How to Sell Company Data to AI Companies: The Seller's Playbook (2026)

Quick answers

Why do AI companies want business records at all?

The public internet shows finished content; it does not show how real work gets done inside a company. AI labs are running out of that public text, and the missing material — how a ticket was resolved, why a deal was priced, how a project was actually delivered — exists only inside operating companies.

That is why the buyers in this market are not buying “customer data.” They are buying workflow records: the connected history of a team doing skilled work over years. A support archive with resolutions, a CRM with closed deals, a project system with drawings and change orders — each is a record of decisions and outcomes that cannot be scraped, because it was never public. The largest public example is also the clearest: in August 2026, Google won a bankruptcy auction for Spirit Airlines' de-identified business records with a $10 million bid, with Mercor as backup bidder at $7.5 million (court record). Customer and loyalty data was explicitly excluded from that package. A bankrupt airline is not a typical company, and $10 million is not a typical price — the example matters because of what was bought (operating records) and what was not (personal data).

Demand is published, not rumoured. micro1 publishes three partnership tiers — $100,000 to $500,000 for qualified partnerships, $500,000 to $3 million for large-scale operational datasets, and $3 million and up for highly unique, proprietary data (its data-partnerships page, checked November 2026). Mercor publishes per-industry ranges from $20,000 to $1.5 million across its referral listings. Scale AI publishes $10,000 to $1 million and up, labelled illustrative on its own page. Those are the numbers this playbook works from — attributed, dated, and treated as ranges to compete over, not promises to repeat.

Step 1: What should you inventory before you talk to anyone?

List each system you run, the years of history it holds and a rough record count. Do not export anything. The inventory is a description of your archive, not the archive — and it is the only thing a buyer needs to value it at this stage.

Work system by system. Email (Gmail or Outlook): how many years, roughly how many mailboxes hold real work rather than notifications. Chat (Slack or Teams): years, and whether channels contain project and decision history or mostly social traffic. Drive or SharePoint: SOPs, templates, proposals, deliverables. CRM (HubSpot, Salesforce or similar): years of deal history, and whether outcomes are recorded or only contacts. Support (Zendesk or similar): ticket volume and whether resolutions are written down. Accounting (QuickBooks): years of transaction history. Code and project systems: repositories with reviews and tickets attached, project files with outcomes.

Two qualities move value more than raw volume. First, connection: a ticket that links to the email thread, the fix and the customer outcome teaches more than three disconnected archives, and buyers say so — chat, CRM and email depth is repeatedly named as what makes the rest of an archive worth more. Second, completion: finished work with known outcomes (closed, resolved, delivered, renewed or lost) is what training data is made from. Drafts and half-finished projects add little.

The output of this step is one page: the data manifest. It is the document buyers bid on, and building it requires no exports, no IT project and no access to your systems by anyone else.

Step 2: Who owns the rights to your records?

Holding data is not the same as having the right to license it. Client files under confidentiality agreements, employee communications, and records containing personal information are the three usual problems — and they are checked before any buyer sees a sample.

Start with your client contracts. Many professional-services agreements promise that client material stays confidential and is used only to deliver the work. Work product you created may be yours to license in de-identified form; the client's own confidential information inside it may not be. The distinction decides which parts of an archive can be counted, and it is exactly the check a serious buyer's diligence will run later — finding a problem early is cheaper than finding it after an offer.

Employee records need the same care. Internal chat and email are the company's records, but they contain employees' personal data, and your privacy notices and handbooks may describe how those records are used. Sector rules add another layer: law firms work under client-consent and privilege duties, healthcare-adjacent companies must keep patient data (PHI) out entirely, and staffing records carry candidate-privacy sensitivity. Platform terms matter too: some tools restrict bulk export or third-party training use in their terms, and a license that ignores those terms invites the deal to unravel.

None of this makes a deal impossible — the standard process exists because these checks are routine. It makes the rights check the step that separates a real valuation from a guess. This playbook is general information about how the market works; the rights position of a specific company is a question for its own lawyer.

Step 3: How is personal data removed?

De-identification removes or masks personal information — names, emails, phone numbers, addresses and identifiers — before any data moves to a buyer. Buyers in this market state that personal data is excluded or de-identified; a manifest describes record counts and types, never the records themselves.

