. A public-cloud endpoint is a residency problem.
The rules tightened
The 2025 HIPAA Security Rule made encryption and access controls mandatory and widened business-associate accountability. New state AI laws add disclosure duties.
Nobody can prove who accessed what
AI agents reach PHI through shared service accounts. When the auditor asks which agent read which record under whose authority, there’s no answer.
The approach
Two problems, one perimeter
Keeping the data in is half the job. Governing how AI reaches and acts on it is the other half. We deliver both.
The data never leaves
Model-agnostic generative AI running on your PHI inside your own environment — Cloudera AI with NVIDIA NIM in-perimeter. No egress, no public endpoint, residency intact.
Governed access, both directions
The governed pipe that lets AI reach Epic, Cerner and FHIR/HL7 sources in-perimeter — and the governed front door that gives every agent its own identity and leaves an audit trail of who touched which record.
access points
In-perimeter, in practice
The signal in the noise — inside the walls
A patient comes in for one thing, but the data streaming across the floor often carries a second signal — one scattered across systems that were never built to talk to each other. For a nationwide provider grown partly by acquisition, we aggregated real-time feeds from many disparate sources into one live clinical picture, and put intelligence on top that listens for a concern beyond the presenting complaint.
When the pattern crosses a threshold, the system raises an alert — and only an alert — to the physician, nurse, and floor lead. It surfaces the signal; it never recommends or decides. That boundary kept it deployable. And every feed, inference, and alert stayed in-house — PHI never left the building.
What we deliver
From stalled pilot to production
Start with the low-risk read; grow into the flagship build — and, only if it's the right fit, optional managed operation. You own the result either way.
Every workload we take toward production is read against three dimensions. That read — not the pilot result — is what separates a demo that impresses from a system you'd actually deploy.
PHI & AI governance review
A fast, fixed-scope read of your AI workload — the value it creates, the burden it must demonstrably honor, and whether it holds up in production rather than just passing the pilot. An architecture decision, not a compliance audit.
Private AI on Cloudera
The flagship build — generative AI on patient data, in-perimeter, model-agnostic.
Governed clinical access
WSO2 agent identity and audit evidence — answer "who touched this record, under whose authority."
Clinical data interoperability
Reach Epic, Cerner and FHIR/HL7 sources in-perimeter, with consent built in — no brittle point-to-point.
POC rescue & production readiness
The pilot passes but you wouldn’t approve the deployment. We read it against outcome, compliance and surety and produce the design that says go, no-go, or go-if — and names what production actually requires.
Not every workload is clinical
Some of the highest-value provider AI runs nowhere near the bedside. All of it runs on data you still can't hand to a public model — patient records, yes, but also contracts, financials, and regulatory filings.
The same in-perimeter approach that keeps PHI in the building keeps the rest of your sensitive data in there too. And because administrative workloads don't make clinical determinations, they often clear review faster — a clean first win that proves the pattern before you point it at clinical care.
Building AI for providers as a vendor? Clinical or operational, we make yours the product that survives the BAA and the security review.
Operated by us — owned by you
Plenty of clients run the system themselves once it's built — that's a complete delivery, full stop. For those who'd rather not staff the operation, we can run it inside your perimeter, under your governance. Nothing leaves; nothing changes hands. The data and the system stay yours, and you can bring operations in-house whenever you choose. Sovereignty isn't who staffs the run — it's whose walls it runs inside.
When a managed build is the goal, we architect the application to be operated from the first design decision. The same properties that earn a workload its go — projected quietness, and durability as data, rules and models change — are what make it cleanly operable. Build-to-operate means the Run tier is designed in, not bolted on.
Depth where it's regulated
Compliance has its owners — your legal and regulatory teams. We design the architecture that makes their posture demonstrable, and project whether it stays durable in production. We're integrators, not counsel.
Start with a governance review.
Fixed scope, low risk, no platform commitment. The fastest way to find out what stands between your pilot and production.