Service guides / AI automation
Data quality checks · AI automationEmail enquiry routing: data quality checks
Agree what a valid source record looks like. Start with redacted enquiries and team routing rules and plan a labelled queue with an assigned owner.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- redacted enquiries and team routing rules.
- Proposed result
- a labelled queue with an assigned owner.
- A decision to resolve
- ambiguous requests and messages needing a personal response.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check redacted enquiries and team routing rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for ambiguous requests and messages needing a personal response.
- Keep unresolved records separate from a labelled queue with an assigned owner; record the reason rather than silently filling a gap.
Questions to answer together
- Which fields, references or measurements must be present?
- How are duplicates, contradictions and missing values reported?
- Which corrections require confirmation from the source owner?
What to have at the end
A validation checklist with examples of valid, rejected and unresolved records.
Record the decision on ambiguous requests and messages needing a personal response 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.