Use case - Engineering, PM
An LLM judge is only useful if it
agrees with your team.
DataFramer builds a judge from the same rubric your reviewers use, then measures how often it agrees with their scores before you rely on it.
Tracked as three metrics
Across every eval
How calibration works
From your rubrics to a judge you can measure.
Build the judge from your rubric, check it against human scores, and only trust it at scale once it agrees.
Build the judge
A judge that grades against your own rubric.
A judge grades against the same rubric your reviewers used.
Calibrate against your team
Run it against traces your reviewers already scored.
Run the judge across a dataset of reviewed traces, each carrying a human verdict as ground truth.
Trust it at scale
Once alignment holds, let the judge grade everything.
Once alignment is high, the judge can grade every trace at scale.
When alignment is low
Ways to calibrate judges in DataFramer.
If the judge and your reviewers disagree, tighten the piece that's off and run it again.
Calibration levers
What you end up with
A judge you can measure, not one you hope works.
Every version carries a number, so you always know whether the judge earned your trust.
Judge-Human Alignment
How often the judge matched your reviewers, tracked per version.
Confidence on every verdict
The judge flags calls it was unsure about, so shaky ones go back to a human.
Per-dimension results
Broken down per dimension, judge grades next to human consensus.
Catch regressions early
Reviewed traces double as a benchmark. Re-run to see if alignment moved.
Scale without more reviewers
Once aligned, the judge scores every new trace on its own.
Reusable and recalibrated
A judge carries into the next workflow that shares its rubric.
Stop shipping judges on faith.
Free to start. Bring your own model key or use DataFramer credits.