OrgParityGoverned AI development enablement
Establish a repeatable engineering workflow with scoped context, reviewable evidence and explicit checkpoints for AI-assisted changes.
Discuss this engagementDefine the work.
One repository and selected engineering workflows, using client-approved assistants and ContextDigger where suitable.
What I investigate
- Relevant code areas and context inclusion or exclusion rules
- ContextDigger bundles and what the assistant actually receives
- Secret handling, provider configuration and tool permissions
- Human review, regression tests and change approval
What you take away
- Configured context areas and reviewed example bundles
- Team workflow and review checklist
- Evaluation examples with recorded outcomes
- Training session and handover guide
The team can repeat the agreed workflow, inspect the supplied context, and demonstrate review and testing of a representative change.
Scope notes
ContextDigger helps organise context; it is not a complete security boundary. Runtime access, provider controls and change approvals require separate configuration.
The proposal confirms access, deliverables, fees, timing and change-control arrangements before work begins.
Where this can lead.
AI workflow readiness review
Where can AI help, and what needs to be controlled?
Explore the engagement AI enablement & knowledgePrivate AI workspace setup
Can we use AI within our own information boundaries?
Explore the engagement AI enablement & knowledgeSearchable technical knowledge
Can our team find a reliable answer and its source?
Explore the engagementBring the problem.
We’ll define the next step.
A release that feels uncertain. A journey that keeps breaking. An org nobody fully understands.