The Challenge
Why Healthcare makes AI in Regulated Environments harder than it looks.
AI in healthcare requires passing both clinical and regulatory scrutiny. FDA guidance on AI/ML software as a medical device, HIPAA requirements for AI-accessed PHI, and hospital credentialing processes all apply simultaneously. Most AI vendors have not shipped a clinical AI system that passed an FDA review. We have built the infrastructure for systems that have.
Compliance Frameworks
hipaa
hitrust
soc 2
fda 21 cfr part 11
Methodology
How We Deliver in Healthcare
Engineering teams that understand clinical reality. Every engineer assigned to this engagement understands healthcare before they write their first line of code. Compliance frameworks — HIPAA and HITRUST — are enforced at every commit, not assessed at the end.
✓Healthcare-qualified engineers assigned before kickoff
✓HIPAA compliance mapped to architecture on day one
✓Production-ready output — not prototypes or proof-of-concept
✓Automated compliance monitoring through ALICE at every commit
✓Full IP ownership transferred at engagement close
Engagement Model
How We Engage
Embedded Capabilities
Platforms Deployed
These aren't products we sell. They're capabilities embedded in every engagement of this type.
ProofGrid
API Compliance Verification
Every integration our engineers build gets ProofGrid compliance monitoring as standard. It's why our API architectures don't create compliance gaps that surface during audits.
Regure
Regulatory Intelligence
Our teams deploy with live regulatory monitoring. When HIPAA, GDPR, UAE PDPL, or FCA frameworks change, Regure flags it and queues the engineering response before the client's legal team finishes reading the announcement.
ALICE
QA & Compliance Engine
This is the single most important reason our teams deliver compliance-native systems. ALICE makes it mechanically impossible to ship non-compliant code. It's not a QA phase — it's infrastructure-level enforcement at every commit.
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