How to sell your company's data to AI companies
AI companies are already paying businesses for access to certain kinds of internal data: support histories, software-development records, sales workflows, operational decisions, and more.
The interesting part is not simply how many files you have. It is what your company's history shows about how real work gets done.
DataDeals helps you identify what may be valuable, understand what the market says about it, and decide how to take it to market from the seller's side.
Free calculator · About 2 minutes · No raw data upload
Many deals are structured as licenses rather than outright sales, so a company may keep ownership while granting specific usage rights.
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This isn't hypothetical. Companies are already licensing internal data.
Buyer programs, reported offers, and completed deals now give us a real market to look at.
CURRENT MARKET SIGNALS
Handshake AI — $100K–$4M
Current published range for qualifying company-data partnerships.
Source
micro1 — $100K+ / $500K+ / $1M+
Published tiers for qualified, large-scale, and highly unique operational-data partnerships.
Source
Warmly — up to $300K
The Information reported that Mercor offered up to $300K for Warmly's code and operating records. Warmly declined.
Source
cielo24 — "hundreds of thousands of dollars"
Forbes reported that the winding-down company monetized 13 years of Slack, Jira, email, and Google Drive history.
Source
These numbers are not directly comparable. They are different kinds of evidence from different companies and deal structures.
At the far end of the market: a $10M selected bid
Spirit Airlines' de-identified enterprise data was auctioned in bankruptcy in 2026.
Google was selected at $10 million, Mercor was the alternate bidder at $7.5 million, and micro1 later filed notice of a $12.5 million competing bid.
As of September 22, the sale had not been approved.
Spirit is an outlier, not a useful comp for an ordinary business. But it shows how seriously buyers can value large, difficult-to-recreate operating histories.
What buyers actually want
Most companies do not have a file called valuable-ai-training-data.csv.
They have years of ordinary work:
Support tickets. Slack threads. CRM history. Code changes. Approvals. Project records. Reconciliations. Decisions. Exceptions. Outcomes.
The valuable part can be the trail between those records.
A customer asks a question. Support investigates it. Someone discusses it internally. Engineering makes a change. The customer issue gets resolved.
That sequence shows how real work happened.
- Customer question
- Support ticket
- Slack discussion
- Jira issue
- Code change
- Resolved customer
Examples of data buyers may care about
Tickets, escalations, internal investigation, QA, and final resolutions.
Issues, code changes, pull requests, reviews, debugging, tests, and releases.
Customer conversations, qualification, objections, approvals, proposals, and won/lost outcomes.
Scheduling, dispatch, fulfillment, projects, exceptions, approvals, inventory, and SOP execution.
Reconciliations, approvals, forecasting, close processes, reporting workflows, and exception handling.
Underwriting, manufacturing, scientific work, technical design, legal review, field service, healthcare operations, and other expert workflows.
The question is not:
How much data do we have?
It is:
What does our operating history show that would be difficult for someone else to recreate?
What makes one company's data worth more than another's?
There is no universal pricing formula.
But the same characteristics keep showing up in current buyer programs and reported deals.
1. Useful history
Several years of usable records can capture more edge cases, decisions, exceptions, and outcomes than a short snapshot.
2. Complete workflows
A ticket or document can be useful. A sequence showing the request, investigation, decision, action, and result can be much more informative.
3. Clear outcomes
Won or lost. Resolved or escalated. Approved or rejected. Passed or failed.
Outcomes show what happened in the end.
4. Hard-to-recreate expertise
Years of underwriting decisions, engineering work, manufacturing exceptions, or technical problem-solving may be difficult to reproduce from public information or synthetic examples.
5. Clean records
More data is not automatically better data.
Broken timestamps, missing history, duplicates, disconnected systems, and messy exports can reduce usefulness quickly.
6. Clear rights
A great dataset with unclear ownership or heavy contractual restrictions can be difficult to license.
7. Real buyer demand
Even good data needs someone who wants it.
The most commercially interesting opportunity combines strong underlying data with a real buyer need. For published pricing evidence and its limits, read how much company data is worth.
What could your company's data be worth?
The public market is wide.
Current programs and reported opportunities run from tens of thousands of dollars into the millions.
There is no dependable average because the underlying assets are so different.
A buyer program, a rejected offer, a completed transaction, and a bankruptcy auction are not apples-to-apples. That is exactly why DataDeals does not average them into one fake "market price."
- BUYER PROGRAM
Handshake AI
$100K–$4M - REPORTED OFFER
Warmly
Up to $300K - REPORTED TRANSACTION
cielo24
Hundreds of thousands - ENTERPRISE AUCTION
Spirit Airlines
$10M selected bid
Get a first estimate for your company
The DataDeals calculator starts with your company size and retained history, then looks at the systems you use, the work happening inside them, whether outcomes are visible, and other signals that help determine whether the opportunity appears commercially interesting.
It is a starting point, not an appraisal or buyer offer.
Free · About 2 minutes · No raw data upload
Before you sell anything, know what you can actually license
The fact that data sits inside your systems does not automatically mean every part of it can be licensed.
A few questions matter early.
Who created it?
Employees, contractors, customers, vendors, licensed data providers, and third-party software can create different rights issues.
What do your contracts say?
Customer agreements, vendor agreements, contractor terms, and software licenses may restrict how information can be reused.
What sensitive information is mixed in?
