AI annotation & evaluation
Professional evaluation and benchmark data
Build reviewable evaluation data around real work tasks. Projects can prepare complete inputs, questions, reference answers, derivations, sources and scoring rubrics, followed by format adaptation and multi-model pilots. Experience includes finance, regulation, clinical evidence, science, engineering and CAD, including internal enterprise evaluations.
Request a sample ↗What to confirm before requesting a quote
- Coverage
- Regions, languages, date range and available record count
- Fields and attachments
- Field definitions, example records and whether original documents or media are required
- Quality and updates
- Deduplication and missing-value rules, sample-based acceptance criteria and update frequency
- Permitted uses
- Internal research, model training, customer-facing products or redistribution
These are project scoping questions, not a statement that every data type includes all coverage or permissions.
01 / WHAT’S INCLUDED
What’s in the dataset
- Task definitions and complete input files
- Questions, reference answers and evidence locations
- Rubrics, versions and duplicate checks
- Model responses, scores and error analysis where commissioned
02 / USE CASES
Where it can help
- Domain-model and professional-application evaluation
- Document QA and multi-evidence reasoning
- Enterprise regression sets and model comparisons
03 / DELIVERY
Choose the data you need
Receive JSON/JSONL in the agreed task format, full attachments, reference answers, rubrics and a readable review pack.
Validate the material, answers and scoring on a few independent tasks. Task construction and model testing can be commissioned separately.
See a sample format ↗Assess the fit for your project
Establish credible reference answers and scoring before evaluating model performance.
Can our business files become an evaluation set?
Yes. Define the work task, complete inputs, verifiable answers and rubrics, then pilot, adapt the format and freeze a version.
Is model comparison included?
It can be commissioned separately. Agree models, inputs, scoring, costs and failure handling, with original responses, itemized results and error analysis.
Sources and usage
Define the target capability, answer validation, data separation and source use. Format compliance, difficulty validation and external benchmark acceptance are separate checks.
If you plan to train a model, use the data in a customer-facing product or share it with third parties, mention this when requesting a quote so we can check source permissions and restrictions.
Public access does not establish permission for collection, training or redistribution. Naming a platform does not imply affiliation, agency or endorsement.
Read about data sources and rights ↗Want to review a sample first?
Tell us your project objectives and we’ll help you assess and find a relevant sample.