Our human evaluation AI service puts trained reviewers on every sample that matters. People read the output, check it against your rubric, and write the reason behind every grade.
Real reviewers, real reasons. We do not outsource judgment to scripts and call it evaluation.
Human evaluation of AI means trained people read model outputs and score them against a defined rubric. It is the standard everything else is calibrated against, because people catch nuance, intent, and context that automated checks miss. It works when reviewers are trained on the rubric, work from the same standard, and document reasons instead of just scores. That is how we run it: every grade carries a written reason, and a second reviewer checks the hard calls.
Automation is fast, but some failures only show up to a reader who understands what the words actually mean.
Keyword and embedding checks can approve outputs that a person would reject in a second: technically accurate, completely unhelpful, or subtly off-brief. Humans read for meaning, not for matching tokens.
Real inputs include cases the rubric never imagined. Trained reviewers flag these instead of forcing them into a box, and propose how the standard should handle them. Your rubric improves with every round.
Medical, legal, and financial outputs need a person in the loop, full stop. We keep humans on every high-stakes sample, not just a statistical fraction, and the report marks exactly which samples got full human review.
The first version of any rubric has vague spots. Reviewers find them fast: the criteria two reviewers read differently, the cases nobody defined. We send those gaps back with suggested wording, so grading gets more consistent over time.
You send 20 to 50 model outputs plus your rubric, or we help you write one.
Trained reviewers grade every sample against the rubric and write the reason for each grade.
Sample-level grades, issue patterns, recommended fixes, and a walkthrough call.
A score without a reason is a dead end. When a sample gets a P1, your engineers need to know why: which rubric criterion failed, what the output did wrong, and what a passing version would look like. Our reviewers write that reason on every single grade.
Written reasons also make disagreements useful. When two reviewers read a sample differently, the reasons show exactly where their readings diverged, and the resolution improves the rubric. The report carries that thinking to your team, not just the numbers.
Real user inputs include cases no rubric anticipated and no automated check was built for: the sarcastic support ticket, the question with a false premise, the request that is technically allowed but unwise to fulfill. These are judgment calls, and judgment is what people are for.
Trained reviewers handle these by flagging them explicitly instead of forcing them into a box. You get a clear picture of where your rubric is silent, which is the first step to making it complete.
The first version of any rubric has vague spots, and the fastest way to find them is to watch trained reviewers work. They find the criteria two people read differently, the cases nobody defined, the examples that contradict each other.
We send those gaps back with suggested wording after every round. Most teams find their rubric is twice as precise after two or three rounds, and grading consistency rises with it. The evaluation gets better because the standard gets better.
Trained evaluators who work from your rubric, not general crowd workers clicking through tasks. They are briefed on your product, calibrated on practice samples, and their grades are checked against each other before the report goes out.
Calibration rounds before grading starts, overlapping samples that two reviewers grade independently, and a written reason on every grade so disagreements are visible and resolvable. The report includes our agreement numbers.
For a pilot batch of 20 to 50 samples, yes. That size is deliberate: big enough to show patterns, small enough for careful human reading. Larger engagements are scoped after the pilot.
Yes. On automated evaluations humans review flagged outputs and a random sample of the rest. Nothing ships to you without a human sign-off on the report.
Your samples stay inside the evaluation engagement. We do not train models on client data, we do not reuse it for other clients, and we work under NDA when you need one. Ask us and we will put it in writing before you send anything.
Then we talk about it on the walkthrough call. Every grade has a written reason tied to the rubric, so disagreements become specific and useful: either we misread the rubric, or the rubric needs a fix. Both outcomes make the next round better.
Send a batch of outputs and get human-graded results 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.