Why a data sample is needed before pricing your data

You won't see an accurate price for your company's operational data until a buyer looks at a sample. The value depends on how much data you generate, what kind of records you keep, how long the history runs, and whether the workflow can be followed from request to outcome.

A quote based only on a description is a guess. It can miss the very drivers that move the price: volume, uniqueness, years of continuous use on the same tools, and visible reasoning that explains why decisions were made. A sample lets a lab verify those elements and decide if the data matches its training goals.

What makes data pricing so variable

Even two firms in the same industry can produce wildly different data sets. A support-ticket archive that includes the original question, the troubleshooting steps, and the final resolution tells a richer story than a list of closed tickets alone. A sales CRM that captures the full conversation chain, from lead to quote, offers more training signal than a spreadsheet of won deals.

The price also climbs when the data spans many years on the same software. Continuity lets a model learn long-term patterns. Niche industries command higher offers, as do clean rights that allow unrestricted use after de-identification. By contrast, a file that is just an email dump, with no clear link between request and outcome, usually brings a lower figure.

Because these factors can only be measured on real files, we ask for a small, representative sample before any numbers are discussed.

Typical data types labs look for

• Support tickets with resolutions. The request, the steps taken, and the final answer.
• Chat threads where a decision was made. Shows reasoning and outcome.
• CRM and sales activity. From lead capture through closed-won status.
• Standard operating procedures and internal documentation. Provide the "why" behind actions.
• Project histories and accounting exports. Give context on timelines and financial impact.

The chain from request to outcome is what has value. An email archive on its own is worth less than most owners expect.

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

How a sample is evaluated

When you upload a sample, the first step is de-identification. All personally identifiable information and any confidential markings are removed, and names become stable placeholders such as "employee 632". This happens before any AI lab sees the data, and your legal team must confirm that the removal meets the applicable privacy rules (see our regions page for guidance).

After the data is anonymized, the lab checks three things:

1. Completeness of the workflow. Does the sample show the full chain from request through decision to result?
2. Volume and density. How many records are in the sample and how much detail does each contain?
3. Uniqueness. Does the data capture processes or industry specifics that are hard to find elsewhere?

The lab assigns a score based on these criteria, which then translates into a price range. For qualified companies the range often lands between $100,000 and $2 million USD.

Quick internal test you can run

Pick a random week of records and ask yourself: Can I follow a single request from start to finish using only the information in these files?

If the answer is yes, the sample likely contains the reasoning and outcome linkage that buyers value. If you struggle to trace the path, the data may need enrichment before it becomes attractive to a lab.

The broker's role in the pricing loop

We never see your raw data. You answer a few basic questions about company size, the types of records you keep, and the systems you use. From those answers we create a one-page summary and send it to several large AI labs. The labs return their best quotes and the conditions that fit your needs.

Only after you approve a quote do we move forward with a secure export and de-identification. The de-identification service can be handled by the buying lab or by us as a separate project; cost and responsibility are negotiated case-by-case. The broker fee is paid by the AI lab once the deal closes, so you incur no upfront charge.

For a step-by-step view of how a deal runs, see our how-it-works page.

How to check if your company is a fit

The first step is to see whether you meet the basic eligibility. Companies need 30 or more full-time employees to qualify, and there is no upper limit on size. Larger firms usually generate more data, which can lead to higher offers, but the price still depends on the data's characteristics, not just headcount.

Visit our free check page to start the fit assessment. The button below will guide you through a quick questionnaire that does not require you to share any data yet. If you pass, we'll request a small, anonymized sample to begin the pricing conversation.

Gather a sample and start the eligibility check

Collect a handful of records that illustrate a full business workflow, perhaps a support ticket with its initial request, troubleshooting steps, and final resolution, or a sales opportunity from lead capture through closed-won status. Make sure the files are de-identified or can be cleaned according to your region's privacy rules (see our regions page for guidance). When you're ready, use the button below to start the eligibility check and move toward a realistic price quote.