Consistent AI Engineering
Your team adopted AI tools. Velocity didn't change. This four-week engagement installs the practices that make AI produce measurable results: shared workflows across the whole team, context files built into your codebase, and a before/after velocity baseline you can show your board. Your engineers own everything after I leave.
You bought AI tools for your team, Copilot, Cursor, or similar, and three months later you can't see a difference in how fast they ship.
Each engineer uses AI differently. There's no shared practice, no shared context. You can't build on each other's AI output.
Your board is asking what you got from the AI investment. You don't have a number to give them.
You want a measurable baseline, velocity before and after, so you can see the change, not just feel it.
You're planning to run agents on your codebase and need the foundations in place before you can.
Your team ships as a unit. Coordinated AI practices replace individual tool use; everyone working the same way, building on each other's output.
The knowledge senior engineers carry in their heads: architecture decisions, conventions, constraints, written down and readable by AI tools.
Written review standards calibrated for AI-generated output. What to trust, what to verify, and how to catch the specific failure modes AI produces.
Before-and-after measurement across the key delivery metrics. You leave with a number you can show your board and use to track improvement.
Your test infrastructure assessed and improved so it produces the clear, actionable feedback that agents need to work reliably.
What to build next, ranked by business impact. Not a backlog: a sequenced plan with ownership assigned internally before I leave.
I run the full AI maturity assessment across your team, interview key engineers, and review the codebase. Output: a clear picture of the gaps in priority order, with a velocity baseline to measure against.
I write the codebase context files and establish the shared AI development workflow across the team. Done collaboratively; your engineers own it from day one.
I write the code review standards for AI-generated output and make targeted improvements to the test suite. Establish the quality measurement baseline.
I embed the workflow across the whole team, run a final measurement against the baseline, and deliver the 90-day roadmap. Check-in at 60 days to verify it held.
Not sure if this is the right starting point?
Tell me what your team is dealing with. I'll tell you whether this is the right engagement or if something else makes more sense. 20 minutes, no pitch.