How to sell your company’s data to AI labs

AI labs have trained on most of the public internet. The next gains come from data the public web never had: the private, operational record of how real work gets done inside companies. If your company has years of tickets, code reviews, contracts or ledgers, you are sitting on an asset labs are actively looking to license.

This guide covers how that market works and what it takes to sell into it without exposing your people, your customers or your secrets.

What AI labs are actually buying

Labs are not buying your customer list or your financial statements. They are buying examples of expert work: the sequence of steps a support engineer takes to close a hard ticket, the review comments that turned a broken pull request into a shippable one, the redlines between a first draft and a signed contract.

That kind of data teaches models to plan, reason and recover from mistakes, which is exactly where today’s models still fall short. Common categories include:

• →Codebases with full history: commits, reviews, failed attempts and linked issues
• →Tasks and tickets captured from first report to resolution
• →Messages and threads where decisions and handoffs happen
• →Documents, including drafts and redlines, especially in regulated industries
• →Structured operational records: logs, invoices, ledgers and CRM data

How company data is valued

Price is driven less by raw volume and more by how useful the data is for training. The factors that matter most are:

• →Completeness: does the record show the whole process, not just the outcome?
• →Depth of history: years of iteration are worth more than a recent snapshot
• →Domain specificity: specialized fields like healthcare, finance, legal and industrial operations command premiums
• →Structure and consistency: clean, well-linked records are easier to package and evaluate
• →Scarcity: data types few other sellers can offer are priced higher

Packaging also moves the price. The same raw data can sell for very different amounts depending on how well it is scoped, documented and quality-checked before a lab sees it.

Protecting privacy and confidentiality

The legitimate version of this market depends on one rule: nothing that identifies a person, a customer or a trade secret leaves processing. In practice that means removing names, emails, phone numbers, account numbers, credentials, addresses and other identifiers, and replacing them with consistent placeholders so the structure of the work stays intact.

You should also control scope. Before anything is listed, you should be able to exclude specific channels, repositories, folders, customers or date ranges, and review exactly what a listing covers.

The process, step by step

• →Connect a source with read-only access and get a valuation
• →Approve the scope of each listing
• →The data is quality-checked, redacted and packaged for buyers
• →Approved listings go to AI labs; when a deal closes, you get paid

With DataLogix, that last step happens in as little as 7 days after a deal closes. The whole process asks very little of your team: someone with admin access connects the sources and signs off on scope, and we handle everything else.

Where to start

Pick the source your team has used the longest and most consistently. That is usually a code host, a ticketing system or a shared drive. Run it through the estimator on our home page for a rough range, then request a free evaluation for a real number.