The eval tuning loop that runs itself.

Five corrections. One autotune. Up to 17% accuracy improvement. No data science required.

Real Results.

Abstention classification

0.87→0.97F1

5 corrections

Context adherence

0.67→0.84F1

5 corrections

The system converges fast. Just five scores. Your reviewers don't need to be prolific.

Correct a score, get a better judge prompt.

The people reviewing your traces already see the errors. Now they can fix the evals directly. Four capabilities. Zero lines of code.

You’re reviewing a trace. The context adherence score says true but the response paraphrased the refund policy instead of quoting it. Click the score. Type why it’s wrong. Thirty seconds. You’re back to reviewing.

Autotune feedback1 of 1 spans

Span level

Context Adherence Label v1

False

Input

Question: why am i being charged a maintenance fee

Context: Info: Banking fee details...

Your feedback

Corrected value*

Rationale

The response correctly highlights all reasons why a maintenance fee would be applied to the account

Example: the source document does not contain the requested output

Add to Queue

Stop calibrating. Start shipping.

Your reviewers already see the errors. Autotune turns that expertise into better evals. Automatically. In minutes, not sprints.