What we do.

Most engineering teams are curious about AI coding but haven’t shipped with it in their own code. The gap is rarely tooling — it’s confidence and shared practice. A few people experiment on the side while the codebase, the review culture and the Monday routine stay untouched.

Unbound closes that gap in two days. Your team works in your own codebase (or realistic proof-of-concept repos) with Claude and Claude Code as the primary tools. Day one opens with a talk on agentic coding — what it changes, what it doesn’t — before teams start hacking with facilitators rotating between them. Day two is the final push: live demos, judging, and a retro built around one question — what do we keep doing after this?

You leave with demoable prototypes tied to real pain points, a team that has actually done it rather than read about it, and a shared vocabulary that survives the Monday after.

Buying AI tools is not a strategy. The value goes to organisations that change how they work — and most leadership teams are missing a clear, shared picture of where they actually stand and where to start.

We build that picture. We survey your leadership team, then spend a full day in a workshop testing the findings against how the work really gets done. You get a report with an independent assessment and a prioritised roadmap: where you are, the highest-impact moves next, and why.

The outcome is alignment you can act on — a leadership team that agrees on the starting point and a concrete sequence of moves, instead of a backlog of pilots.

AI fluency isn’t optional anymore — but generic training doesn’t stick. A lecture on what AI can do changes very little. What sticks is practice, tied to the actual work and the actual tools being rolled out.

We run two formats. Org-wide AI training is a packaged session of around two hours that gives everyone, across functions, a shared baseline in the tools you’re rolling out — platform-agnostic, whether that’s Claude, Copilot or a mix. Hands-on workshops are smaller-group, higher-intensity days tailored to one function’s real workflows — finance, support, engineering — where people work on their own tasks and leave with methods they use the next day.

The outcome: a common vocabulary across the organisation, and pockets of real, demonstrated practice where they matter most.

AI adoption is a journey, not a project — much of the value is decided in the months after kickoff. So we don’t disappear when the engagement ends. We stay available as a standing sounding board for the people carrying the change forward: reviews, course corrections and second opinions, from people who build.