Expert Review & Evals - Domain experts, Ops, PM, Engineering
Turn expert corrections into improvements that stick.
Capture expert judgment on the journeys and cases that matter, turn confirmed corrections into reusable ground truth and regression tests, and verify that the resulting changes improve the outcome.
Across every review
How a review flows
From an important case to reusable ground truth.
Start from the cases that affect the outcome: a stalled application, a corrected claim, an escalated ticket. Set the standard once, run those cases through the loop, and every correction feeds back on its own.
Define the standard
One rubric, with real examples, guides every reviewer.
Under Rubrics you set the dimensions to grade, a scoring type for each, and good, bad, and ambiguous examples from real cases. One rubric guides every reviewer, and later calibrates your judge.
Route & grade
Reviewers grade in a workbench built for the rubric.
Queues hand the right cases to the right reviewers, rubric attached. Reviewers score each dimension in an in-built workbench, set rationale, and edit the answer inline. The copilot drafts the correction and rationale with full context.
Reuse every correction
Every correction shortens the next similar review.
No review is a one-off. Each correction is distilled into memory that shortens the next similar case, and flows straight into judge calibration and regression datasets.
Review Copilot
A copilot that grades with the full picture.
Grounded in the same rubric it grades against, so its help always matches your standard.
Knows
Outcomes
Every review makes the next one easier.
The value shows up after the first review, and compounds from there, until the same journey measurably performs better than it did before.
Consistent standards
A shared rubric enables reviewers to reach the same verdict on the same trace.
Trustworthy ground truth
A reviewer-agreement score tells you whether the standard is solid, or ambiguous enough to fix before you build on it.
Faster reviews
The copilot drafts rationales and corrections with full context, so reviewers approve instead of writing from scratch.
Reusable memory
Each correction is distilled into memory that shortens the next similar review, so review effort compounds instead of repeating.
Judges & datasets
The same rubric calibrates an LLM judge, and every correction becomes a regression test case.
History that survives
Versioned rubrics keep past grades intact, and every review stays in DataFramer when a reviewer moves on.
Proof the change worked
Re-measure the same journey and the same business outcome after the fix, so you know the correction improved the result and did not reintroduce an old problem.
Going deeper: use the same ground truth to build and calibrate an LLM judge, then evaluate at scale and generate the cases production has not shown you yet.
Stop losing expert judgment to one-off reviews.
Free to start, no card required. Bring your own model key or use DataFramer credits.