How to sell your company's data to AI companies: a step-by-step guide
A practical, step-by-step guide to selling your company's data to AI labs: what qualifies, how to value it, how to stay compliant, and how deals are structured.
By Databounties Editorial Team · Updated 6 October 2026 · 8 min read
Key points
- AI labs license real-world business data because the public web is exhausted and scraping is legally risky.
- The data that sells best is specialist, built up over many years, and comes from systems such as ledgers, LIMS, ERP, CRM and process logs.
- The process has six steps: inventory, valuation, rights check, anonymisation, buyer outreach under NDA, and a licence agreement.
- You license the data. You don't sell ownership, and you keep running your business as normal.
AI labs have trained on almost everything published on the open web. To keep improving their models, and to build AI that can do real work in accounting, science, engineering and operations, they need data the internet doesn't have: the records businesses build up just by operating. That has created a market where companies can license their historical data for AI training and get paid for it.
This guide walks through how to do it properly, from working out what you have to signing the licence.
Step 1: Work out what data you actually have
Most companies underestimate what they hold. Buyers aren't after your customer list. They want the records that show how expert work gets done over time. Start by listing your systems and how many years of history each one holds:
- Finance: Xero, QuickBooks, Sage or NetSuite ledgers, reconciliations, close packs.
- Operations: ERP records, orders, inventory, routing, maintenance logs.
- Science & engineering: LIMS, electronic lab notebooks, batch records, process and yield data.
- Commercial: CRM history, quotes, pricing over time, support tickets.
- Documents: reports, SOPs, contracts and the email trails around them.
The strongest datasets cover five or more years, are consistent over time, and capture decisions and outcomes, not just final states. See what makes data valuable.
Step 2: Get a valuation
Prices vary widely. The main factors are sector, how scarce the data is, its volume and history, how clean it is, and whether you license it exclusively. A valuation from someone who has seen your schema and a sample tells you whether it's worth going further before you spend any time on export.
Step 3: Check you have the right to sell it
This is the step most sellers skip and the one buyers care about most. Before anything leaves your systems, confirm that:
- Your company owns the data, or has the right to license it for this use.
- No client contracts, NDAs or software terms restrict sharing it.
- Personal data can be removed or anonymised so that GDPR no longer applies to what you sell.
- Nothing is subject to export controls, regulatory confidentiality or legal privilege.
We cover this in detail in is it legal to sell company data to AI companies?
Step 4: Export and anonymise
Data is exported from each system, usually as CSV, JSON or database dumps, and then cleaned. Cleaning means removing or transforming anything that identifies a person (names, emails, account numbers, free-text mentions) and anything commercially sensitive you don't want to share, such as client names or pricing. Read how to anonymise business data before selling it.
Step 5: Find buyers and share samples under NDA
Buyers sign an NDA before they see anything. They then review a representative, anonymised sample and a short "data card" describing the dataset: what it covers, its date range, volume, fields and how it was cleaned. Approaching several buyers at once is what creates price competition. Deciding whether to do this yourself or through a broker is covered in data broker vs selling direct.
Step 6: Sign a licence and deliver
You don't sell the data outright. You grant a licence to use it for a defined purpose, usually AI training and evaluation, on exclusive or non-exclusive terms. The agreement sets the price, payment schedule, permitted uses, restrictions on re-identification and resale, and what happens at the end of the term. See what to include in an AI data licensing agreement.
What sellers usually get wrong
- Selling too early to one buyer. A single offer is rarely the best one.
- Granting exclusivity cheaply. Exclusive rights should command a significant premium.
- Treating anonymisation as find-and-replace. Free-text fields and rare combinations of values can still identify people.
- Vague licence terms. "Any purpose, forever" isn't a standard you have to accept.
Sources
- 1.Will we run out of data? Limits of LLM scaling based on human-generated data
Epoch AI (Villalobos et al.), arXiv, 2022
- 2.
- 3.Anonymisation guidance
Information Commissioner's Office (ICO)
Frequently asked questions
Can a small company sell its data to AI companies?
Yes. Size matters less than how specialised your data is and how much history it covers. A 20-person accounting practice with ten years of well-structured ledgers can be more valuable than a large company with generic data.
How long does it take to sell company data?
Typically four to twelve weeks from first call to signed licence. Most of that time goes on the rights check, export and anonymisation. Buyer review of samples usually takes two to four weeks.
Do I need to give AI companies access to my systems?
No. Data is exported, anonymised and delivered as files. No buyer gets access to your live systems.
Who buys company data for AI training?
Frontier AI labs, companies building industry-specific AI models and agents, and research groups that need evaluation data. Demand is strongest for specialist, real-world records that are not available publicly.
