How to Sell Your Company's Data to AI Companies

What kind of company data do AI buyers want?

Think about a business that repairs commercial equipment. Its invoices show what customers paid for. Useful, perhaps, but not particularly revealing. Now imagine it also has ten years of fault reports, technician notes, replacement parts, repeat visits, and final repair outcomes. Those records might show how an experienced technician diagnosed thousands of real problems.

That second archive tells a story an AI system could potentially learn from. And similar histories exist in almost every established business.

A software company may have support tickets connected to bug reports and code changes. A manufacturer may have production problems, engineering approvals, rework decisions, and quality results. A sales organization may have conversations, proposals, objections, and records of why deals were won or lost.

The strongest opportunities often involve a problem, the work done to solve it, and a recorded outcome. That's more informative than a folder of documents with no context. It also gives a buyer a way to understand what the data could help an AI system learn or evaluate.

This is not just a theory about future demand. micro1's company data program explicitly lists operational documentation, CRM data, project histories, and decision-making patterns. Handshake AI has also announced a program for licensing records of how organizations work.

None of that means those companies want every type of record. Demand differs by industry, buyer, and the specific work captured. The first job is to find the part of your history that might actually be useful.

How much could your data be worth?

There is real money in this market, but published numbers need some perspective.

Handshake AI says qualifying data partnerships generally range from $100,000 to $4 million, depending on the records involved. micro1 publishes $100,000+, $500,000+, and $1 million+ partnership tiers. These are buyer-advertised program amounts, not the average payment a business should expect or a promise that an applicant will qualify.

For an example of money actually received, cielo24's reported deal was worth hundreds of thousands of dollars. Forbes also reported that SimpleClosure, a company that helps startups wind down, had handled nearly 100 data transactions in the preceding year, with typical payouts of $10,000 to $100,000 per company. Those were wind-down situations, not directly comparable to an established company licensing years of ongoing operations.

So what determines the price? In practice, a buyer will want to understand how much usable history you have, whether records connect across systems, how unusual the work is, whether outcomes are recorded, and what permissions you can actually grant. The biggest factor may be simpler: does a buyer need this particular data right now?

An archive can look impressive and attract no interest. A smaller, specialized collection could be valuable to the right buyer. Until somebody has reviewed your specific records and expressed interest, any dollar figure is an estimate.

How to sell your company's data, step by step

1. Start with an inventory, not an export

You do not need to gather ten years of files to get started. In fact, you probably shouldn't.

Begin by listing the systems your company uses and what each one contains. For example, a software business might list Zendesk for customer support, Jira for development tickets, GitHub for code, Slack for team discussions, and Google Drive for internal documentation.

For each system, note roughly how far back the usable records go, the approximate volume, and whether information can be connected to a real business outcome. Add one sentence about whose information is likely to appear there: employees, customers, suppliers, or third parties.

A first-pass inventory might look like this:

Illustrative example, not an actual company's inventory.

This is enough to begin a serious conversation about possible value. It is not a request to upload the underlying messages, customer lists, or code.

2. Work out what the records could teach an AI

A list of tools is useful, but buyers need to know why the history matters.

“We have seven years of Zendesk, Jira, and GitHub data” tells them what you store. “We can trace customer-reported software problems from the original ticket through engineering investigation, code changes, and final resolution” tells them what the records show.

Try writing a short description of one workflow. Who starts it? What information does the team use? What decisions get made? What happens next? How do you know whether the work succeeded?

If you cannot trace a workflow from start to finish, that does not rule out a deal. It simply means you should describe what you can show, rather than promising a complete history you may not have.

3. Check what you have the right to share

This is where a promising opportunity can run into trouble. The records may live in your company's account, but they can still contain customer information, employee conversations, material owned by a contractor, or data your business agreed to keep confidential.

Before giving anyone access, review the obvious constraints. What do your customer and vendor agreements allow? Does the archive contain personal, health, financial, or other regulated information? Is third-party intellectual property mixed in? Would licensing part of the record expose trade secrets or give a future competitor an advantage?

Buyers may offer to remove names and other identifying details, but de-identification is not the same as permission. The fact that a buyer can clean a dataset does not automatically mean your company can lawfully transfer it in the first place. Handshake AI's program materials emphasize scoping, exclusions, and de-identification, while law firm Sidley Austin identifies ownership, permitted use, privacy, and downstream rights as central issues in training-data agreements.

You do not need an expensive legal review of every document just to ask whether the market is interested. But you should flag likely problems early and get qualified advice before disclosing sensitive records or signing an agreement. Some opportunities will need a smaller dataset, exclusions, or a different structure. Others may not be worth pursuing.

