Data buyers
What kinds of company data AI labs are buying right now
The ten kinds of company data AI developers want right now, who holds them, what makes a dataset attractive and what buyers will not touch.
Henry Jackson, Founder, Knowledge Fuel. . 5 min read
Buyers of training data are no longer looking for more of the public web. They want records of real work: how a problem was solved, what a machine did before it failed, how a claim moved from filing to payment. This article covers the ten kinds of company data in demand now, who holds them, what raises the price and what buyers will not touch.
The ten kinds of data in demand
1. Customer support tickets and chat logs
Zendesk, Freshdesk, Intercom or ServiceNow exports, live chat transcripts and internal IT help desk tickets. They show real problems, real resolutions and the exact words customers use. Buyers use them to train models that diagnose and resolve issues.
2. Call recordings and transcripts
Contact center, sales, dispatch and field service calls. Natural speech with accents, interruptions and domain vocabulary is scarce in public datasets. Paired with transcripts and outcomes, recordings train speech recognition, voice agents and call analytics. Consent records are reviewed before anything moves.
3. Email and internal communications
Shared inboxes such as sales@ or support@, vendor correspondence and project threads. Professional writing about real work, with context and follow-through, is far rarer than public text. Everything is de-identified first, and privileged or sensitive threads can be excluded entirely.
4. CRM and sales histories
Salesforce, HubSpot or Dynamics exports, quote and proposal histories, account notes. Structured records of deals, stages and win or loss reasons teach models how business decisions unfold over time. They train forecasting, summarization and next-step recommendation.
5. Documents, contracts, manuals and knowledge bases
SOPs, training manuals, contract and policy libraries, engineering documentation and internal wikis. Long, structured, domain-specific documents are exactly what general web text lacks. They train models to read, summarize and reason over professional material.
6. Images, video, dashcam and drone footage
Fleet camera footage, site and equipment inspection photos, aerial imagery and product photography. Real-world visual data from specific settings, with timestamps and labels, is in constant demand for vision models. Footage with existing labels or outcomes is worth considerably more.
7. Sensor, telemetry and machine logs
PLC and SCADA logs, vehicle telematics, building energy data and agricultural sensors. Time-series data becomes valuable when paired with what happened next: a maintenance ticket, a failure, a yield figure. Long histories across many assets are the most sought after.
8. Transaction, logistics and operational records
ERP and order exports, dispatch and routing records, claims and billing histories, warehouse movements. Clean records of what moved, when, where and at what price teach models how real businesses run. They feed forecasting, anomaly detection and planning models.
9. Code repositories
Internal applications, infrastructure and configuration code, code review threads and issue history. Private production code with commits, reviews and tests is richer than public repositories. Licensing is scoped to exclude secrets, customer data and anything you want kept proprietary.
10. Domain-expert annotations and workflow recordings
QA and compliance review records, expert-graded assessments, annotated images and screen recordings of specialist work. These capture judgment that cannot be scraped. They are the scarcest data of all and the most valuable per record, and even modest volumes attract strong interest.
Who holds this data
Most of it sits in systems that were bought for another purpose. By department: support and IT own tickets and chats. Sales owns CRM histories and sales calls. Operations owns dispatch, ERP and telematics. Engineering and maintenance own sensor logs and manuals. Legal and compliance own contract libraries and review records. Finance owns billing and claims.
By industry, the clearest fits are healthcare, for imaging paired with reports, clinical documentation and claims; insurance, for claims notes, adjuster calls and underwriting files; logistics and transportation, for dispatch records, telematics and dashcam footage; and manufacturing and industrial IoT, for machine logs paired with maintenance history. Call centers, law firms, financial services firms and software companies hold comparable assets.
What makes a dataset more or less attractive
More attractive:
- Outcomes attached to records: resolutions, failure types, win or loss reasons, claim decisions.
- Three or more years of history, so the data covers seasonality and rare events.
- Consistent structure: stable fields, timestamps and identifiers that let records be joined.
- Existing labels, grades or review notes written by people who know the domain.
- Clear rights: contracts and consent records that show you can license the data.
Less attractive:
- Short histories or a single system snapshot with no change over time.
- Free text with no outcome, or outcomes with no supporting text.
- Mixed formats that would take weeks to normalize.
- Data a buyer could assemble from public sources at low cost.
None of these disqualify a dataset on their own. They move the price. Any figure quoted before a sample review is an estimate, and actual value depends on uniqueness, quality, rights and buyer demand.
What buyers will not touch
- Personal data sold for marketing. Buyers of training data are not building contact lists. Knowledge Fuel is not a consumer data broker and never sells personal data for marketing. Personal details are removed before any buyer sees a sample.
- Data without rights. If your contracts, vendor terms or consent records do not support licensing, the data is excluded until they do. A buyer will not accept a dataset with an unclear chain of rights, and neither will we.
- Scraped third-party content. Web pages, social posts, licensed databases or partner data you do not own are off the table. Buyers want data you generated or collected under terms that let you license it.
Where to start
Look through the full taxonomy and examples on data we buy, then run the free estimate. It takes about ten minutes, asks for no files and returns an illustrative range for your industry, data types and years of history. You keep ownership throughout, and you approve every buyer and every permitted use.
Questions on this topic
Does my data need to be large to interest a buyer?
No. Buyers pay for uniqueness and context first. Fifty thousand support tickets with resolutions attached are more useful than a million without them, and expert review records attract interest at modest volumes. Any estimate of value is illustrative and depends on uniqueness, quality, rights and buyer demand.
Do buyers want raw exports or cleaned data?
They will take raw exports, but they pay more for data with consistent fields, timestamps and labels. De-identification is handled before any buyer sees a sample, so you do not need to clean the data yourself before asking for an estimate.
What if my data contains customer names and account details?
Most operational data does. Personal information is removed or de-identified before review, and buyers sign terms that forbid re-identification attempts. Data that cannot be de-identified, or that you do not hold rights to, is excluded.
Henry Jackson
Founder, Knowledge Fuel. Knowledge Fuel buys and licenses operational data from companies and sells it to frontier AI labs, model developers and enterprise AI teams.