In practice the sequence is conservative. First the manifest goes out with no personal data in it at all — it is metadata. Then, if a buyer wants to proceed, a small redacted sample is shared under a mutual NDA so the buyer can judge quality. Only after a license is signed does the full archive move, de-identified under the standard the license specifies. Who performs the de-identification — the buyer, the seller, or a specialist — is a negotiated term, and it belongs in the contract, not in a sales conversation. Our de-identification explainer covers what is removed, what is kept, and why what is kept (the structure of the work) is the part buyers are paying for.

Step 4: How do buyers value a manifest?

Buyers price a manifest on team size, years of connected history, industry and exclusivity. Published evidence puts operating companies of 20–500 staff in a broad $100,000–$4M published-range band, with documented completed sales frequently far below the published ceilings.

The published anchors, all checked November 2026: micro1's tiers start at $100,000–$500,000 for a qualified partnership (30+ staff on its page). Mercor's industry listings run from $20,000 for small agency and call-centre archives to $1.5 million at the top for manufacturers, with law firms up to about $1.2 million, construction $115,000–$1M and accounting $25,000–$865,000 in its compiled listings. Polyshares, a direct buyer, states typical deals of $100,000 to $2 million and above, and publishes four completed licenses between $110,000 and $450,000. Against all of that, AIData.Broker's compilation of public prices puts the median published offer at about $280,000 and the median documented public sale at about $31,600.

Plan around the documented medians, not the ceilings, until a written offer exists. The factors that move a company up its range are consistent across buyers: more years of connected records, more systems included, work that shows expert judgement with outcomes, and exclusivity — an exclusive license is commonly priced at a multiple of a non-exclusive one (a 2.5–5× range is cited in published pricing guides), because it removes the seller's ability to license the same archive again. That trade is the owner's decision, made with the numbers in front of them, which is why the next step exists.

Step 5: Why should buyers bid against each other?

A single buyer quotes its one price for your archive. Several buyers reviewing the same manifest bid against each other, and the Spirit Airlines auction is the public proof of the mechanism: the winning bid was 2.5 times the first bid on the same records.

This is also where a first submission can quietly cost money. Some programs ask for exclusivity or a first look as part of applying; signing that before other buyers have seen the manifest gives away the competition that sets the price. Nothing in the standard process requires it: the manifest is metadata, sharing it with several buyers costs nothing and commits you to nothing, and written offers can then be compared on the terms that actually differ — price, license length, exclusivity, who pays for de-identification, and payment timing. Buyers' published timelines put most deals at six to twelve weeks from first contact to payment, so the comparison step does not meaningfully slow a deal down.

The platform comparison on this site lists the main routes — direct buyer programs, marketplaces, single-buyer brokers and this multi-buyer introduction service — with each one's published terms, so you can judge the routes before you pick one. If you decide to apply direct anyway, you negotiate with that one buyer alone — most owners run the fit check first so competing offers set the price.

Step 6: What should the license itself say?

Negotiate a license, not a sale: limited use (AI training, as defined), a set term, exclusivity only at a price that reflects it, no onward resale of your raw records, and deletion or return at the end of the term. You keep ownership throughout.

The clauses that carry the money and the risk: the grant (exactly which records, for exactly which uses); exclusivity (exclusive, non-exclusive, or exclusive for a fixed window); term and deletion (how long the buyer may keep and use the data, and what happens when the term ends); de-identification standard (what is removed, by whom, verified how); payment (amount, timing — some buyers publish payment within about 60 days of delivery and acceptance — and what acceptance means); and warranties (what you are warranting about rights, which is why Step 2 comes first). The explainers on license vs sale and exclusive vs non-exclusive unpack each choice in plain language. A lawyer reviews the final document; no broker's summary substitutes for that, including this one.

What are the most common mistakes sellers make?

The expensive mistakes are all made before any negotiation: assuming a headline ceiling is a price, signing away a first look too early, counting records the company does not have the right to license, and exporting data before anyone has asked for it.

Each has a cheap fix, and the mistakes page lists them in full. In short: treat every published figure as a range to compete over; keep the manifest non-exclusive until offers exist; run the rights check before the valuation, not after; and send nothing — no files, no samples, no credentials — until a signed license or an NDA-covered sample step calls for it. Owners who follow that order reach the offer stage with their options intact, which is the whole game in a market this young.

Sources — checked November 2026

micro1 data-partnerships page (published tiers and fit). Mercor referral listings (published per-industry ranges, compiled October–November 2026). Scale AI data-partnership page (illustrative ranges). Polyshares published deal examples. AIData.Broker compilation of public prices (median published offer and median public sale). Spirit Airlines archive auction, court record, August 2026. Every figure in this guide is a published range or a documented public example, attributed where it appears.