AI Evaluation & Human-in-the-Loop
Add evaluation, review queues, and auditability where AI output cannot simply be trusted.
The situation
We define what good means, test AI output against it, and route uncertain or high-risk cases to the right person. The goal is not human review everywhere. It is human judgment where failure costs more than review.
What we do
Evaluation criteria, a test set, and automated output checks
Confidence thresholds and a practical human review queue
Accuracy, exception, reviewer-load, and drift reporting
An audit trail where the workflow requires one
Why this work matters
Without a feedback loop, the team cannot tell whether the system is improving or quietly drifting.
Review everywhere removes the speed advantage; review nowhere hides the risk.
Clear thresholds show where automation works and where human judgment still earns its place.
