Production-grade AI for the platforms running modern healthcare.
We help health-software companies build AI that clears the clinical-safety and HIPAA bar, integrates the EHR and claims systems already in production, and delivers measurable outcomes.
AI that clears the clinical-safety bar.
The architecture handles PHI under HIPAA from the first byte, integrates the EHR and claims systems already in production, and is continuously monitored for drift.
PHI ingest & de-identification
Clinical notes, lab results, claims and device feeds arrive as protected health information, encrypted, access-logged and de-identified before a model ever sees them.
The state of AI in HealthTech.
Health-software companies have spent a decade building compliant, audit-ready data foundations. AI is the obvious next layer; but HealthTech sits in a higher-engineering-bar quadrant than most software verticals, for structural reasons.
PHI carries HIPAA obligations that transaction data doesn’t: consent, minimum-necessary access, audit trails and breach exposure all shape what a model is even allowed to see.
A confidently wrong answer can touch a care decision. The bar isn’t plausible output. It’s grounded, citable, guardrailed output a clinician can trust.
Health systems expect software to pass security review, hold up under audit, and integrate the EHR on day one, conditions that break a typical SaaS deployment.
The opportunity is real. The engineering bar is higher.
What goes wrong in HealthTech AI.
Four failure patterns we see again and again, and what they require to fix at the engineering layer.
PHI handling & HIPAA exposure
AI features bolted on after the fact leak PHI into prompts, logs and third-party endpoints. Compliance gets discovered in security review, not designed in, and the launch stalls.
PHI boundaries from day one: de-identification, encryption, access logging, BAA-ready vendor paths.
Unsafe output under real conditions
A model that looks flawless in a demo hallucinates on messy clinical notes, edge-case histories and ambiguous coding. Demo conditions are not care conditions.
Grounded generation, clinical eval sets, confidence gating, and a human-in-the-loop path baked into the build.
EHR / EMR & claims integration
Most health customers run Epic, Cerner, a clearinghouse and a tangle of legacy interfaces. AI features that need pristine FHIR APIs die in deployment because integration is harder than the model.
We treat integration as core engineering: FHIR / HL7 bridges, message queues, schema validation.
Model degradation in production
Coding guidelines change, payer rules shift, and clinical language evolves. Without continuous eval infrastructure, models silently degrade and clinical teams lose trust.
Eval + regression in CI, safety dashboards, automated retraining triggers.
What makes HealthTech AI different.
Three system constraints that HealthTech AI has to be designed around, not retrofitted into.
Two engagement paths, depending on where you are.
We work differently with teams just starting their AI journey versus teams optimizing what’s already running.
For HealthTech companies that haven’t yet shipped production AI.
We map where AI delivers the most ROI for your specific platform, design the HIPAA-compliant infrastructure foundations correctly from day one, and ship the first production deployment with ROI instrumentation built in.
First production AI shipped, compliant, and with measurement baked in.
For HealthTech companies already running AI features.
We audit what’s running, identify the highest-ROI optimization wins, and implement with quantified before/after measurement: latency, cost, reliability, safety and observability gaps.
Better latency, lower cost, higher reliability. Measured.
We don’t just plan it. We ship it.
A measured outcome from a production-AI engagement, the same operational discipline we bring to HealthTech.
The engineering bar is higher here. So is ours.
Before AI was the conversation, we were building performance-critical, real-time systems for Disney, Coca-Cola, Apple, LEGO, Warner Bros, and SXSW, systems that had to run reliably under unpredictable load, integrate with existing infrastructure, and hold up at brand scale.
That same engineering discipline, observability, integration rigor, real SLAs, security awareness, is what production AI requires. Especially in HealthTech, where PHI, clinical safety, and the EHR raise the bar higher than in most other software contexts.
