What makes company data valuable to AI labs? A self-check for founders

When founders first hear that AI labs pay for company data, the next question is almost always about their own. Is our data valuable to AI labs? What would anyone pay for our Slack history, or for ten years of support tickets?

The truthful answer is that it depends, and the things it depends on are easier to check than most people expect. You do not need a buyer to tell you whether your archive is interesting. You can look at it the way a lab would, which mostly comes down to asking what a model could learn from it.

These are the questions we would ask about any archive, roughly in order of how much they matter.

How far back does it go?

History is one of the clearest signals of value. A ticket system with several years of records shows how a team handled the same kinds of problems over and over, how its processes changed and how new people learned the ropes. A few months of data only shows a snapshot.

Continuity matters as much as length. Five years in one system is usually worth more than five years spread across three tools that were migrated badly, because the thread of each piece of work survives intact.

Is the work written down, and can you see it being done?

This is the question that separates a good archive from a great one. Plenty of companies have procedures, playbooks or a wiki. Plenty also have ticket history or project boards. The interesting ones have both, and the two line up.

When a lab can read how a task is supposed to be done and then see many real examples of people doing it, including the shortcuts and the times it went wrong, it gets something close to an apprenticeship in written form. That pairing is rare on the public internet, which is a big part of why it is worth so much.

Do tasks have outcomes?

A ticket that says a customer cannot log in is mildly interesting. A ticket that shows the investigation, the fix, the customer's reply and the final resolution is far more useful, because a model can learn what success looks like.

Outcomes can be as simple as whether a ticket was closed or reopened, whether a deal was won or lost, or whether a refund was approved or turned down. If your tools record how things ended, that field is often the most valuable part of the whole export.

How much of the work happens on a computer?

If most of your team's work runs through software, your records capture most of what they do. A support team, an accounting department or an engineering group leaves a detailed trail behind. A field crew or a warehouse team leaves much less, because so much of their work happens away from a screen.

That is why our intake form asks roughly what share of your team's work happens entirely on a computer. It is a quick way to estimate how much of the real work your data actually reflects.

Is it in a few places or scattered everywhere?

Data spread across a dozen tools is not worthless, but it is harder to prepare. Each system has to be exported, matched up with the others and cleaned. Data that lives in a handful of well used tools, with consistent naming and links between them, is cheaper to work with and usually more valuable as a result.

It also helps to know roughly how big each export would be. Most tools show storage or record counts somewhere in their admin settings, and a rough figure is enough to start a conversation.

What languages is it in?

A large share of AI training data is in English. Archives in other languages, or in the mix of languages many international teams actually use day to day, are harder to come by. Depending on what a buyer is building, that can make your data more interesting to them.

Do you have the right to share it?

This one is less about value and more about whether a deal can happen at all. Before you go further, think about who else has a claim on the data.

Customer contracts sometimes limit how you can use information your clients gave you. Employees may need to be told, and in some places asked. Information about people's health or finances comes with its own rules. If you operate in Europe or the UK, the GDPR shapes what you can do with personal data, and the UK regulator's guidance on anonymisation explains how anonymous data needs to be before those rules stop applying.

You do not have to solve all of this alone, and a good buyer will help you work through it. It is still worth knowing where the sensitive spots are before the first call.

How tangled is the personal information?

Every real workplace archive contains names, email addresses and personal details. What matters is how deeply they are woven in. Support tickets full of customer addresses and payment details take far more work to clean than an engineering wiki does. That extra work does not rule your data out, but it changes how long preparation takes and what it costs. We wrote about how the cleaning works in how to de-identify workplace data.

Things that matter less than you might think

Raw size is the big one. Terabytes of recorded video calls or duplicated files can be worth less than a few gigabytes of well organized ticket history with clear outcomes.

Polish matters less than people assume, too. Labs are not looking for a tidy, curated version of your company. The mess is often the point, because real work is messy and models need to learn how to handle it.

And it does not matter much whether your company is well known. A small team with clear processes and good records can be more useful than a famous company with a chaotic archive.

Putting it together

If you worked through these questions and found years of history, documented processes, records with outcomes and mostly computer based work, your archive is probably worth a serious conversation. If you found gaps, that is normal, and it does not necessarily rule you out.

The next step is to write it down. List your tools, what each one holds and roughly how far back it goes. If you would like a second opinion, you can share that overview with us and we will tell you plainly whether we think it is a fit. For a wider view of how these deals work, start with can you sell your company's data to AI labs.