Your delivery playbook may teach a model more than a folder of old reports.

Start here

Does any of this sound familiar?

• Your team repeats a structured process for discovery, analysis, implementation, or quality review.
• A senior consultant can explain why one branch of the playbook applies and what evidence would change that choice.
• You want to understand possible AI use without handing over client deliverables or exporting a project archive.

A consulting playbook captures context, judgment, and what happens next. That can make a designed example useful for AI training or testing; it does not make the firm's project drive reusable.

Start with the work pattern, not a file request. A no-export inventory can separate firm methods from client material and show whether an outcome can be described without identifying a client.

The problem

A method is not the same thing as its client evidence

A team diagnosing a delayed warehouse rollout might compare dependencies, owners, and change approvals before recommending recovery. The reusable method is its questions and decision rules. A client's staffing chart, contract dates, interview notes, and project plan remain separate records with their own restrictions.

Historical deliverables may mix confidential information, licensed research, and outdated assumptions. A slide deck may show a recommendation but omit rejected alternatives, supporting evidence, and results. A useful AI task specifies permitted inputs, the judgment expected, and how a qualified reviewer will assess the answer.

The reusable asset may be the decision rule; the client file is not automatically yours to reuse.

The solution

Turn a repeatable method into a bounded task

Treat a prospective example as governed work: establish task usefulness and separately verify source permissions.

DataSupply partners only with labs that meet its top 0.01% credibility standard. We help assess whether a qualified buyer may be a fit and negotiate terms that reflect the data's potential value, including exclusivity where relevant. We also help you work through diligence questions about rights, privacy, security, and compliance, then present a high-level inventory of permitted records, not the dataset. Fit is specific to each situation; no buyer or value is guaranteed.

What to inventory before any buyer conversation

• Describe one work decision Write a task card naming the role, permitted inputs, constraints, decision, and completion criteria. For a rollout review, check whether dependencies and escalation owners are identified, not whether a model reproduces a proprietary answer.
• Trace each example to its rights Mark elements firm-created, client-provided, licensed, or mixed. Check engagement terms, confidentiality, subcontractor rights, and model-training, evaluation, derivative, and disclosure limits. Exclude anything until its owner confirms authority.
• Record reviewer judgment and version Keep the approved task, rubric, reviewer rationale, outcome, redactions, source category, and version together. Another consultant should understand why an answer passes and when to escalate.

Set the boundaries before discussing access.

Prefer synthetic or expressly authorized examples; de-identification does not cure contract restrictions. Put purpose, access, retention, deletion, security, model and derivative rights, audit, and incident handling in counsel-reviewed terms. Never send credentials or client exports to an unverified party.

What could make a permitted example useful?

Realistic, reviewable examples may cost more to recreate than generic text, but task work differs from licensing a historical corpus. Preparation, review, rights, and use affect feasibility; neither a benchmark nor reported task work sets your price.

A practical first step.

Draft one generic task card without files. Ask its method owner and counsel to identify exclusions and approval authority.

datasupply.ai can discuss possible fit and buyer questions without receiving your dataset. You decide whether to pursue any introduction. No buyer, license, or payment is guaranteed.

Documented example / what it proves

A public benchmark illustrates the task-and-evaluation distinction

SWE-bench describes evaluating language models on real software issues collected from GitHub: a model receives code and an issue, then generates a resolving patch. It illustrates bounded tasks with assessable results rather than reliance only on a general historical corpus. Read SWE-bench project.

Keep task work distinct from corpus licensing: a task arrangement may pay for experts to define or assess bounded work, while a historical-data license grants defined rights to use operational records. SWE-bench is a software analogy, not proof of consulting demand or rights to reuse client records.

The important limit: The benchmark documents task-based evaluation, not a closed data-license deal, consulting buyer, payment, or rights to private client work.