Your developers use AI every day, without guidance
Most of your developers taught themselves AI coding through trial and error and tips from colleagues. Nobody guides them on live work. The rules on which tools and data are allowed are unclear. And AI output often needs so much review that the speed you hoped for disappears. You may already have a policy on which tools are allowed. What your developers are missing is the practical skill: how to break a feature into tasks an AI agent can handle, how to review a pull request the AI wrote, and when to stop prompting and write the code themselves. A coach builds that skill with them, on live projects.
When to use AI, and when not to
Which tasks AI handles well in your stack, which it gets wrong, and how your developers tell the difference before a change reaches review.
Reviewing AI output
How to check code, tests and documentation the AI produced, so trust comes from a method and review time comes down.
Reshaping how you build
Adjusting planning, estimation, review, testing and release for a team where people and AI agents both contribute.
Setting up the team
How to organize for AI-assisted delivery, including small AI teams that take a problem from the business and turn it around in short cycles.
90 days inside your team, on real work
Weeks 1–2, Baseline and setup
We start by measuring where the team is today: cycle time, review effort, defects. Then we agree the working rules with its leads and set up the AI tooling with BAS, so the AI works from your architecture rules and coding standards.
Weeks 3–10, Coached delivery
The coach works inside the team's normal cycle, pairing with developers on live work and adjusting the approach at each retrospective. The team ships its project; the coach guides.
Weeks 11–13, Measure and write up
We measure against the week-one baseline, write up the team’s way of working with the people who use it, and leave TRACE in your pipeline to catch regressions in future AI-assisted work.
A team that builds with AI on its own
One shipped project
A project from your own backlog, delivered by your team.
Measured results
Cycle time, review effort and defect rates, compared against the week-one baseline.
A way of working your team wrote
The adjusted lifecycle, documented by the people who use it, with BAS and TRACE configured for your codebase.
Internal champions
Developers and leads ready to coach the next teams the same way.
Where this leads next
Write it down
Turn the new way of working into procedures your teams and auditors can follow, with the AI SDLC Playbook & SOPs.
Need extra build capacity?
Hand a specified application to our engineers through AI Application Development.