How to Find Proprietary Data Sources for a Specific AI Use Case

This page covers the discovery technique. For a comparison of sourcing channels (brokers, marketplaces, direct licensing, custom collection), see the AI data buyer hub; for the wider procurement lifecycle, start at the AI training data procurement hub.

Start from the acquisition purpose, not the dataset name

The first output of data scouting is a one-paragraph purpose statement, because "support tickets" or "invoices" describes thousands of incompatible datasets. Practitioner guides on buying datasets make the same point: articulate why you are acquiring data before you search for sources [1]. The purpose should fix four things: the training stage (pre-training, SFT, preference tuning, evaluation, agent trajectories), the decision or action the model must produce, the minimum fields that make a record usable, and the evidence of outcome you need.

The training stage changes where you look. An SFT set for instruction-response pairs built from business records needs a request and a resolved answer in the same record. An eval set needs outcome labels from real business decisions, such as approved or denied, refunded or not, escalated or closed. Agent training for computer use needs step-level actions across applications, which most systems of record never store; the enterprise agent training data page covers that category.

Write the purpose so a non-ML counterparty can read it. A data holder's operations lead will decide whether they recognize their own records in your description, and jargon such as "multi-turn tool-use trajectories" stalls that recognition.

Map the workflow to its systems of record

Every operational workflow leaves records in a small set of systems, and naming them tells you which fields actually exist. The table below is the core artifact for this step: it links a target behavior to the system that stores it, the objects and fields to ask about, and the trap that most often makes an export unusable.

Illustrative example: invented to show structure; it does not describe an available dataset.

Two details deserve explicit questions. First, ask whether the holder can export history, not just state: Zendesk, for example, records ticket audits with change events that include previous values and authors [2], and that sequence is often the most valuable part for agent or eval data. Second, ask how records are joined across systems; CRM platforms support External ID fields that identify the same record in another system [3], and the presence or absence of such keys decides whether a multi-system record can be assembled at all.

Check public alternatives at this stage so you know what proprietary data must add. For example, TWEETSUMM, a widely cited customer-service summarization dataset, is built from chat-style conversations [4], which leaves phone calls, internal notes and resolution outcomes uncovered.

Map systems to holder types and sizes

Once you know the system, the holder is "any organization that runs this workflow at the volume you need," and you can reason about which kinds of organizations those are. Volume, tenure and process discipline matter more than brand: a regional insurer that has run the same claims platform for a decade may hold cleaner longitudinal records than a larger firm that migrated twice.

Use three filters to sort holder types:

• Volume per holder. Estimate records per year from the workflow. A 40-seat support team handling 30 tickets per agent per day produces roughly 300,000 tickets a year; a five-person AP team may process tens of thousands of invoices. If one holder cannot reach your target, plan a multi-holder schema from the start.
• Process standardization. Holders that use controlled vocabularies (disposition codes, GL accounts, resolution categories) produce labels you can train on; holders that rely on free text produce data you must relabel.
• Organizational type. Operating companies, outsourced service providers (BPOs, managed service providers, third-party administrators) and software vendors may all touch the same records but hold very different rights to them.

The outsourced-provider case is the most common trap in data scouting. A BPO may run millions of support interactions, but its client contracts usually say the records belong to the client, so the provider cannot license them. Software vendors face the same constraint with customer data stored in their platforms; read the AI data buyer hub for how channel choice interacts with that problem.

If no holder plausibly has the records, that is a signal to compare custom data collection with licensing existing data instead of continuing to search.

Screen rights friction by holder type before outreach

Rights friction is predictable by sector and data type, so score it before you contact anyone. The goal is not a legal opinion but a triage: which holder types can say yes with ordinary approvals, which need de-identification, and which face consent problems that no contract can fix.

Illustrative example: invented to show structure; it does not describe an available dataset.

Your own downstream obligations also shape which holders are usable. As of October 2026, providers placing general-purpose AI models on the EU market must maintain a copyright policy and publish a training-content summary, duties that have applied since 2 August 2025 [10], and California's AB 2013 requires developers of generative AI systems offered to Californians to post documentation about their training data, which was due by 1 January 2026 [11]. If a holder will not allow its data category to be described at that level, it may be the wrong source for a public model, regardless of quality.

For deeper privacy handling, see the buyer's guide to de-identified data for AI training. This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Turn the map into a holder-agnostic data request

The output of scouting is a request that describes records, not companies, so any qualified holder can recognize itself in it. This also keeps your request usable across channels, whether you approach holders directly, run an RFP for training data, or work through an intermediary.

Illustrative example: invented to show structure; it does not describe an available dataset.

request_id: example-support-eval-01
purpose: "Evaluate a support agent on resolving billing disputes end to end"
training_stage: evaluation
workflow: "B2B SaaS billing dispute, from customer ticket to credit or denial"
system_of_record: "ticketing system plus billing or ERP credit memos"
required_fields:
  - ticket_id (stable, joinable to credit memo via external key)
  - full comment history, public and internal, with timestamps and author role
  - status and field-change history
  - outcome: credit_issued | denied | escalated, with amount band
record_volume: "5,000 resolved disputes minimum; 2023-2026"
holder_profile: "operating company running its own support desk; not an outsourced provider"
exclusions: "no scraped content; no standalone contact lists"
privacy: "names, emails, phones and account numbers removed or replaced before delivery"
format: "Parquet or JSONL, one row per ticket event"

Write acceptance tests into the request early. Quality frameworks such as ISO/IEC 5259-4 treat data quality as a process covering training and evaluation data, including labeling [12], and the same thinking applies here: define completeness of the history, outcome coverage and join rate before you see a sample. Then use a structured sample request and the data provider due diligence questionnaire to test what the holder actually has.

If only one holder type can supply the records, document the reasoning from this mapping exercise; it becomes the core of a sole-source justification for proprietary data.

Where an intermediary fits in data scouting

An intermediary is useful when your request is clear but you cannot identify or approach holders efficiently yourself. SourceX sources operational datasets from US companies on request (nothing is held in stock, and a request does not guarantee a match) and looks for businesses that hold the data you describe; buyers describe the records, not the companies. The kinds of data include support and sales histories, engineering records, documents, finance and legal workflows, and new recordings of hands-on work. SourceX does not source scraped web content, standalone contact lists, or generic CCTV or photos.

Every release is approved by the supplying company, and each dataset is reviewed for ownership and consents and delivered under a license that defines records, uses, term and delivery. You can submit a holder-agnostic data request built from the template above.

Find holders of the records your model needs

SourceX looks for US companies that hold the operational records you describe, assesses data and licensing permissions, and agrees pricing and allowed uses in a license before anything is delivered; nothing is contracted until a supplier agrees. It serves AI teams wherever they are based. Describe the records you need.