Common mistakes when selling company data to AI labs

The most common mistakes when selling company data to AI labs are skipping a rights audit, sending raw data before de-identification, pricing without considering data depth, and treating every workflow as the same. Avoiding these errors makes the licensing process smoother and helps you get a fair market offer.

Mistake 1: Skipping a rights audit

Before you expose a single record, make sure you own the data and that any customer contracts permit a de-identified license. Ownership is not automatic just because the files sit on your servers; many agreements contain clauses that forbid secondary use. A quick internal checklist catches the most common traps:

• Do you have clear title to the records?
• Do any customer agreements require the data to stay inside your organization?
• Have you removed, or planned to remove, all personally identifiable information (PII) before a buyer sees the data?

If you answer "no" to any of these, resolve the issue first. Our free fit check on the sell page walks you through those questions and confirms whether you meet the 30-employee minimum and other basic criteria.

Mistake 2: Sending raw data before de-identification

AI labs care about the operational signal, not the names of individual employees. Sending an email archive, ticket dump, or CRM export with full identifiers breaches privacy laws in many jurisdictions and drags the price down. De-identification swaps names, exact dates, and other personal markers for stable placeholders such as "employee 632."

We run a separate de-identification service (see the de-identification page) that blends automated tooling with human review. The buyer usually covers that cost, but the exact arrangement is negotiated case-by-case. Never let a lab see raw files until your counsel signs off that the PII removal satisfies the test required for your region.

Not sure your company is a fit? Take the free check

Mistake 3: Pricing without data depth

Many sellers assume "any data is valuable" and set prices too high, causing deals to fall apart. The market actually looks at three concrete factors:

1. Volume. More rows or files create a larger training signal.
2. Continuity. Years of uninterrupted history on the same tools (for example, a three-year log of support tickets) are far more valuable than a one-month snapshot.
3. Rarity. Data from a niche industry or a specialized workflow commands a premium because few alternatives exist.

Approved deals typically land between $100,000 and $2 million USD; many buyers cite a sweet spot of $200,000 to $300,000 USD for a well-structured dataset. Ignoring volume, continuity or rarity either leaves money on the table or prices you out of the market.

Mistake 4: Assuming every workflow is the same

Licensing a set of SOPs, knowledge-base articles, or QA records may look similar on paper, but each workflow carries its own value. A logistics firm's routing decision chain differs dramatically from a software vendor's bug-resolution process. Treating them as interchangeable can lead to under-pricing or to a buyer demanding data you never intended to share.

When you have multiple departmental processes, consider offering them as separate workflow licenses. Pricing per organizational workflow lets the buyer pick the most relevant parts and gives you a clearer picture of revenue potential. Our workflows page explains how workflow licensing works and what documentation you may need.

How we help you avoid those pitfalls

We begin with a few basic questions, no data moves at this stage. From your answers we draft a one-page summary and share it with several vetted AI labs. The labs return their best quotes and conditions, which we bring back to you for review. Only after you approve a quote do we start the secure export, de-identification (if required), and final contract negotiation. The full flow is outlined on the how-it-works page, and you can launch the free eligibility check by clicking the button below.

A quick test you can run today: open a random support ticket from the past six months and ask whether any field contains a name, email, or exact timestamp. If the answer is yes, that record is not ready for a buyer. Mask or remove those personal fields, then repeat the check on a few more tickets. Consistently finding personal data signals that a systematic de-identification step is required before any licensing conversation.

By confirming ownership, cleaning the data, pricing with volume, continuity and rarity in mind, and treating each workflow as a distinct asset, you position your company to receive fair market offers and avoid costly delays.