Your AI features are live in clinical workflows. Now make them dependable at scale.
We help health-software companies already running AI in production tame the operational reality: inference cost growth, latency inside the EHR, silent model drift on real patient data, PHI handling, and the audit evidence your compliance team and your buyers demand.
What operational trouble actually looks like.
The signs that a HealthTech AI feature has crossed from “demo working” to “operational problem” are usually clear once you know what to look for; and in clinical software, the stakes on each one are higher.
Five lights. One pattern. We exist to turn them green. →
How we work.
A structured audit-then-implement cycle that doesn’t end at delivery, because production AI in clinical workflows never does. As you onboard more health systems and ship more features, the loop runs again.
Production audit
We inventory every running AI feature, its cost, latency, clinical-quality incidents, PHI exposure surface, and clinician feedback, so the optimization work starts from evidence, not guesswork.
What you walk away with.
Concrete audit reports, measured optimizations, and the observability and compliance infrastructure to keep what we build running, and provable.
Production AI at scale: the engineering pattern that ships.
A measured outcome from the same operational discipline we bring to HealthTech AI.
An enterprise SaaS provider needed a production-grade RAG and vector pipeline serving thousands of in-app AI features, with strict latency SLAs and predictable cloud spend.
An MCP-based retrieval pipeline with a tuned vector store, caching and batching for inference-cost control, and observability for query latency and recall.
Production reliability was our discipline long before AI made it urgent.
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, and integration with complex existing infrastructure.
That discipline (observability, integration rigor, real SLAs, and operating under unpredictable load) is exactly what production AI demands once it’s live in a clinical workflow. Most AI teams learn it the hard way, in production, with PHI on the line. We brought it with us.
