Knowing when the AI is right
Know when the AI is right, and route the cases it isn’t sure about to a person.
“Nobody trusts the AI, so someone checks every answer by hand.”
We define what a good answer looks like, build a test set that captures it, and check the model against it, starting with whether your own people agree on the answers. Then we set thresholds so confident cases flow through and uncertain or high-stakes ones go to a reviewer.
Audubon: two volunteer datasets agreed on 63% of labels, so we fixed the labels first
What we do
- Criteria and a test set — Including whether the human labels agree with each other.
- Automated checks — Rerunnable, so quality is measured, not remembered.
- Thresholds and a review queue — Sized so reviewers aren’t buried.
- Reporting — Accuracy, exceptions, reviewer load, and drift over time.
What usually goes wrong
- Scoring a model against labels your own team disagrees on.
- Reviewing everything, which throws away the speed, or nothing, which hides the risk.
- No feedback loop, so nobody can tell whether the system is improving or drifting.
Where would an AI mistake cost you the most?
We’ll help you decide what “good” means, test against it, and put people where the risk is.