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 ↗Help reviewers spend time on the change's substance with a traceable starting point.
What behavior changes? Which tests actually ran? Where should reviewers concentrate their attention?
Do not claim tests passed unless execution evidence exists. AI review preparation does not constitute approval to merge.
We use these answers to agree on integration boundaries, responsible reviewers, access controls and a definition of done. The proposal identifies dependencies and what is included before implementation begins.
Pilot a set of completed changes and compare proposed briefs with author corrections and review findings.
Measure unsupported test claims, missing behavior changes, irrelevant commentary and reviewer preparation time.
Save the accepted description and reviewer checklist with the pull request.
Document the owner, support path and recovery procedure. Define how the workflow behaves when information is incomplete, access fails or a reviewer declines the proposed result. Agree on what needs fresh validation after a model or integration change.
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 a scoped implementation plan from the repository's actual structure, conventions and test boundaries.
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.