Proprietary datasets for AI training and evaluation

To train and evaluate a coding agent, you need real engineering tasks with a checkable outcome: an issue, a snapshot of the repository before the change, the merged pull request with its review thread, and the tests that prove it works, ideally from private repositories that were never public.

A customer support AI agent needs resolved cases with their outcomes, the policies and knowledge articles human agents followed, the actions they took in billing and order systems, and how QA reviewers scored the work, most of which never appears in public FAQ or forum data.

Enterprise and computer-use agents learn from real task trajectories: the request that started a piece of work, the records and files consulted, each action in each system, the approvals and handoffs, and the final outcome, plus the SOP that governed it.

Document AI, enterprise search and RAG systems are trained and evaluated on real company corpora: messy file collections in native formats with folder structure, version history and access metadata, plus questions with known answers and known source passages.

Finance and accounting AI is trained on real bookkeeping work: transactions as coded and corrected by accountants, reconciliations with their exceptions, month-end close workpapers, working spreadsheets and the reviewer sign-offs that approved each step, ideally across several fiscal years.

Legal AI needs private legal work product on top of public case law and statutes: contracts traced from first draft through each round of redlines to the signed version, the playbooks and fallback positions behind each change, and the edits and approvals of supervising lawyers.

Voice agents and speech models need real two-party conversations recorded on the channels they will run on — phone and VoIP audio with accents, line noise, interruptions and hold time — with accurate transcripts and a record of what each call achieved.

AI sales agents and revenue models learn from real deal histories: CRM opportunities with timestamped stage changes, the calls and emails inside each deal, the proposals that were sent, and whether the deal was won, lost or stalled, and why.

Healthcare administration AI, such as prior authorization, coding, claims, denial management and patient access agents, learns from de-identified records of real administrative work: authorization requests and determinations, claims linked to remittances, denials and appeals with outcomes, payer and patient calls, and the payer-specific playbooks staff follow.

Robots and embodied AI models that must do skilled physical work learn from first-person video of trained people doing it on real sites, paired with records of each task and its result.

To evaluate models and agents on real work without contamination, you need private, held-out tasks built from business records that were never published: the context available at a decision point, what a skilled person did next, and the outcome or expert grade that followed.

To license proprietary data for AI training, write a data spec, find a business that holds matching data and agrees to license it, check fit, rights and quality on a manifest and a sample, agree de-identification, negotiate permitted use, price and other terms in a written license, and take delivery through a secure transfer.

Licensed, synthetic and scraped data do different jobs: licensed data supplies real business context, outcomes and documented rights; synthetic data adds cheap, controllable volume but cannot add ground truth its generator lacks; scraped public data gives breadth but brings contamination, quality and rights risk.

Before licensing AI training data, confirm that the licensor owns or controls the data and may license it for your use; that contracts, notices and consents allow that use; that personal, special-category, confidential, open-source and export-controlled content is removed or cleared; that delivery is secure and documented; and that a representative sample passes your quality and contamination checks.

The terms that matter most in an AI data license are permitted use, field of use, exclusivity, duration and territory, ownership of derivatives and trained models, retention and deletion, and the audit, warranty, indemnity and payment terms that allocate risk.