Bring AI into your health platform, without the compliance and safety failures that sink most first deployments.
We help health-software companies move from “we’re thinking about AI” to “we’re shipping AI features that clear review”, with HIPAA-aware infrastructure, clinical-safety governance, and ROI instrumentation built in from day one.
Where Pre-AI HealthTech companies stall.
Most health-software companies starting their AI journey hit the same wall in the same place. The pattern is almost identical, engagement after engagement.
The pilot looks promising.
A model summarizes clinical notes or codes claims with high accuracy on a clean, de-identified dataset. Leadership greenlights the work. A small AI team is hired or assembled.
The compliance question gets deferred.
The pilot ran on synthetic data in an open environment. Production needs PHI-safe infrastructure, HIPAA-aligned access controls, audit logging, and a BAA-ready data path, none of which were in scope.
The use case wasn’t strategically prioritized.
The pilot was chosen because it was technically tractable, not because it was the highest-ROI, lowest-clinical-risk use case for your customer base. The more valuable use cases stay un-built.
There’s no ROI instrumentation.
The feature ships, but nobody can quantify what it’s actually delivering to providers and patients. Leadership can’t make the case for the next investment.
Momentum stalls.
The AI initiative loses internal sponsorship. The compliance and infrastructure investment isn’t paid back. The next pilot starts from zero.
We exist to break that cascade. →
How we work.
A structured 60–90 day engagement that sequences strategy first, then HIPAA-aware infrastructure, then deployment, with measurement and clinical-safety governance baked into every stage.
AI Opportunity Mapping
We start by mapping where AI actually pays off for your platform: every candidate use case scored on customer ROI, clinical-safety risk, and strategic compounding. You get a ranked list, not a wishlist.
Every candidate use case, scored before a line of model code.
We map and prioritize opportunities against customer-facing ROI, clinical-safety risk, and strategic compounding, specific to your platform. You walk away knowing exactly what to build first, and why.
The panel that proves what the feature is actually delivering.
Measurement systems and runbooks quantify the value to your customers and to leadership, so the case for the next investment makes itself. Evidenced, not asserted.
What you walk away with.
Concrete artifacts, HIPAA-aware infrastructure, and a shipped feature, not slides.
A first production deployment, shipped, not piloted.
What a Pre-AI engagement looks like when it lands: a scoped feature live in production, HIPAA-aware and instrumented from day one.
A health-software team had a promising note-summarization pilot but no production path: built on synthetic data, no PHI-safe infrastructure, no EHR integration, and no way to prove ROI to its own customers.
We scored the highest-ROI, lowest-risk use case, built the PHI-safe data and EHR integration foundations, and shipped the first production feature, instrumented to measure customer outcomes from day one.
Engineering under real-world constraints, before it was an AI problem.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where production reliability wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Real-time interactive systems, AR/XR experiences, integration with complex existing infrastructure.
That experience (engineering under real-world constraints, integration with legacy systems, observability discipline) is exactly the discipline HealthTech AI requires, where PHI handling and clinical safety leave no room for error. Most AI teams don’t have it. We do.
