Universities, Research Labs, and Health Systems

Train where the data lives, deploy where the work happens

In research and regulated operations the data is sensitive, the models are consequential, and the work is inspected. Models can make research, laboratory, and production teams dramatically faster, but only if they can be trained without moving protected data, speak the standards your systems already use, be explained when someone asks why a model decided what it did, and be evidenced when an inspector 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, interoperable data built on HL7 and FHIR so a model reads the electronic health record instead of a bespoke export, interpretable outputs, records that satisfy 21 CFR Part 11, and readiness for ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act at the management-system level.

From the bench to the line

The same discipline runs from research computing to the production floor. 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 turning HL7 and FHIR data into governed, de-identified pipelines a model can safely learn from, on infrastructure they own.
• Compounding labs, fill/finish, and cGMP tablet manufacturing using computer vision for automated optical inspection and AI-driven process optimization, held to the same evidence bar as the batch record.

How we help

We help these teams build and run AI on their own data and their own lines, in ways their people trust and an inspector will accept.

• Vet the leaders and vendors for real ML depth and regulated-industry 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 existing quality system, 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/FHIR data engineering, RAG and agent orchestration on Amazon Bedrock, computer-vision optical inspection, manufacturing process optimization, secure data lakes and transfer, on-prem GPU deployment.

Request a briefing to talk through your program.