Our LLM evaluation services grade your model's real outputs against a rubric you sign off on. You get sample-level grades, clear failure patterns, and fixes you can act on this week.
We grade outputs against agreed rubrics. We do not train models, run fine-tuning, or invent scores to make anyone look good.
LLM evaluation is the process of scoring a language model's outputs against defined criteria, so teams know what the model does well and where it fails. It matters because models that pass standard benchmarks still fail on real customer inputs. Good LLM evaluation uses your own data, your own rubric, and human review on the calls that matter, so the results describe your product and not a test set.
Public leaderboards test general knowledge. Your users test your product. Here is what we catch in the gap between the two.
A model can top public leaderboards and still fail your users. Public benchmarks test general knowledge, not your product's prompts, tone, or edge cases. We grade your actual outputs, so the score describes what your customers experience.
A new model version or a prompt tweak can fix one thing and break three others, and teams often hear about it from angry users first. We compare releases sample by sample, so regressions show up in the report before they reach your customers.
Models can answer a question well on Tuesday and badly on Thursday. Small changes in wording can flip an answer from right to wrong. Grading a batch of samples surfaces that inconsistency, so you can tell whether it is a prompt problem or a model problem.
When teams review outputs informally, every reviewer applies a different bar, and nobody can compare scores across weeks. Our rubric plus the P0 to P3 scale gives every sample one shared standard, so a grade means the same thing no matter who graded it.
You send 20 to 50 model outputs plus your rubric, or we help you write one.
Trained reviewers grade every sample against the rubric, backed by automated checks.
Sample-level grades, issue patterns, recommended fixes, and a walkthrough call.
Public benchmarks are built to compare models against each other on general tasks. They were never designed to tell you whether your support bot resolves tickets, your summarizer keeps the facts straight, or your copilot writes code that runs. That gap is where most production surprises live.
An evaluation built on your own inputs answers the question benchmarks cannot: how does this model behave inside my product, on my users' requests, against my quality bar? That is the number your roadmap decisions should rest on.
Twenty to fifty carefully graded samples will surface patterns that a dashboard of aggregate metrics hides. Averages smooth over the exact failures your users will hit: the one prompt shape that always breaks, the topic where the model quietly invents facts, the version where tone slipped.
Sample-level grading keeps every failure visible and traceable. You see the actual output, the grade, and the reason, so the fix is obvious instead of a guessing game for your engineers.
The first round gives you grades. The second round gives you trends. By the third, your rubric has absorbed every edge case the reviewers found, and your scores are comparable across releases. That history turns model swaps and prompt changes into measured decisions.
Teams that evaluate continuously stop arguing about quality from anecdotes. They point at the benchmark, the rubric, and the trend line, and everyone works from the same evidence.
A batch of 20 to 50 model outputs and whatever rubric or quality bar you use today. If you do not have a rubric, we draft one with you on a short call, and you approve it before we grade anything.
Outputs. We score what your model produced against your rubric. We do not retrain, fine-tune, or reconfigure your model. We tell you exactly where the outputs pass and where they fail.
Public benchmarks answer a different question: how the model does on general tasks. We answer how your model does on your inputs, with your quality bar. That is the difference between a spec sheet and a road test of your actual product.
Yes. Send outputs from both versions generated from the same prompts, and we grade them side by side. You see which version wins, where, and by how much, instead of guessing from a handful of spot checks.
Trained human reviewers grade every sample, with automated checks as a backstop for consistency. A human signs off on the final report before you see it.
That is normal. Reviewers flag every place the rubric is unclear or silent, and we send those gaps back with suggested wording. Your rubric gets sharper each round, and grading gets more consistent with it.
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.