AI strategy defines which healthcare problems to solve first, in what order, and against what governance bar. It aligns clinical, IT, and compliance leadership on use case prioritization, vendor selection, and clinical ROI. The output is a phased roadmap your board and your auditors can both trace.
AI governance sets the rules every model must clear before it touches a patient. It defines how models are classified by risk, tested for bias across protected attributes, made explainable, monitored for drift, and retired when performance degrades. The output is the documentation regulators expect at audit.
MLOps is the production discipline that keeps healthcare AI accurate after launch. It governs how models are versioned, deployed, monitored, and retrained — with audit logs, access controls, and incident response built in. Strong MLOps hands operational ownership to your team without vendor lock-in.
01 · Discover & Assess
We map clinical workflows, inventory PHI, classify model risk, and baseline bias. No advisory proceeds until we know what success looks like clinically, and what audit will require.
02 · Architect & Govern
We design the AI-ready cloud, the integration blueprint, the MLOps plan, and the governance framework. Every dependency is BAA-backed; every control maps to HIPAA or SOC 2.
03 · Stand Up & Sustain
We help your team stand up production MLOps, the on-call rotation, and the audit documentation. The goal is independence by year two.
We provide advisory, governance, and implementation support for providers, payers, digital health, and life sciences across the U.S., U.K., and EU. We combine clinical workflow expertise, HIPAA-aligned architecture, HL7/FHIR integration, and production MLOps under one delivery model. We don’t sell slide decks, we ship healthcare AI that survives an audit.