4. Find buyers that want your type of data

You can approach data programs directly. For example, both micro1 and Handshake AI have public company-data programs. They provide evidence that a market exists, but they are starting points, not an exhaustive list of buyers and not a guarantee of fit.

There are also intermediaries that source or prepare datasets for AI developers, and seller-side representatives who help businesses assess an opportunity and approach relevant counterparties. These roles are not identical. A company offering you a price may be buying the rights for its own program, arranging a license for someone else, or receiving a referral fee if it introduces you to another buyer.

Ask who the actual contracting party will be, who can ultimately use the data, and whether the firm is acting for a buyer or for you. If someone approaches you first, you do not have to accept their proposed process, timeline, or exclusivity terms on the spot.

Where it makes commercial sense, getting interest from more than one relevant party can help you understand whether an offer is competitive. The aim is not to blast every buyer with the same pitch. A short, specific opportunity sent to a few plausible buyers is more useful than a mass email about “millions of valuable company records.”

5. Send a short opportunity summary, not your dataset

A buyer's first question is usually whether the asset could fit its needs. You can often begin that conversation without sharing confidential material.

An initial summary should cover what the business does, which workflow the records capture, the systems involved, approximate years and volume, whether outcomes can be traced, and any known limitations. It should also say that access to underlying records would require a separate review and agreement.

For example:

Subject: Potential software workflow dataset

We're exploring a possible license of several years of operational records from an established software business. The archive includes customer support, engineering tickets, and code history, with links between reported issues, investigation, fixes, and outcomes. We have an initial inventory and are checking the relevant rights and privacy constraints. Is this the type of company data your team is currently evaluating? If so, what information would you need for an initial fit assessment?

No raw records attached. No promises about rights that have not been checked. Just enough information for the right buyer to decide whether to keep talking.

If the buyer is interested, the next steps may include a deeper inventory, a discussion of technical requirements, and a carefully scoped sample or review process. Do not confuse signing an NDA with agreeing to unrestricted access. Specify what may be reviewed, by whom, and for what purpose before transferring anything sensitive.

6. Compare the deal, not just the dollar amount

Suppose one buyer offers $300,000 for a limited, non-exclusive license to a defined archive. Another offers $500,000 but wants exclusive rights, future updates, broad sublicensing, and permission to use the data in any commercial model. Those are not simply two prices for the same product.

Before signing, make sure the written agreement answers these questions:

• What exactly is included? Which systems, years, records, exclusions, and possible future contributions?
• Who can use it, and for what? Training, testing, commercial products, onward transfers, and use by other companies are different permissions.
• Is the license exclusive? Could you make a similar deal later, or does this contract close that door?
• When do you get paid? On signing, delivery, acceptance, or later milestones? Who pays for exporting and preparing the data?
• Who handles privacy and security? What must be removed, who performs that work, and what happens if there is a problem?
• What survives termination? What must be deleted, and can models already trained on the data continue to be used?
• What are you liable for? Pay particular attention to warranties about rights you may not be able to guarantee.

These questions are not theoretical. A model trained on licensed records may keep operating after the buyer deletes its copy of the original files. Contract terms need to address that reality before you agree to a deal.

Once the scope and terms are settled, the seller and buyer can plan the approved export, preparation, security checks, and delivery. Do not let the mechanics of getting the files across become a substitute for agreeing on what the buyer is entitled to receive.

When selling your data is the wrong decision

A six-figure offer can sound compelling, but some information is worth more inside your business than it would earn in a one-time license.

If the records reveal pricing strategy, a proprietary method, valuable customer relationships, or trade secrets that could be used against you, you should consider whether those risks can genuinely be controlled. The same goes for archives full of sensitive information you do not have authority to disclose.

Sometimes the right answer is to exclude a system, narrow the time period, license only a specific workflow, or decline the deal. You do not have to license everything to license something, and you do not have to make a deal at all.

Where to start

If you're curious whether your company has a real opportunity, start by making a simple inventory of your systems and picking one workflow that has a meaningful history. You should be able to explain what the business does, what records it has, how far they go back, and what a buyer might learn from them, without sending a single confidential file.

Then test whether buyers are interested before committing your team to a large export or a lengthy legal project.

DataDeals works on the seller's side. We help companies identify promising records, understand buyer demand, prepare an opportunity for market, and compare offers and terms. We do not buy your data, and you stay in control of whether to proceed.

If you'd like a second opinion on your company's opportunity, request a free 20-minute Data Opportunity Review. No raw data or files are needed to get started.