Business data for AI agents
Business dataset types for AI: choosing the right records
Last updated /Business data for AI agents
Business dataset types for AI are a selection problem. Support records, CRM histories, documents, transactions, and workflow traces answer different questions. A team that needs to see how a ticket was resolved will not get that from invoices. A team that needs to see whether a deal stalled will not get that from a policy manual. This page is a buyer matrix: use, fields that matter, and the limitation that usually kills the request. The holder-side guides linked below explain the same shelves from the company that has the files. Read both. Do not treat their pages as your specification.
The procurement sequence that uses this choice is how a buyer sources proprietary business data. Why a sequence of work can teach an agent is how business data trains AI agents to do real work. The holder overview of categories is the kinds of operating records AI teams ask about. Send a real need through the buyer request, not by pasting this matrix into a form as if it were a purchase order.
Why the wrong shelf wastes diligence
Diligence is expensive when it is spent on a file that never contained the ending you care about. Choosing the type first is how you avoid reviewing a document dump for a tool-use question, or a transaction table for a judgment question. The limitation column is part of the choice. A type that usually cannot be separated from a customer's private content may still be the right type, and it may still be one you should not request in raw form.
A selection matrix, not a menu of stock
Support records
Use them when the lesson is diagnosis, escalation, or a resolution that held. Fields that matter: the symptom in the company's words, the steps tried, the coded ending, and whether the case returned. Limitation: customer payloads, account numbers, and chat-tool terms. The holder view is how support tickets and chat logs can be licensed. A software vendor's version of the tenant problem is what a SaaS company already generates that can train AI.
CRM histories
Use them when the lesson is how an opportunity moved: qualification, a promise, a stall, a result. Fields that matter: stage changes and notes about the work. Limitation: the contact directory is not the history. The holder view is how CRM and pipeline records can be licensed.
Documents and procedures
Use them when the lesson is a rule the company wrote and still follows, including exceptions. Fields that matter: authorship, currency, and the decision notes attached to the rule. Limitation: client exhibits, vendor manuals, and drafts nobody used. The holder view is how internal documents and playbooks can be licensed.
Transactions
Use them when the lesson is a commercial event: what was requested, what was delivered, whether it was disputed. Fields that matter: the event and a status. Limitation: card numbers, bank details, and full identity. The holder view is how orders and invoices can be licensed. A store's exception around an order is what an online store already generates that can train AI.
Workflow traces
Use them when the lesson is an ordered action: state, move, result. Fields that matter: what the actor could see and what they did. Limitation: credentials, screenshots of other people's sessions, and logs that are only heartbeats. The holder view is how workflow and process records can be licensed.
Selection mistakes
- Requesting all five types to seem thorough. You will diligence noise, and a holder who can permit one shelf may refuse the bundle.
- Confusing a system brand with a type. The brand does not tell you whether outcomes were stored.
- Ignoring the holder guide's refusal list. If they say the contact list is not the product, do not write the brief around the contact list.
- Treating this matrix as available supply. It is a way to choose. It is not a claim that each cell is on a shelf we can ship.
How a type becomes a request
Unlockt, LLC operates Big Data/Little Guy. Name one type in the buyer request or compare a written need with profiles via the marketplace matching description. We match descriptions. We do not promise that the type you chose is populated.
After you choose, write the brief before you ask for a sample. What belongs in an AI dataset requirements brief is that next gate. The buyer overview remains the page about reviewing a profile, not a second copy of this matrix.
Questions people ask
Which type is best?
The one that contains the decision and the ending you need to teach or test. There is no universal ranking.
Are the holder pages the same as this one?
No. Those pages tell a company what it might describe and what it should withhold. This page tells a buyer what to request and what limitation to expect.
Can I ask for a mix?
Yes, if each type has a reason. A mix without a reason is a dump. Say which field from each type your acceptance test needs.
Do you have these types in stock?
No. This is not an inventory. A profile appears only when a holder has allowed a description to be discussed.
What if the type I need is mostly personal data?
Expect a narrow slice or a no. Do not write the brief so that it only works with the fields a holder should remove.
Where do I send the choice?
Into a requirements brief, then the buyer request. Do not skip the brief because the type name sounded right.
Describe the data you need
Tell us the use, the records, and the limits. A request is not a promise that a matching dataset is available.
Tell us what data you need