01
Scope
Choose one relevant agent or workflow, its owners, the relevant changes and the decision to improve.
Design-partner pilot
Prove the decision on one real AI workflow.
Start with the governance bottleneck you need to remove. Together we define the representative system, evaluation scope, evidence boundaries and what a useful outcome looks like before making any claims about savings.
The engagement
Four stages, one measurable question.
Can an approved AI system change without restarting every assurance check?
02
Establish
Represent governing baseline, control relationships and available evidence. Record unsupported inputs.
03
Test change
Apply representative prompt, model, tool, permission or data-source changes and trace resulting impact.
04
Evaluate
Review evidence reuse, required revalidation, unmapped uncertainty and the reviewer decision packet.
Bring to the discussion
A system, a change, a decision.
- One representative AI agent or workflow
- The owner and current approval path
- Known components and a sample change
- Relevant policies, evaluations or evidence—if available
What the evaluation produces
A reviewable assurance impact.
- An explicit approved and candidate state
- A semantic change and affected-dependency view
- Retained, invalid and unresolved evidence distinctions
- A documented next-action and reviewer decision context
Pilot acceptance
Measure real review work—not feature count.
Evidence retained
Which previously approved evidence remains in scope after change, with a traceable rationale.
Revalidation scope
Which checks must be repeated and whether the selection is explainable.
Unresolved change
Which paths lack a reliable mapping and need human review.
Review experience
Whether a reviewer can understand the proposed state, affected controls and next actions.
Tell us where AI production review gets stuck.
We'll assess fit, integration availability, data handling and an appropriately bounded evaluation before committing to scope.