A disciplined evaluation model that separates model quality from hype and keeps product decisions evidence-based.
A strong AI system depends on a repeatable review loop: example sets, task definitions, failure modes, and clear thresholds for acceptable quality.
We treat evaluation as part of the product contract. If a feature matters to a user, it needs a score, a human review path, and a known failure policy.
Without this harness, teams optimize for the newest model instead of the outcome that matters to the business.
