Start with a defined trigger
A data owner authorizes a defined set of quality checks.
Quality check-to-owner review
This is a proposed workflow pattern. We confirm data access, permissions, integration options and review ownership with your team before implementation.
Do not silently replace missing values or treat unusual data as wrong without evidence.
A data owner authorizes a defined set of quality checks.
Collect permitted records, approved validation rules, source-system ownership and relevant process dependencies.
Group exceptions and explain their possible operational impact, marking uncertain relationships.
Data owners verify findings and approve corrections or changes to validation rules.
Record accepted issues and route approved remediation to the authoritative system.
Measure false positives, missed consequential errors, ownership gaps and triage effort.
Keep the input, relevant context, review decision and final result connected. When a reviewer corrects something, use that evidence to improve the instructions or integration, then validate the change against representative cases.
The workflow stays centered on your business. The engagement determines who learns, implements and operates it.
Start with AI workflow training and the Factory Line source package. Workshop support is available when you need feedback.
Explore the $2,500 package ↗Get everything in DIY, three months of workshops, weekly team sessions and direct implementation coaching.
Explore guided implementation ↗Include all the learning and guidance, with ownership of agreed operations and major implementation decisions.
Explore managed delivery ↗Prepare likely duplicate records for careful review using explicit identity evidence.
Explore this solution ↗Bring a process, a bottleneck, or an idea. We'll help you decide what to automate, what to keep under human review, and where to begin.