We turn what practicing professionals know into high-quality data for training and evaluating AI models.
Practicing professionals show how they reason. We turn it into tasks, reference answers and rubrics your models can learn from.
| Criterion | Points |
|---|---|
| Tests the exit multiple against the peer range provided | 3 |
| States a clear base-case view with reasoning; a different conclusion earns full credit if the evidence supports it | 3 |
| Names what evidence would justify a higher multiple | 2 |
| Connects leverage to the risk of a lower exit multiple | 1 |
| Uses only the figures provided; no unsupported numbers | 1 |
| Total | 10 |
The knowledge that separates a good model from a great one lives in the judgment of people who've spent years getting good at something. Our job is to turn that judgment into structured, verifiable signal.
The work is done by working professionals with verifiable credentials, such as licensed clinicians, practicing lawyers and buy-side analysts, not general annotators.
We shape tasks, reference answers, and evaluation criteria around your model’s target capabilities and intended use cases.
Every batch goes through human QA and consistency checks, and ships with documentation in the format your pipeline expects: tasks, reference answers, rubrics and metadata.
From scoping to first delivery in weeks, not months.
Tell us your training objective. We'll discuss scope, data requirements, and next steps.
Domains, sample formats, QA process and turnaround times, in one PDF.