For quality engineers
Quality engineering for AI outcomes.
Test more than the interface. Measure whether AI remains complete, consistent, and valid in the real workflows it affects.
What gets in the way
Quality cannot be a final checkpoint.
- Confident outputs can still be wrong.
- Drift emerges after the test suite passes.
- False positives exhaust reviewers and hide real failures.
What changes
- Evaluate outcomes against grounded quality dimensions.
- Keep human approval visible and auditable.
- Retain validated patterns as institutional knowledge.