How should a model lab scope an approval-authority engagement?

Scoping starts with choosing which dimensions of authority matter to your model, then deciding how much variety you need in each.

The main choices are: - **Authority dimensions.** Amount, risk rating, role, category, region or acting status. Each added dimension multiplies the cases a model must handle. - **Domain.** Financial services, insurance, utilities, logistics, healthcare administration, retail operations or professional services. One domain with depth may suit a first pilot better than several with little. - **Form.** A static dataset, a task pack with graders, or a simulated environment with tools and reset. These can be combined, but each has its own checks. - **Difficulty and splits.** The mix of clean, near-limit and exception cases, and which organisations or policy variants are held out. - **Formats.** JSONL, Parquet, Markdown policies and tool schemas.

Licensing is a conversation, not a default. Ask early about permitted use, such as training versus evaluation only, whether derived models are covered, retention, redistribution and how versions are identified. Terms are agreed for the engagement rather than assumed from this page.

Two limits apply. The output is generated, not real business records, so it cannot stand in for validation against your own operations. And generation does not guarantee privacy compliance or regulatory approval for any use you plan. A sensible first step is a small, tightly specified pilot that you can inspect before widening scope.