Service guides / AI automation
Acceptance checklist · AI automationSpreadsheet consolidation: acceptance checklist
Define how to recognise a usable result. Start with sample workbooks and column definitions and plan a merged dataset with a validation report.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- sample workbooks and column definitions.
- Proposed result
- a merged dataset with a validation report.
- A decision to resolve
- conflicting identifiers and inconsistent units.
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 merged dataset with a validation report against one agreed sample.
- Include a failing example involving conflicting identifiers and inconsistent units, 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 conflicting identifiers and inconsistent units 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.