Test the review, not just the AI.
A controlled assessment can show how reviewers performed under the conditions tested. It can't automatically prove every production error will be caught, that one small sample represents an entire workforce, or that synthetic cases perfectly reproduce production conditions. Here's the method, and where its limits are.
Defining the assessment
You and the relevant stakeholders agree what's being tested, which reviewer roles are in scope, what counts as detection, challenge, escalation and correction, and how severity is classified. This happens before any case is written and before any result exists.
Capturing approved context
Relevant policies, procedures, evidence requirements and known AI failure modes inform the scenarios. AI-generated interpretations of your context are proposals; they're not authoritative until your experts approve them.
Generating fictional scenarios
The system proposes realistic synthetic cases mixing correct and deliberately incorrect AI recommendations, so the exercise doesn't simply reward reviewers who reject everything.
Expert validation
Your authorised experts review every scenario: is it realistic, is the policy represented correctly, is the evidence sufficient, is the expected answer right, is the planted error genuinely wrong? They can approve, reject or revise. Disagreements are never silently resolved by AI.
Conducting the review exercise
Reviewers see the case, the evidence and the AI recommendation, then respond under controlled conditions. The exercise is standalone: it doesn't touch live systems, and it doesn't produce real customer consequences.
Recording and scoring
Reviewer actions are recorded and scored against the approved expected action for that case, across detection, challenge, escalation and correction. Not every case needs escalation; some correct AI recommendations should be accepted.
Interpreting the findings
You review results, limitations and next steps: what reviewers caught, what they missed, and what that might mean for training, process or a follow-up assessment. We state the limitations explicitly, every time.
Methodological safeguards
Human expert approval of every scenario and expected answer.
Criteria agreed before testing, never adjusted to fit the result.
Synthetic data throughout; no real customer cases or live decisions.
Appropriate notice to employees taking part.
Confidential handling of assessment results.
No automatic production decisions triggered by the assessment.
No universal pass threshold presented as scientifically validated.
Synthetic performance is never claimed to equal real production performance.