We drop a proven AI orchestration engine into the software you already run — the intelligence layer and chat interface that interconnect your systems, then schedule, automate, and operate multi-step work between them. It runs on a foundation we design and maintain ourselves, proven inside U.S. hospital systems under HIPAA and GxP. Senior AI engineers build it into your codebase, to your review standards, and you own what runs.
Your software already carries clinical work. The AI layer on top of it is a different discipline.
Production engineering is the gap. Clinicians find it first.
Getting AI into production is mostly software engineering — orchestration, retrieval, evaluation, serving, observability, governance — not model building. It is a specialist discipline most teams building healthcare software have never had to staff, and it is where AI stalls between a demo the board applauds and a system a clinician will sign off on.
Building that capability in-house is slow. Hiring for it takes months, and the clinical workflow you meant to support runs the manual way through every one of them.
We bring the engine and the engineering to the software you already run.
Production AI in regulated environments.
Our products run in healthcare, where the compliance bar is set by regulators, not preference. This is the standard we hold on every engagement we take.
PHI and data protection
Data stays in your environment. Scoped access, encryption, audit logging, and incident response are designed in, and a BAA is available where PHI is in scope.
Validation in GxP contexts
Most validation tooling was built for deterministic software and fits stochastic models poorly. We design the evidence — intended use, risk assessment, evaluation records — around GAMP 5 rather than against it. Release validation tests the system in its intended environment with authorized representative data and generated edge cases. It follows the earlier design proof in simulation.
Data integrity
Training, inference, and monitoring data handled to ALCOA+ expectations: attributable, legible, contemporaneous, original, accurate, and complete.
Audit and traceability
What the model is, what it was evaluated on, how decisions are logged, and who reviewed them — documented as part of the build, not reconstructed afterwards.
Healthcare sets the engineering standard. The controls are reviewed against your environment and your obligations.
What “AI engineering” actually means here.
The disciplines between a model that works and a system your business can depend on. We design our own architectures and maintain the orchestration foundation these run on — the same one deployed in hospitals for doctors. Roughly 80% of the effort in getting AI to production is this work, not model development.
- 01Orchestration architecture
- 02Retrieval architecture
- 03Evaluation harnesses
- 04Agent and tool design
- 05LLM infrastructure
- 06Observability and tracing
- 07Model governance
- 08Data and integration engineering
Four ways to close the gap.
Health systems, health-tech companies, and clinical startups weigh the same four options. It is worth being honest about all of them, including when we are not the right one.
- 01
Build the capability in-house
The right long-term answer if you can wait, and we will tell you so. The catch is timing: the search runs months while the AI work waits.
- 02
A large consultancy
Strong on strategy and governance frameworks. Weaker on shipping — the roadmap arrives, and the production engineering lands back on your team.
- 03
Staff augmentation
Adds hands quickly. Does not add judgment: contractors work inside your architecture decisions rather than helping you make them, and the AI knowledge leaves when they do.
- 04
DotCross
Senior AI engineers who set the technical strategy and then implement it in your codebase, with your team, to your standards. You own the architecture, the code, and the working methods.
All four are real options. Where one of the first three fits your situation better, a senior partner will say so on the call.