Observability told you what happened.

Now meet its partner evaluation.

As AI moves from pilots to production in regulated environments, one question matters more than speed: can you prove control? For GxP teams, that proof comes from two complementary disciplines—observability and evaluation.

Observability provides execution traceability: what the model saw, what sources were retrieved, what tools were called, which prompt/model/version ran, and what safety or policy flags were triggered. Evaluation provides acceptability evidence: explicit criteria, pass/fail thresholds, rubric scores, SME outcomes, trend lines, and regression results that demonstrate outputs remain fit for intended use—even as systems change.

This is how you run AI as a controlled capability – not an experiment.

Observability tells you why it behaved that way and how to control it.

Evaluation tells you whether it’s acceptable.

Together, they give the controlled speed to move faster with operational traceability and defencible output.

In our full white paper, we break down how these controls work together to enable controlled speed: faster iteration without sacrificing inspection defensibility. We also include a practical GxP example (AI-assisted deviation triage) and sample scorecards and evidence artifacts you can use to design your own program.