01
Realistic domain tasks
Specialists author prompts and scenarios around the workflows, constraints, and difficult decisions your model will encounter. Define coverage by task type and difficulty.
Model evaluation / Expert-authored datasets
Real tasks.
Expert judgment.
Measure your model against work that matters in your domain. We coordinate specialists to create realistic tasks, reference answers, and grading criteria for a reusable evaluation dataset.
What we create
A benchmark grounded in the work.
Useful evaluation starts with realistic problems and a defensible definition of success. We turn domain knowledge into tasks your team can use to assess model behavior across versions.
02
Validated reference answers
Create reference responses and supporting reasoning. Review them for correctness, completeness, and assumptions, including cases with more than one acceptable answer.
03
Explicit grading criteria
Define what a successful response must contain, what errors matter, and how to assign scores. Include rubric guidance for partial answers and ambiguous cases.
Execution & quality assurance
Qualified specialists.
Reviewed evaluation sets.
We manage contributor qualification, task authoring, reference validation, and review decisions so your team receives a coherent evaluation dataset.
- 01
Scope the benchmark
Agree on the domain, target workflows, coverage, difficulty, and scoring requirements.
- 02
Qualify & calibrate
Select specialists for the domain and calibrate authors and reviewers on sample tasks and grading criteria.
- 03
Author & review
Develop tasks, validate references, resolve disagreements, and refine rubric language through quality review.
- 04
Version & deliver
Check the delivery schema and package the accepted tasks, references, rubrics, and version information.
What you receive
The tasks.
The answers.
The grading logic.
Receive a structured evaluation set your team can run and reuse, with the context needed to interpret its results.
- Task set and coverage
- Stable task identifiers, prompts, domain categories, difficulty labels, and agreed task metadata.
- References and rubrics
- Reviewed reference answers, supporting reasoning, scoring criteria, and handling rules for alternative valid responses.
- Versioned dataset package
- Structured files in your required schema, with dataset versions, review status, and records of material changes.
Build your domain evaluation
Show us the work
your model needs to do.
Share the domain, intended users, target tasks, and how you evaluate today. We will scope the specialists, coverage, review workflow, and delivered evaluation set.
Scope your evaluation dataset