Start with the capability you need to improve.

CEOBuySell qualifies sourcing opportunities for connected business-workflow data. Tell us the task, required evidence and intended use. We assess fit before proposing a dataset or commercial commitment.

Specify the task.

  • The problem, starting context and actions a system should handle.
  • Required sectors, languages, geography, time coverage and volume.
  • Evidence of the actual outcome and how it should be graded.
  • Whether training and evaluation sets must remain separate.

Specify the rights.

  • Training, fine-tuning, evaluation, research, retrieval or analytics.
  • Users, affiliates, territory, term and onward sharing.
  • Exclusivity and the permitted use of resulting models.
  • Sample access, retention, deletion and confidentiality requirements.

A delivery specification, not a folder of files.

For a qualified opportunity, the proposed package should define the schema, data dictionary, provenance summary, rights scope, quality evidence and known limitations. JSONL, Parquet, CSV or a buyer-defined format can be considered during scoping.

Evaluation before production use.

Any sample has its own permitted-use terms. Sample access does not automatically allow training, redistribution or production use. Agree acceptance tests and correction procedures before delivery.

Availability must be confirmed.

This site is a requirements desk, not a live dataset catalogue. We do not imply that a dataset, exclusive source relationship or continuous feed is available until it has been qualified.

Buyer qualification

Define your requirement.

Give enough detail to assess fit. Do not submit confidential model specifications, proprietary examples or source data through this form.

Discuss a data requirement

Describe the opportunity. Do not submit raw datasets, customer or employee details, confidential documents, or credentials. Fields marked * are required.

Read our privacy notice before submitting. An enquiry grants no data, licensing or AI training rights.

Describe the problem, relevant context and how success will be measured.

Include languages, geography, approximate volume and any additional uses or exclusions. Do not include proprietary examples.

Required outcomes, labels, provenance, evaluation separation, sample review and rejection criteria.