AI Adoption
From repo-aware agents to day-to-day delivery, this route is for teams that want to learn how agentic AI can support real engineering and operations work.
Build With Agentic AI
This is not basic prompt training. It is hands-on teaching for teams that want to use repo-aware and terminal-aware agents on real work: choosing the task, giving the right context, setting tools and boundaries, reviewing output and diffs, testing properly, and knowing when a human should step in.
Build
Learn the build workflow
Show teams how Codex, Claude Code, Gemini, skills, and local or hosted models fit into app changes, homepage updates, internal tooling, migrations, refactors, and optimisation work from live repos.
Run
Learn the repo & terminal workflow
Train teams to work with repo and command-line flows using bounded scope, sensible context, safe tool use, and clear handoff points.
Control
Learn the review & rollout workflow
Set the workflow and guardrails around tests, diff review, eval loops, tool comparison, and team rules so AI use stays useful and controlled.
How We Work
01
Scope the System
Start with the real setup: repo, docs, tools, constraints, and the delivery problems your team wants AI to help with. No generic workshop deck, just the environment people actually work in.
02
Get Read-Only Access
Where needed, you provide read-only access to Git, M365, documentation, or infrastructure so the teaching is grounded in the real environment, not a made-up demo.
03
Plan, Then Execute
First we define the use cases, guardrails, review points, and team workflow. Then the work becomes workshops, pilot runs, practice, and measured adoption your team can carry forward on its own.