Private, task-specific test sets and scoring rubrics built around your product, versioned so every model, prompt or agent change can be compared against the last.
Book a scoping callA list of questions is not a benchmark. Each Logrine benchmark ships as a complete, rerunnable package.
What the model is meant to do, what counts as success, and which failure modes matter to you.
Items with expert-verified reference answers, split into slices so results can be read per scenario, not only overall.
Explicit scoring criteria for correctness, safety, tone, instruction-following and tool use, with the exact judge prompts included.
Code that runs the benchmark end to end and produces comparable results, compatible with common evaluation frameworks.
Measured agreement between the automated judge and human raters, so you know how far to trust the scores.
A starting score for your current model, so the first change you make has something to be compared with.
Research on LLM judges has documented position, verbosity and self-preference biases. We test for them rather than assume they are absent.
Human reviewers label a sample against the written rubric.
The judge scores the same sample, with answer order swapped to test position bias.
We measure agreement and read the disagreements, by slice.
We revise the rubric, re-run, then freeze the judge version used for scoring.
Read more: LLM-as-a-judge: three biases to control before you trust a score
Reports break results down so you can act on them, and state how much a difference can be trusted.
Synthetic datasets and evaluation benchmarks for teams shipping LLMs and AI agents, delivered with the evidence.