Our automated AI evaluation pairs calibrated LLM judges with human review. You get fast scoring at scale without handing final judgment to a machine.
Automated judges are calibrated, not oracles. A human reviews and signs off on every report we send you.
Automated AI evaluation, often called LLM-as-a-judge, uses models to score model outputs at a speed and volume no human team can match. It works best for high-volume, well-defined checks: format compliance, tone, rubric sub-scores, refusal behavior. It needs human calibration, because judges drift, favor familiar styles, and miss context. So humans spot-check the judge, settle the close calls, and sign the report. Read our judge selection guide if you are deciding when automation fits.
Teams ship thousands of outputs a day and grade none of them. Automation closes that gap, if it is set up honestly.
Support bots, content pipelines, and copilots produce more output in a day than a human team can read in a week. Automated judges score every output against your rubric, and humans review a sample to keep the judge honest.
Full human grading is thorough but slow, and it can hold up a release. Automation brings turnaround down to hours for routine checks, while humans stay on the hard calls. You ship on time with evidence in hand.
Even trained reviewers drift over a long day: the bar at 9am is not the bar at 6pm. A calibrated judge applies the same rule to sample 1 and sample 10,000, and human spot checks confirm it is still on standard.
A judge that was never tested against human grades is a guess with good formatting. We calibrate every judge against human-graded samples before it touches production data, and we report the agreement rate so you know exactly how far to trust it.
You send 20 to 50 model outputs plus your rubric, or we help you write one.
Calibrated automated judges score the batch; trained reviewers check samples and settle close calls.
Sample-level grades, judge agreement rates, issue patterns, and a walkthrough call.
The honest way to run automated evaluation is as a division of labor. Humans define what good looks like, grade the calibration set, and settle the ambiguous cases. The automated judge then applies that standard to volumes no human team could read.
This only works if the judge is kept honest. Every judge we deploy is calibrated against human grades first, and the report states the agreement rate in plain numbers. If the judge drifts, the numbers show it before the grades go bad.
An uncalibrated judge is just another opinion with good formatting. Judges have known failure modes: they favor longer answers, they prefer their own model's style, they go easy on the first option in a pair. Each of these can be tested for and corrected.
Our calibration covers blind grading, position shuffling for pairwise comparisons, and a fixed test set the judge must pass. We document which biases we tested and what we found, so you are not taking the automation on faith.
The right way to adopt automated evaluation is to start with the checks that are easiest to define: format compliance, refusal behavior, rubric criteria with clear rules. Prove the judge agrees with humans there, then expand to subtler judgments.
The pilot report includes our recommended automation split for your data: which checks can run automated today, which need humans, and what would have to change to move the line. You automate with evidence, not hope.
No, and you should be suspicious of anyone who says they do. Our judges are calibrated against human grades first, run with agreement monitoring, and humans review flagged and sampled outputs. The report states the judge's agreement rate with humans in plain numbers.
Format compliance, rubric sub-criteria with clear rules, toxicity and refusal behavior, and grounding checks against sources. Anything ambiguous, subjective, or high-stakes goes to a human. The pilot report tells you exactly where we drew the line for your data.
Blind grading, position shuffling for pairwise comparisons, a fixed calibration set it must pass, and periodic human re-checks. If the judge starts drifting, the agreement numbers show it before the grades go bad.
The pilot is the first step: it proves the judge works on your data. Ongoing monitoring is a separate engagement built on the same setup. See our continuous monitoring page when you are ready for that.
When the rubric is brand new and untested, when every output is high-stakes, or when you need deep qualitative insight rather than scores. In those cases we recommend starting with human evaluation and automating later.
The pilot proves the approach on your data: the judge is calibrated, the automation split is documented, and you have a graded report in hand. From there we scope what comes next based on your volume: larger grading batches, wider rubric coverage, or ongoing monitoring of production outputs. You decide with the pilot numbers in front of you.
Send a batch of outputs and get a graded report back in 2 to 3 business days once scope is confirmed.
Not sure where to start? Talk to us and we will point you at the right evaluation.