Your team owns the build.
Start with AI workflow training and the Factory Line source package. Workshop support is available when you need feedback.
Explore the $2,500 package ↗Prepare evidence-based cleanup suggestions for customer records without silently overwriting valuable context.
Duplicate organizations, inconsistent names and stale fields make automation unreliable. AI can suggest a correction but needs a review boundary for identity and ownership.
Give operations a prioritized queue of explainable record-quality issues.
A data owner selects an approved record set for review.
Identify possible duplicates and inconsistencies with supporting evidence, keeping uncertain matches separate.
A data steward confirms identity and authorizes corrections or merges. Customer history must not be discarded based on similarity alone.
Confirm field ownership and downstream automation effects. Avoid overwriting a recent authoritative update with older evidence.
Measure false duplicate matches, corrected fields, review time and downstream issues avoided.
Before expanding the workflow, compare the result with the current process. Include review effort and exceptions in that comparison so the improvement reflects the team's actual experience.
Read the implementation guide ↗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 ↗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.