An AI data collection company makes new data to order. It designs a task, recruits people or deploys devices, and delivers what they produce. Licensing works the other way round: it takes a copy of records a company has already built up over years of real work. The two are often lumped together, but they have different suppliers, different costs and a different place for a broker. This page explains both, and why the second kind of data is the scarce one.
Looking for the wider market? See AI training data companies, mapped for brokers.
Every dataset a lab buys from outside starts one of two ways. Either someone is paid to produce it now, or a company that already holds it agrees to license a copy. A broker's work only makes sense on one of these routes.
Commissioned or crowdsourced: new data, produced on request
A copy of records a company built up by doing its job
Search results for "AI data collection companies" mix both. Some of the companies listed run collection services: they recruit contributors, design tasks and deliver labeled output. Others license data that already exists, from content archives to company records. Some do both under one name. For a broker, the first question about any of them is the same: does this company need people, or does it need companies?
Collection runs on individual contributors, so a broker who finds companies has little to sell it. Licensing runs on companies that own records, so finding, qualifying and introducing those companies is exactly the work it needs. The rest of this page explains why that second supply is short, and how to tell which kind of buyer you are talking to.
What collection companies offer as services, described by method. Each one solves a real gap for AI developers, and each has a limit that operating records do not share.
A large pool of contributors completes short tasks online: writing prompts, ranking model answers, recording speech, taking photos, transcribing audio. Work is split into small units and paid per unit.
Credentialed specialists, such as physicians, lawyers, accountants and engineers, label data, write model answers, review outputs and explain where a model went wrong. Usually paid by the hour.
Teams or devices record the physical world: roads, warehouses, store shelves, machines, buildings. Output can be images, video, lidar, audio or sensor logs, captured to a plan.
Wearers record tasks from their own point of view: cooking, repairs, assembly, cleaning, lab work. The camera shows hands, tools and the order of steps the way the worker sees them.
Contributors complete software tasks while their screen, clicks and keystrokes are recorded, often with spoken or written narration of why they do each step.
Years of history. Every form of collection captures what happens during the project. None can produce the decade of tickets, reviews, approvals and revisions that a running company accumulates without trying.
Two kinds of collection against licensing a company's existing records. The descriptions are general; individual companies and contracts differ.
| Commissioned collection | Crowdsourced collection | Licensing existing operating records | |
|---|---|---|---|
| What is produced | Data made to a detailed specification | Many small contributions, combined | A scoped copy of records the company already holds |
| Who creates it | Hired specialists, field teams or devices | An open pool of contributors | The company's own staff, through years of normal work |
| Time span covered | The length of the project | Minutes per task | The company's history, often many years |
| Realism | Designed to resemble real work | Varies by task and contributor | Real decisions with real consequences |
| Supply | As much as the budget and schedule allow | Large, if the task is simple | Fixed: only what exists and may be licensed |
| Main cost | People's time, travel and equipment | Per-task pay at volume, plus quality control | Scoping, legal review, export and de-identification |
| Main risk | Staged behavior, inconsistent quality | Low effort, fraud, shallow answers | Confidentiality, personal data, ownership of records |
| Who gets paid | Contractors and contributors | Individual contributors | The company that owns the records |
| Where a broker fits | Rarely | Not at all | Finding, qualifying and introducing companies |
Companies produce records every day, yet buyers compete for them. The deals reported in 2026 show what one company's internal data can be worth when it comes to market.
Forbes (16 April 2026), Fast Company and Gizmodo covered startups selling old Slack and email archives. These figures describe archives from companies that are closing or bankrupt. They are not a forecast for any running business.
A collection project can hire more people next month. It cannot hire ten years. Long histories, with the same clients, systems and teams over time, exist only where a business actually ran that long.
Operating records show decisions made with real money, real clients and real deadlines, and what happened next. A staged task can copy the steps, but not the pressure or the outcome.
The same project shows up in email, chat, tickets, code reviews and invoices, joined by the same people and dates. A collected sample usually lives in one tool, cut off from the rest.
Most companies have never thought of their records as something to license, and no marketplace lists them. Supply appears only when someone finds the company and it agrees to talk.
Client files under NDA, privileged matters, patient records and personal data often have to stay out. What remains after that review can be a small share of what the company holds.
Retention policies, system migrations and shutdowns delete history every year. Once deleted it is gone, which is why a company's activity status matters as much as its size.
The clearest way to see the difference is the document each deal starts from. Both examples below are invented, and neither shows a price.
Licensing buyers have a supply problem that money alone does not solve. They know what they want, records of real work, but those records sit inside companies that have never been asked. Each company has to be found, checked against the buyer's rules, and persuaded to have a first conversation. That is the work brokers, referrers and sourcing teams do.
The company-data programs publish what they look for. As published, checked 7 October 2026: micro1 lists 30+ employees (its referral posting says 30 to 200), US first and primarily English, with "$100K-$2M+ for approved data packages". Mode lists 20+ full-time US office employees, accounting firms at 10+ and law firms at 6+, with several years of records and a range of "$100K-$5M". Grepped lists "$20K-$5M". Miro Advisory lists operating datasets at $100K-$1M+ and codebases at $10K-$1M+.
Three of them also publish what they pay the person who brings the company: micro1 "earn $50,000" with "no cap", paid after onboarding plus a minimum revenue threshold; Mode "Earn $50K per referral", described on X as up to $55k or 6%; Grepped "Refer for another $10K". micro1's terms add sole discretion and clawbacks, forbid sharing payouts with companies and forbid posing as its partner.
Field capture and first-person video sometimes need access rather than people: a warehouse, a fleet, a lab bench, a workforce that agrees to be filmed during real work. A company can grant that access. Those arrangements are bespoke. None of the programs on this page publishes rules or referral terms for them, so treat any such deal as a separate negotiation with its own consent and privacy questions.
