How much is my company's data worth to AI labs?

It's the first question every founder and operator asks, and the honest answer is: it depends, but less mysteriously than you might think. Data from US companies with 20+ employees generally attracts offers somewhere between $50K and $1M+. Where a particular company lands within (or beyond) that range comes down to a handful of factors you can reason about before talking to anyone.

This guide explains those factors, what tends to move the number up or down, and why the only reliable way to price enterprise data today is to get competing offers.

Why there's no price list

Enterprise training data is a young market. Transactions are private, datasets are all different, and buyers' needs shift as their models improve. That means there's no public index you can look your company up in, and anyone who quotes a precise per-gigabyte or per-message rate without seeing your data is guessing.

What we can say is which characteristics consistently matter to buyers. Our value estimator uses a simple heuristic based on these factors to give you an illustrative range. It's a conversation starter, not an offer.

The five factors that drive value

1. Volume

More data is generally worth more, up to a point. Volume is measured differently per system: messages in Slack, pages in a wiki, records in a CRM, gigabytes in a shared drive. But volume alone is a weak signal. A million near-identical automated notifications are worth far less than a hundred thousand substantive human conversations.

2. Years of history

Longer histories are valuable because they show how work changes over time: processes being introduced, revised and abandoned; teams forming and reorganizing; tools being adopted. Several years of continuous history is usually more interesting to a buyer than a large but recent burst of activity.

3. Workflow richness and uniqueness

Labs want examples of complex, real-world work. Data that shows multi-step processes, cross-team handoffs, approvals, exceptions and the reasoning behind decisions is worth more than data that shows simple, repetitive tasks. Unusual domains, specialized expertise and industry-specific workflows tend to command a premium because they're harder to find elsewhere. See what AI labs look for in enterprise data.

4. Quality and structure

Clean, consistent, well-organized data is easier to use and therefore worth more. Structured records with clear fields, documents with consistent formatting, and conversations with intact threading all help. Heavy duplication, broken exports and large gaps reduce value.

5. License terms

The same dataset can be priced very differently depending on the terms. Exclusive licenses, broader permitted uses, longer durations and fewer restrictions generally increase what a buyer will pay. Narrow, non-exclusive, time-limited licenses usually cost buyers less. You get to choose the trade-off. Our licensing vs. selling guide explains the options.

What usually increases an offer

• Multiple connected systems. Slack plus docs plus CRM shows how work flows between tools, which is more valuable than any one of them alone.
• Long, continuous history. Gaps and short windows reduce value.
• Specialized domains. Real estate, healthcare administration, manufacturing and trading data can be in particular demand, case by case.
• Good documentation. SOPs, playbooks and process docs give context that makes the rest of the data more useful.
• Competition. Having several labs bid is often the single biggest lever on price.

What usually decreases an offer

• Heavy exclusions that remove most of the substantive work.
• Mostly automated content such as bot messages, notification emails and system logs.
• Very small teams, where there are fewer distinct workflows and handoffs.
• Poor export quality: missing threads, corrupted files, inconsistent formats.
• Restrictive terms that limit how the buyer can use the data.

A rough way to think about it

Without inventing numbers, here's a useful mental model. Start from your headcount: more people usually means more varied work. Adjust upward for each substantive system you'd include and for each year of history. Adjust again for uniqueness: is your industry or workflow something a lab couldn't easily get elsewhere? Then remember that terms and competition can move the final number significantly in either direction.

That's essentially what the estimator on our homepage does. Treat its output as a sanity check on whether a conversation is worthwhile, not as a quote.

How to get a real number

The fastest path to a real number is to share rough estimates with a broker who can take them to multiple buyers:

1. Which systems you use
2. Roughly how much data is in each
3. How many years of history
4. Headcount
5. Years in business

No files need to change hands at this stage. A good broker turns those estimates into a packaged description, gauges interest from several labs, and comes back with offers you can compare.

Frequently asked follow-ups

Is the value one-time or recurring? Most deals today are one-time payments for a defined dataset, though some agreements include options for future data deliveries. It depends on what you and the buyer agree.

Does de-identification reduce value? Generally not much, because labs want workflows rather than identities. Careless de-identification that destroys context can hurt, which is why method matters. See how to de-identify business data.

Will my number go up if I wait? Possibly, since you'll have more history. But demand, competition and buyer priorities also change. There's no harm in getting an offer now to understand where you stand.

DataOffer doesn't charge anything upfront to find out. Share your rough estimates and we'll come back with competing offers. Nothing is shared until you approve the buyer, price and terms.

This guide is general information, not legal, tax or financial advice. Figures and ranges are illustrative; talk to qualified advisors about your situation.