Service guides / AI automation
Acceptance checklist · AI automationForm data validation: acceptance checklist
Define how to recognise a usable result. Start with sample submissions and required field rules and plan a clean submission queue with error reasons.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- sample submissions and required field rules.
- Proposed result
- a clean submission queue with error reasons.
- A decision to resolve
- valid records incorrectly rejected by strict rules.
This is a planning example. It does not describe a completed client engagement.
Acceptance checklist for this project
- Compare the result with a manually checked sample; measure corrections as well as time saved.
- Write the expected content and format of a clean submission queue with error reasons against one agreed sample.
- Include a failing example involving valid records incorrectly rejected by strict rules, and agree the correct response.
Questions to answer together
- What is the expected result for one agreed example?
- Which edge cases must pass before signoff?
- Who accepts the result, and where is that decision recorded?
What to have at the end
A repeatable acceptance pack with examples, expected outcomes and signoff criteria.
Record the decision on valid records incorrectly rejected by strict rules alongside the source material. If it is still unresolved, identify who can answer it and what work depends on the answer.
Prepare your enquiry
Use this as a starting point. Add your current process, available examples and constraints before sharing.
Send your completed brief to hello@iansamonte.com or visit the main page to discuss your project.