Machine Learning / AI engineering for Digital Health
Production Machine Learning / AI built for the compliance reality of Digital Health. Not generic engineering adapted to your sector — sector-native architecture from the first design decision.
Digital health Machine Learning / AI applications operate in a space where consumer expectations intersect with healthcare compliance requirements. HIPAA governs PHI handling even in consumer-facing mobile and web applications — a digital health startup using Machine Learning / AI is a HIPAA covered entity or business associate if it handles PHI, regardless of its size or funding stage. The common failure mode is building a Machine Learning / AI application to consumer product standards and then attempting to retrofit HIPAA compliance before Series A or enterprise distribution.
Machine Learning / AI in digital health also intersects with ONC interoperability rules, which require SMART on FHIR application support for applications that connect to EHRs. HITRUST certification — often required by hospital system distribution channels — requires evidence of Machine Learning / AI security controls that meet the highest healthcare security standard. We build digital health Machine Learning / AI applications that satisfy these requirements from the architecture phase, enabling distribution into enterprise healthcare channels without architectural rework.
Digital Health engineering operates under a specific set of regulatory frameworks that govern data handling, security controls, audit requirements, and system availability. Every Machine Learning / AI architecture decision we make in this sector is evaluated against these frameworks — not added as a compliance layer afterward.
HIPAA compliance architecture for consumer-facing Machine Learning / AI applications — not retrofitted after product-market fit
SMART on FHIR integration architecture for EHR connectivity where required
HITRUST CSF control mapping for enterprise distribution channel readiness
SOC 2 Type II evidence generation built into the Machine Learning / AI deployment infrastructure
Our Digital Health case studies include Machine Learning / AI technology deployed in production — compliant from architecture, delivered on fixed-price timelines. Not proof-of-concept work. Production systems serving regulated organizations.
View Case StudiesReady to deploy Machine Learning / AI in your Digital Health environment?
We deploy engineering teams that build Machine Learning / AI systems compliant with HIPAA, SOC 2, HITRUST from the first architecture decision. Fixed price. No discovery phase. Production delivery.
Start the Conversation