Customer identities, employee information, health data, financial information, credentials, and confidential commercial information may need to be excluded, transformed, or reviewed.
Is third-party IP included?
Contractor code, licensed databases, external documents, customer materials, media, and other third-party content may require separate rights.
What would the buyer actually be allowed to do?
Training, evaluation, benchmarking, commercial deployment, sublicensing, onward transfer, and derivative datasets are different rights.
De-identification can reduce some risks. It does not automatically clear every privacy, contract, or confidentiality issue.
You do not need to finish every legal question before deciding whether an opportunity is worth exploring.
You do need to know if an obvious blocker exists.
How do you actually sell company data?
For most businesses, the process looks something like this.
1. Work out what you have
List the systems, years of history, approximate scale, important workflows, and where outcomes are visible.
2. Find the part a buyer may care about
"We have a lot of data" is not a useful pitch.
"We have eight years of support cases tied to internal investigation and final resolution" is.
The second tells a buyer what the data actually teaches.
3. Check the obvious constraints
Identify sensitive information, third-party content, contract issues, and anything that may need to be excluded or de-identified.
4. Package the opportunity
A buyer should quickly understand:
- what the company does;
- what process the data captures;
- which systems are involved;
- how much history exists;
- what outcomes are visible;
- why the records are difficult to recreate;
- any known rights issues;
- how access could work.
5. Choose how to go to market
There are three basic routes.
Direct buyer
Simple when you already know the right buyer. The risk is treating one buyer's offer as the whole market.
Marketplace or intermediary
Can bring buyer relationships and infrastructure. Understand fees, sublicensing, exclusivity, and who ultimately controls scope and pricing.
Seller-side representation
Can help identify, package, position, compare routes, and negotiate an opportunity.
DataDeals works on the seller's side. See how the seller-side process works and who runs DataDeals before choosing how to proceed.
The headline price is only half the deal
A $500K offer is not automatically better than a $300K offer.
It depends on what you are giving the buyer.
Before signing, compare:
Upfront payments, recurring payments, royalties, minimum guarantees, payment timing, and fees.
Which systems, years, records, volume, exclusions, and whether future data is included.
Exclusive or non-exclusive. Permitted uses. Term. Renewal. Sublicensing. Onward transfer.
De-identification, security, retention, deletion, audit rights, and approval of future data packages.
What are you promising about ownership, privacy, and third-party rights? What happens if one of those promises is wrong?
Deleting the original files is not necessarily the same thing as undoing training that has already happened.
The agreement should be clear about whether trained models, derived datasets, or other outputs can continue to be used after termination.
There is no universal answer. It needs to be negotiated.
What about your company?
You do not need a finished data room to find out whether this is worth pursuing.
Start with a few basic questions:
- Which systems are in scope?
- How many years of usable history do you have?
- Can important workflows be followed from start to finish?
- Are final outcomes visible?
- What contracts or third-party rights may apply?
- What sensitive information may need to be excluded?
If you are still at the "could this apply to us?" stage, start with the calculator.
If you already think you have something interesting, request a free 20-minute Data Opportunity Review.
We will help you answer:
- What data looks most promising?
- Does the opportunity look real?
- What should you do next?
No files needed. You do not need to run the calculator first.
If the opportunity looks real, DataDeals can help package it and take it to market.
Frequently asked questions
Can a normal operating company really sell data to AI companies?
In some cases, yes. Current buyer programs actively seek proprietary operating data from established businesses. Whether your company has a real opportunity depends on the records, usable history, workflows, outcomes, buyer demand, and whether the data can be licensed.
Is this usually a sale or a license?
Often it is a license. That can allow a company to keep ownership while granting specific usage rights. Outright sales, non-exclusive licenses, exclusive licenses, and recurring arrangements can have very different economics.
What kinds of company data are buyers looking for?
Current programs mention software-development history, support workflows, CRM and sales records, finance processes, logistics, operations, manufacturing, and specialized expert work.
The common thread is real work that is difficult to reproduce.
How much can company data be worth?
Public references currently range from tens of thousands of dollars into the millions for qualifying opportunities. There is no reliable average. Value depends on the actual asset, buyer demand, rights, scope, exclusivity, and deal structure.
Do we lose ownership if we license the data?
Not necessarily. A license can allow the seller to retain ownership while granting defined rights to a buyer. Pay close attention to exclusivity, sublicensing, duration, permitted use, and what survives after termination.
Should we just contact buyers directly?
You can. For a simple opportunity, that may make sense.
Just remember that one buyer's offer gives you one buyer's view of the opportunity, not necessarily the whole market.
How we researched this page
DataDeals reviews current buyer programs, reported offers and transactions, public filings, and independent reporting.
We distinguish buyer-program ranges, active demand, reported offers, completed transactions, pending bids, and calculator estimates because they do not mean the same thing.
Market-sensitive sources are rechecked regularly. When a number is illustrative, self-reported, rejected, pending, or tied to an unusual transaction, we label it that way.
Last updated: September 22, 2026
Sources used for this page
- Handshake AI Data Partnerships
- Handshake AI Employer Data Program
- micro1 Data Partnerships
- Scale AI Data Partnerships
- Prism Enterprise
- The Information: Warmly / Mercor reported offer
- Forbes: cielo24 company-data transaction
- Stretto: Spirit Airlines data auction status
- Spirit AFA-CWA objection summary
- Sidley Austin: legal issues in AI training-data agreements