Use case - Engineering, PM
Evaluate with what you have.
Generate what you're missing.
Score your real traces against a calibrated judge, or generate synthetic cases production hasn't shown you yet and evaluate those too. Your reviewed traces carry the human verdict as ground truth, and generation fills the gaps.
Across every eval
How it works
From a benchmark of real traces to quality at scale.
Point a calibrated judge at real traces, see where it tracks your team, and fill the gaps production hasn't shown you yet.
Build a benchmark
An evaluation dataset is real traces used as a benchmark. Build it from reviewed traces so each carries a human verdict as ground truth, or add traces straight from Findings. The more reviewed traces it holds, the more the judge has to check itself against.
Run the evaluation
The judge scores every trace in the set. Results break down per rubric dimension, with the judge's grades and the human consensus side by side, so you see where it agrees and where it still disagrees. Open any trace to dig in.
Generate what production lacks
Real traces can't cover failures you haven't seen. Generate synthetic data grounded in your traces for regressions and rare edge cases, then run and review it so it joins your ground-truth set.
Fill your dataset
Built from what you have, and what you don't.
Real traces give you ground truth. Generation covers the failures production hasn't produced.
Dataset sources
What you end up with
Quality measured, and the data to keep testing.
Every eval tells you how far the judge tracks your team, and leaves you the data to test the next fix.
Quality scored at scale
One judge run scores every trace in the set.
Judge vs human per dimension
See where the judge agrees with your reviewers and where it disagrees.
Stronger with more ground truth
The more reviewed traces a dataset holds, the more the judge is checked.
Coverage for unseen failures
Synthetic data fills regressions and rare edge cases.
Findings become benchmarks
Add a failure pattern from Findings to a dataset in a few clicks.
Programmatic access
Datasets, specs, generation, and evaluations over the API, with a pydataframer SDK.
Start with your real traces. Build from there.
Free to start. Bring your own model key or use DataFramer credits.