The same company can be a buyer for the companies you find, or a partner you work with on collection. Ask different questions in each case, and get the answers in writing before you start.
Before you introduce a single company.
Before you recruit anyone or arrange access to a site.
Who does what on a licensing introduction. Practitioners say the deal itself takes 60 to 90 days to close, and the referral comes after that.
Start from companies likely to hold long, connected records. A Data Asset Score gives each one a score from 0 to 100, a grade, the data it likely holds, its history and its activity status.
Check headcount, US presence, language and years of records against micro1, Mode and Grepped. The free company check does it from public pages, with quotes, in about 20 seconds.
Explain who the buyer is, that the buyer pays you, and that the company is free to say no or to apply elsewhere.
Register first, then introduce through the program's own process, so the referral is recorded the way its terms require.
The buyer and the company sign an NDA and look at a manifest and samples: systems, years, volume and sensitivity.
Price, scope and exclusivity are set in writing. The agreed copy is exported and personal and confidential details are removed.
The buyer confirms delivery meets the agreed criteria and pays the company as the contract says.
Paid under the program's own terms. For micro1, that means after onboarding plus a minimum revenue threshold, at its sole discretion and subject to clawbacks.
Collection companies recruit people. Licensing buyers need companies, and that is what our tools score, at company level only: no contacts, named people, emails or phone numbers.
Scores any company from 0 to 100 for the data AI buyers want, with a grade, the data it likely holds, its history and its activity status. Built on our index of 102 million domains, 99.99% of the active internet, with domain history. Nine factor groups, including history, operational systems, customer systems, knowledge assets and activity status. Free demo, limited per day.
Try the demoReads a company's public pages and compares team size, location, history and systems with the published rules of micro1, Mode and Grepped, with a quote for every fact it finds. Free. For a broker, it is a way to qualify a target in about 20 seconds, before any outreach.
Check a companyReady lists for 20 US sectors, from labs and CROs to law firms, software companies and logistics. Every company is verified active: the domain resolves, is not expired, is not parked and shows no error or placeholder page. Each comes with its Data Asset Score, likely data assets, history and activity status, and every list has a free preview with the top 5 visible. Buy a list once from $249 (a CSV snapshot, no updates), or keep lists current through the API: Pro ($299 a month, 25,000 lookups) returns them as JSON with paging, and Scale ($799 a month, 100,000 lookups) adds the bulk CSV export and new segments on request. Start with the labs and CROs preview or buy a list.
See the 20 listsMost come from treating collection and licensing as one market. They are two.
Sending a company introduction to a team that recruits contributors.
Send it to a published company-data program, and confirm its referral terms cover companies.
Telling a company its employees can earn by annotating for a buyer.
A license pays the company that owns the records. Individual paid work is a separate, personal arrangement.
Asking a company to send you exports so the introduction looks stronger.
Leave samples to the buyer's own NDA and review. Describe what exists at company level.
Proposing that a company stage new recordings of work it has a decade of records of.
Start with what the company already holds. Its history is the scarce part.
Waiting until a company has closed, its staff gone and its systems switched off.
Watch activity status. A company winding down can still scope and export while people remain.
Using shut-down market figures to set a running company's expectations.
Quote each program's published range, with its date, and say that only a buyer's review sets a price.
They produce new data to order for AI developers. A collection company designs a task, recruits people or deploys devices, captures what they produce, checks its quality and delivers it in the format the buyer asked for. The work ranges from short crowd tasks and expert annotation to field and sensor capture, first-person video and recordings of people using software.
Typically: task and guideline design, recruiting and paying contributors, running the capture itself, labeling and review, quality checks, consent paperwork, and delivery to the buyer’s specification. Some add annotation of data the buyer already has. What they do not usually offer is years of real operating history, because that cannot be produced on request.
Collection makes new data: people are paid to perform tasks, and the result is as large as the budget. Licensing copies records a company already built up through years of real work, such as tickets, messages, procedures and code, and pays the company that owns them. Collected data is designed to resemble real work; licensed operating records are real work.
Because the useful part is narrow. Buyers want long, connected histories of real decisions, and most of that sits inside companies that have never offered it to anyone. Confidentiality and privacy remove a large share, retention policies and system migrations delete more, and a company that shuts down can lose it entirely. Time is the one input no collection budget can buy.
Rarely with company introductions. Collection runs on individual contributors, so a company has little to offer it except, sometimes, access to a site or a workflow. Brokers who find companies fit the licensing side: company-data programs such as micro1, Mode and Grepped publish company payout ranges and referral terms, as published, checked 7 October 2026.
Several publish programs, as published and checked 7 October 2026. micro1 lists “$100K-$2M+ for approved data packages”, Mode lists “$100K-$5M”, Grepped lists “$20K-$5M”, and Miro Advisory lists operating datasets at $100K-$1M+ and codebases at $10K-$1M+. These are ranges a buyer will consider, not offers.
Ask for its terms in writing. As a buyer of a company’s records: does it publish eligibility rules, a payout range and referral terms, who de-identifies, and what is the scope of use. As a partner: how contributor consent is recorded, who owns what is captured, and whether the work would require you to handle personal data yourself. General information, not legal advice.
No. We do not sell a shut-down or wind-down list. Every Data Asset Score shows a company’s activity status: active, winding down or acquired, parked, or unreachable. Our ready company lists cover 20 US sectors. Every company on them is verified active (the domain resolves, is not expired, is not parked and shows no error or placeholder page), and each still carries its activity status, so you can see which ones are winding down or acquired.
Score a company for the records AI buyers want, check it against the published rules of micro1, Mode and Grepped, or compare plans for scoring at volume.