Business data for AI agents

How to source proprietary business data for AI

Last updated November 7, 2026/Business data for AI agents

How to source business data for AI, on the buyer side, is a procurement sequence. You write what the model or agent is supposed to learn or be checked against. You choose which kind of record could carry that lesson. You review a controlled sample for provenance and holes. You ask counsel whether the permission covers training, evaluation, outputs, and the parties who will touch the file. You accept a delivery only after your receiving team can see that the files match the brief. This page is the sequence. It is not a storefront, and it is not a promise that a holder with your exact history is waiting.

The five guides under this one take a single gate each. Choosing records is how a buyer chooses among business record types. Writing the need so a search can fail honestly is what belongs in an AI dataset requirements brief. Looking at a sample without grading your model is how to diligence a business dataset before licensing. Questions for counsel, not clauses, are questions buyers should ask counsel before a training license. What the receiving team verifies is what a receiving team checks before accepting a dataset.

Why operating records can teach an agent at all is how business data trains AI agents to do real work. To describe a need without browsing a catalog, use the buyer request on this site. Profiles, when they exist, are discussed from the buyer overview of how a profile is reviewed and the marketplace description of how a profile is matched. A profile is a description. It is not inventory we are listing as in stock.

Why sourcing starts from the failure you need to fix

Teams that already have public text still miss the action: the tool call, the refusal, the outcome. They go looking for proprietary records because the public web ended. The search goes wrong when it starts from a format, a brand of software, or a hope that someone will upload a year of everything. A useful search names the decision the system must get right, then asks which record would show that decision and its ending.

Holders describe the same shelves from their side. Their category guides are not your shopping list. Read them to see what a careful holder will refuse to include. Then write your brief so those refusals are expected, not a surprise at acceptance.

The procurement gates, in order

1. Requirements. Intended use, industry, modality, history, geography, schema, and what must be excluded. A brief that cannot be failed is not a brief.
2. Source type. Support threads, CRM histories, documents, transactions, or workflow traces. Pick the type that contains the action and the ending. Do not ask for all five because the storage bill is someone else's.
3. Diligence. A controlled sample: where it came from, what is missing, what is duplicated, whether outcomes are real, whether the slice represents the job, and whether the answer has already leaked into the prompt.
4. Licensing. Your counsel asks whether training, evaluation, affiliates, subprocessors, retention, and the holder's own right to permit the use are covered. We do not draft those clauses.
5. Handoff. Your receiving team checks format, schema, documentation, transfer, version, and integrity, then accepts or sends it back. A file that arrived is not a file that passed.

What this hub will not do

It will not name a dataset we are holding for you. It will not quote a price. It will not certify a supplier. It will not tell you the license is valid in your jurisdiction. What AI data licensing means for a company is the holder-side definition of a permission. Read it so you know what the other party thinks they are granting. Then have counsel read the draft.

Sourcing moves that waste the search

• A brief that says all operational data from any industry. Nobody can match that, and anyone who claims to has not read it.
• Skipping the sample because the profile prose was fluent. Prose is not provenance.
• Grading your model on the sample and calling that diligence. Diligence here is about the file. Model performance is a later experiment, on a split you control.
• Accepting a drive because the transfer finished. Integrity, schema, and the exclusions in the brief are still unchecked.
• Treating a match as a guarantee. A profile can resemble a brief and still fail rights, completeness, or your own acceptance tests.

How a request is not a catalog

Unlockt, LLC operates Big Data/Little Guy. We compare a stated need with profiles holders have approved for discussion. The comparison can be empty. Empty is a result. Describe the need on the buyer request form or, if you are checking whether your own company holds something, the free data value check is the holder-side door. Do not use one form to do the other job.

We do not attach raw archives to a first reply. We do not promise a modality, a geography, or a schema we have not actually got a holder to describe. If a later transfer happens, your team still runs the acceptance checklist. Our role is the introduction and the profile, not a warranty that the bytes match your brief.