Domain experts grade your model side by side with other frontier models against key rubrics. The report shows where it can improve, why, and what data is needed to close the gap.
Your model attempts each task. Experts grade the work where judgment matters, and automated tests check the rest.
From our collections or from you, scoped to the work you want to measure.
The model makes one or more attempts at each task.
Experts grade against task-specific rubrics, or choose the better of two answers.
Where a program can check the result, tests grade it too.
Expert judgment for the work tests can’t fully measure.
Clarity, design and scalability, graded the way a senior engineer reviews a pull request.
Generated interfaces rated by senior designers against a craft rubric.
Agents working in repositories and terminals, graded by tests on the final state.
Task sets calibrated to a difficulty you set, kept private to your lab and checked for leaks into pretraining.
Experts choose the better of two answers in side-by-side, blind preference tests, and run the code where it applies.
Reference-free rubrics for tasks with no single right answer, such as spec generation and deep research.
Task quality, verifier accuracy and rubric quality in an existing dataset or benchmark.
Coding sessions in real repositories rated turn by turn and as a whole, with written reasons.
Step-by-step grading of how a model reasons through code.
Benchmarks that test whether agents find and fix vulnerabilities.
A real rubric from our public UI Quality Bench. On a client project, the rubric is authored for the task.
Each dimension is scored from 1 to 5
Ten frontier models answered the same 35 hard prompts, for 350 designs. Three senior designers from our network rated every design on their own, with the files anonymized and shuffled. Then each designer ranked the ten models for every prompt.
Agreement between the three designers: Kendall’s W = 0.91.
Every eval ends in a clear report on what to improve and the data that will get you there.
Scores per rubric dimension, with error margins, for your model and the frontier models next to it, on the same tasks.
Failure categorization behind each weak dimension.
The data to collect and the training that would close each gap.
Evaluation work for a foundation model company and a leading hyperscaler.
to 1,000+ expert-evaluated tasks, across 25 parallel queues
Task-specific rubrics that turned clarity, scalability, and design into criteria a reviewer can verify, for benchmarking across models.
Read the storyapproval rate, up from 54%
A design-first workflow for a leading hyperscaler evaluating front-end generation. Designs were approved before any code was written.
Read the storyEach eval is graded by experts from our network of 400,000+, matched to the domain of the task. Dual-expert review and consensus settle disagreements.
We scope the tasks and the rubric, run the grading, and deliver the report.
Your team runs the eval; our vetted experts do the grading in your tools.
A private task set calibrated to a difficulty you set, to track your model over time.
A live leaderboard on private collections that stay unpublished: SWE-Lancer, Terminal Bench 2.1 and 3.0.
Ten frontier models, 35 hard prompts, three senior designers ranking every result blind.
Tell us the model and the capability. A researcher will scope the tasks, the rubric and the report with you.