Is an intake dataset a good fit for our catalogue and customer base?
A short fit test for resellers deciding whether a generated intake line suits their catalogue, covering customer needs, licence permissions, schema documentation and risk review.
Use this list to decide whether a generated intake line suits your catalogue and the customers you serve.
- Your customers build triage, routing, document-checking or case-handling agents and need material to train or test them. - They need messy, incomplete and contradictory submissions, not only clean records that already passed every check. - They can use reason codes and handover notes as supervision, rather than outcome labels alone. - Your own licence arrangements allow you to license generated material onward, and you have confirmed the permitted uses in writing. - You can describe the schema, version it and give buyers a change note when it alters. - You have a process to inspect grounding and disclosure risk before anything is listed or sent to a customer. - You know which domain your buyers care about most, such as lending, claims, utilities or logistics, and can ask for depth there. - You can support buyers who need exceptions covered, including reopened cases, duplicates and escalations.
Run this fit test before spending catalogue space: it separates real buyer demand from a line that merely sounds useful.
- Audience:
- Licensed resellers
- Content type:
- Checklist
- Buyer question:
- What it is and why it matters
Content release: r-a220eb6e099b243b
Related questions
- What happens when an intake agent finds a loan file incomplete?
- Which signs show an intake process is worth modelling for agents?
- Which systems and links does an intake agent work across?
- What does application intake look like inside a real business?
- Why is intake a good test for AI agents?
- What does a single intake decision look like when an agent has to change state?