SERVICE APPLICATIONS
Data Cleanup
Duplicate customers, inconsistent job statuses, and incomplete records create problems that AI cannot reliably resolve on its own. We review the data your workflow depends on, agree how records should be structured, and prepare controlled corrections with a record of what changed.
Find a practical starting point for your business.
Describe the process and the systems you use. We’ll discuss where automation could help and what a useful first project would involve.
ENGAGEMENT SCOPE
Clean records with traceable changes
These deliverables form the starting point for your proposal. We agree the systems, responsibilities, and acceptance criteria before work begins.
- Data quality report
- An assessment of missing fields, conflicting values, duplicate candidates, and inconsistencies across the selected datasets.
- Correction rules
- Agreed field formats, record-matching criteria, and a review queue for changes that cannot be resolved safely by rules.
- Validated dataset
- A cleaned export or agreed system update, a change log, and validation checks to help prevent the same issues recurring.
How we deliver the work
We work from a controlled copy or export first. Your data owner reviews matching rules and ambiguous records before changes are applied. We reconcile counts and selected records after the update and agree backup and recovery arrangements before any production import.
Will AI automatically merge similar customer records?
Not merely because the names look alike. AI can suggest possible matches, but shared names or addresses are not sufficient evidence. We establish matching criteria and send ambiguous cases to your data owner.
Application examples
Explore a workflow below to see the proposed process and where your team stays involved.
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