GMLP readiness in Life Sciences

The governance framework for GxP AI, from ideation to monitoring

Accelerate readiness, not reconstruction

Most teams spend quarters reconstructing compliance evidence after they've already built a working prototype. Good Machine Learning Practice (GMLP) captures that evidence as you build. Risk tier and residual risk are tracked from Ideation onward, instead of bolted on after, so review starts with answers already in hand.

One governed path, four stages

Every use case moves through the same four governed stages: ideation, project, model version, and bundle. Then, each closes on a recorded sign-off before the next opens. Once deployed, the model or agent stays under continuous monitoring and revalidation because governance continues even after go-live.

Guarantee independent validation

Every model version is signed off by an independent validator who isn't the person who built it. So nothing reaches production on the builder's word alone.

Govern models and agents as one

Classical ML and autonomous agents run through the same GMLP template. It adapts automatically to what's actually built, so agent-specific steps only appear when there's really an agent present. You're not maintaining two separate governance processes for two kinds of AI.

Drop inspection panic and stay inspection-ready

Every sign-off, every change, every model version is logged automatically and searchable in one place. When an inspector asks for evidence, it's already there versus assembled after the fact.

Automatically monitor every governed model

Live metrics scored against real thresholds, synced daily from production, so drift shows up before it becomes an incident.