Universities, Research Labs, and Health Systems

Train where the data lives, deploy where care happens

In research and in health care the data is sensitive, the models are consequential, and the work is reviewed. Models can make research teams, clinical laboratories, pharmacies, and revenue-cycle staff dramatically faster, but only if they can be trained without moving protected health information, speak the standards your systems already use, be explained when someone asks why a model decided what it did, and be evidenced when the surveyor or auditor arrives.

We are ML engineers first. We build the data pipelines, train and fine-tune the models, and deploy them inside your walls when the data cannot leave. That means privacy-preserving training under HIPAA, interoperable data built on HL7 and FHIR so a model reads the electronic health record instead of a bespoke export, interpretable outputs with audit trails behind them, and readiness for ISO/IEC 42001, the NIST AI Risk Management Framework, and the Joint Commission and CHAI guidance on responsible AI in health care.

From the research cluster to the clinic

The same discipline runs from research computing to hospital operations. We work with:

• Universities and research labs training and fine-tuning models on sensitive research data, with lineage, versioning, and reproducibility engineered in.
• Health systems and physician groups turning HL7 and FHIR data into governed, de-identified pipelines a model can safely learn from, on infrastructure they own, with HIPAA and information-blocking obligations mapped from the first design review.
• Clinical laboratories bringing automation and AI into specimen tracking, result routing, and quality review under CLIA and CAP accreditation, with every automated step documented for the next inspection.
• Hospital and health-system pharmacies adding AI and automation to dispensing, inventory, and sterile compounding workflows held to USP 797 and 800.
• Revenue cycle and clinical operations teams putting governed agents to work on prior authorization, coding support, scheduling, and ambient documentation, ready for the CMS prior authorization APIs that arrive in January 2027.

How we help

We help these teams build and run AI on their own data and inside their own operations, in ways their clinicians trust and their compliance office will sign.

• Vet the leaders and vendors for real ML depth and health care operating experience, not slideware.
• Govern the models with interpretability and audit trails, so an AI decision can be explained and defended, not just scored.
• Document it as an ISO/IEC 42001 management system mapped to your HIPAA safeguards and accreditation program, not a parallel paper stack.
• Build and fine-tune models on your own data, with lineage, versioning, and reproducibility from the start.
• Secure it with federated learning and sovereign compute you own, so the model comes to the data and the data never leaves your walls.
• Enable your people so the model is something they trust and use, and the work actually gets faster.

Under the hood: federated fine-tuning, de-identification and privacy pipelines, HL7 v2, FHIR R4, and SMART on FHIR integration, RAG and agent orchestration on Amazon Bedrock, laboratory and pharmacy workflow automation, secure data lakes and transfer, on-prem GPU deployment.

Request a briefing to talk through your program.