Most clinical AI dies in the gap between a working notebook anda hospital network. Our approach is engineered specifically forthat crossing.
We read the paper and run the notebook before we write a line of production code. Models that survive contact with real data don'tcome from generic full-stack shops.
HIPAA, GDPR, SOC 2, FHIR, HL7 — these constrain the architecture from day one, not at security review. Audit logs, de-identification,role-based access aren't sprinkled on at the end.
Our engineers sit with your radiologists, oncologists, and PIs. Clinical workflows are observed, not assumed. The interface fits thework not the other way around.
Versioned models, evaluation suites, monitoring, rollback. We treat clinical AI like the regulated software it is, because it is.
We don't hand off a tarball and disappear. On-call SLAs, MLOps handoff, and operator training are part of every engagement.
Strategic consult, two-week engineeringsprint, working demo. No retainer, noobligation.
Requirements, data audit, compliance plan,system design — turned into an engineeringroadmap.
Embedded squad ships incrementally. Clinicalreviewers in the loop. Continuous evaluationagainst your golden dataset.
Production rollout, MLOps handoff, on-callSLAs. We stay until the model is yours to run.
Two weeks. A working prototype. No retainer.
