The takeaway
Start with what your business sees, knows or does that is difficult to reproduce. Then look for the evidence that connects those experiences to outcomes.
Distinctive data does not require a technology company
A plumbing company may have years of diagnostic notes connected to repairs and callbacks. A manufacturer may know which machine readings preceded a failure. An architecture firm may retain design revisions alongside engineering calculations and the comments that changed a project. Those records describe something a generic internet document may never capture: how a particular problem unfolded in the real world.
The opportunity is not limited to large employers or businesses that call themselves AI companies. A small specialist can have unusual cases, hard-to-access observations or unusually consistent documentation. Size still affects available volume and delivery capacity. It simply does not answer the whole question.
OpenAI’s published data partnership program describes interest in data that is not easily available online, across multiple formats. That establishes a research rationale for looking beyond public content. It does not establish demand for a particular company’s inventory or a price for it.
Five places to look inside your business
Retail, logistics, agriculture, healthcare, professional services and construction can all contain these patterns. These are examples to investigate, not claims that every organization in those sectors has licensable data. A record held by your company may still contain material controlled by a customer or another party.
The useful unit is often a complete example
Consider a maintenance record. “Pump failed” is a thin observation. A richer example includes the operating conditions, the readings before failure, the technician’s diagnosis, the repair, and a later performance check. If those pieces share stable identifiers and timestamps, a buyer can investigate a much more specific task.
For a service business, the equivalent may be an initial request, the options considered, a quote, an approval and the final resolution. For a design practice, it may be a draft, review feedback, a revision and acceptance. The connected sequence can matter as much as the number of individual files.
Use our data quality framework to think about linkage, missingness and outcome coverage. A unique story with unreliable records still needs preparation.
Match the asset to the commercial path
An existing collection can become a defined dataset license. Fresh observations can support ongoing delivery. Documented judgment may support expert workflows or evaluation tasks. Sequences that show how a system changes after an action can justify investigating a specialized model partnership.
These paths have different requirements. They are not a fixed ladder of increasing value. A clearly scoped dataset that solves an immediate research problem can be a stronger first offer than a much larger development partnership that nobody has yet defined.
A useful first description takes one paragraph
Describe the subject, how you collected the records, the history available and what makes the collection difficult to reproduce. Add whether outcomes connect to the source records and whether new observations continue to arrive. If you are unsure about permissions or exportability, say so.
For example: “We have six years of equipment inspections, including technician corrections and subsequent repairs. Records share asset identifiers. We would need to review customer permissions before sharing a sample.” This is an illustrative description, not a customer case study.
You do not need to attach the records to start. The OTD assessment includes a unique-data question and a place for a high-level description. We use that information to shape the next conversation before discussing a sample, potential buyers or commercial terms.
Sources & further reading
Sources checked October 13, 2026.
