License data for AI evaluation: training, testing and holdouts

What is AI evaluation data?

Evaluation data measures how a model or agent performs on a defined task. For a reconciliation agent, a case could contain the information available to a reviewer and a verified expected final state. For an inspection system, it might pair authorized images with an independently reviewed finding.

A good evaluation case includes an instruction, allowed context, an expected result or rubric, provenance and a way to distinguish an acceptable alternative from an error. Expert disagreement and uncertainty should remain visible; an undocumented reviewer opinion is not universal ground truth.

Compare the uses before comparing the fee

A recipient can request more than one use, but each should be explicit in the proposed scope.

What makes a private case useful?

Held-out cases are reserved from the training material used by the system under test. Buyers may value cases that were not publicly exposed, but an owner should describe what is known rather than promise contamination-free data. A case already circulated to multiple model builders can lose its usefulness as a secret test.

Keep a record of prior recipients, publication history, time period and relevant overlap with training sets where known. Agree who can see the answers, how attempts are logged and when a case should be retired or replaced.

• Define the task and acceptable outcomes before selecting cases.
• Preserve timestamps and distinguish information available before and after the decision.
• Separate case inputs from answer keys and scoring notes.
• Describe difficult, failed and unresolved cases; do not hide selection bias.
• Record expert review, disagreement, sampling method and known limitations.

What evidence supports demand?

Protege markets healthcare evaluation using private patient cases, while Mercor publishes work-based accounting evaluations. These are concrete examples of evaluation activity. They do not establish that every business has licensable cases or that Origin has healthcare datasets available.

The practical opportunity is a set of cases that answers a real buyer’s performance question. That can be a narrower, higher-effort product than a general archive. Ask which failure the buyer wants to measure, how success is scored and how often fresh cases are needed.

Source: Protege: healthcare evaluation

What should the commercial discussion cover?

Compare scope, access, exclusivity, review effort and payment milestones together. Possible arrangements include a fixed evaluation project or periodic delivery of newly reviewed cases. Recurring revenue requires recurring agreed work and demand; it is not automatic.

• Named task, model or product scope and authorized recipients.
• Whether examples, answers, scores or synthetic derivatives may inform training.
• Whether access is controlled or copies are delivered, plus retention and deletion obligations.
• Case acceptance criteria, expert review responsibilities and dispute handling.
• Limits on publication of benchmarks, examples or customer identities.
• Refresh cadence, exclusivity period and the effect of prior licensing.

Describe the task without sending the cases

Start with a task description, approximate case count, time period, available expert outcomes and known restrictions. The buyer requirements worksheet and workflow case manifest help organize those details locally.

Origin can discuss a company-level licensing brief and possible introductions. It does not certify benchmark validity, rights or model performance. Owners can request a review; AI teams can send a requirements inquiry.