License Business Data for AI Evaluation: Seller Guide

To license business data for AI evaluation, offer records that can support a defined task and a defensible judgment of the result. Explain what information was available, what counted as acceptable work, and what remains excluded. Then agree the buyer's evaluation rights separately from any permission to train models or redistribute the material.

Sell a task description before an archive

A purchaser asking for evaluation data needs to know what can be tested. Recommend starting with a short task statement rather than a description of your total storage.

For a hypothetical service operation, the task might be deciding whether a repair request can be approved from the relevant policy and work notes. The evaluation would need the request, the applicable policy version, the available evidence, and the criteria for approval or escalation.

That example is a proposed dataset design. It is not a Garrow client case, an actual buyer requirement, or a measured result.

Anthropic's engineering guide to agent evaluations describes evaluations in terms of tasks, trials, graders, transcripts, and outcomes. It discusses using different grading methods and the difficulty of judging agents that act across tools. A business archive needs a similarly explicit testing purpose before its contents can be assessed as evaluation material.

Distinguish the uses the purchaser wants

Ask whether the buyer intends to measure a model, teach it, create a practice environment, or collect human feedback. The answer affects the records, preparation, and permitted-use discussion.

Micro1's partnerships page describes operational workflows and business tasks as material for frontier AI development. That provides evidence of a market category. It does not establish that any particular archive will be accepted as a benchmark.

Recommend putting the intended use in the proposal and the license schedule. If the purchaser wants both training examples and an evaluation collection, ask how it will separate them and whether the grants differ.

An agreement saying “research” may leave important commercial questions unanswered. Have counsel review what is allowed, what recipients can do, and whether the prepared tasks can be supplied to others.

Our business data licensing process covers the broader transaction. This guide concerns the task-level preparation questions within that process.

Reconstruct what was known at the decision point

A historical record may contain information added after the work was completed. Recommend deciding where the test begins and preserving the boundary between prior context and later outcome.

In the hypothetical repair task, the model should see the request and evidence that an operator could reasonably use at the relevant moment. A later confirmation should not silently become part of that initial context.

Ask the operations reviewer to distinguish contemporaneous notes from later corrections. Record the policy version and any changes in process. If that reconstruction is uncertain, flag the task for review instead of inventing missing history.

This preparation question can reveal that a proposed task is not recoverable from the archive. A final status field may not preserve why the decision was made or what evidence was available. Describe that limitation to the purchaser.

Define success before selecting flattering examples

Recommend asking experienced staff to describe the acceptable outcome and the unacceptable actions. Avoid selecting only cases that make the workflow look simple.

For the hypothetical approval task, success could mean a supported approval, a supported rejection, or an appropriate request for missing information. If several outcomes are reasonable, describe that range rather than forcing the historical outcome to be the only correct answer.

Distinguish factual correctness from style preferences. A polite explanation can still make an unsupported commitment. A concise response can be correct even if it differs from the original employee's wording.

Write the review criteria before asking a model to attempt the task. That gives the purchaser a way to inspect the package without changing the definition of success whenever the model produces a different result.

Build an evidence sheet for each task

Recommend creating a consistent task sheet. Keep the production records separate from this prepared description so that revisions do not overwrite history.

Do not turn an unresolved case into a clean benchmark by guessing the answer. Ask the buyer whether ambiguous tasks should be excluded, retained as uncertainty tests, or reviewed further. Label the chosen approach.

Avoid exposing answers in the task input

As a practical preparation rule, review whether the proposed input reveals the outcome through filenames, status fields, pasted resolutions, or later comments. Those details may be appropriate in an answer file while being inappropriate in the evaluated system's context.

Recommend keeping expected outcomes and reviewer notes separate from the test input. Also check whether closely related records appear in both proposed training material and evaluation material.

The buyer should explain its own separation and contamination controls. Your company can describe duplicates, reused templates, repeated incidents, and source overlap that you know about. Do not promise that no evaluated model has previously seen any part of the material unless there is evidence supporting that statement.

A dataset being private inside your company is not proof of everything that may have appeared elsewhere. Keep the claim narrower than the evidence.

Give reviewers a way to disagree productively

Recommend a written procedure for uncertain outcomes. Ask reviewers to record the reason for a judgment and the source supporting it, rather than merely applying a pass or fail label.

Where the original employee's action conflicts with current policy, decide whether the task tests historical practice or current rules. Those are different evaluations. Preserve the distinction in the package description.

If the record appears mistaken, do not silently present the mistake as ground truth. Explain what was checked, who reviewed it, and whether the proposed answer has been corrected. Ask the purchaser whether it wants such cases at all.

A reviewer should also be able to say that the evidence is insufficient. That is more useful than a confident label added to make the package look complete.

Describe coverage and limits honestly

Recommend describing the work represented, the sources omitted, the difficult cases included, and the selection method. A prepared evaluation package should not imply coverage of a whole industry simply because it contains authentic business tasks.

Ask whether unusual situations, unresolved work, changed policies, or missing attachments are present. Record any selection decisions that favor straightforward cases. The buyer can then assess whether the collection serves its intended measurement.

Keep claims about model performance separate from claims about data quality. Passing the prepared tasks does not establish suitability for every real-world workflow, and an operational archive is not automatically a validated benchmark.

If the buyer reports results back to you, ask which model, tool setup, task version, and review method produced them. Avoid quoting a result without those boundaries.

Resolve licensing and privacy before preparing delivery

Task preparation can still involve confidential records. Review rights to license company records and privacy before selling data before sending a sample.

Agree who pays for labeling, reviewer work, and corrections. Ask who owns the prepared task descriptions and whether your company can reuse them. Decide whether later workflow changes or new evaluation tasks require another arrangement.

Public buying material such as Polyshares' operational-data description does not replace an evaluation-specific agreement. The purchaser must assess the proposed records and permitted use.

Separate technical relevance from commercial terms

Treat an interesting task description, a technically usable sample, and a paid agreement as separate milestones. Ask the purchaser to assess task suitability, specify the rights it needs, and set out acceptance and payment conditions before committing to preparation work.

The recommendations in this guide help you describe and question an opportunity. They are not a proven pricing method or a promise that a buyer will fund dataset preparation.

Describe your operating records through Garrow's intake to explore a potential introduction without sharing task files.