How we use AI.The right agents and skills, on every project.
We bring the best of what AI can do into how we build software, and turn it into software that's scalable, maintainable, reliable and ready for real users. Often that means taking an idea or a prototype all the way to production, safely and securely. Here's how we set that up.
An AI agent beside every specialist
Every specialist in a pod works with an AI agent built for their discipline. The agent takes the routine work; the specialist does the specialist work and signs off everything the agent produces.
An agent is made up of skills: individual pieces of our know-how, such as "write user stories from a workshop transcript" or "review a pull request against our standards". We've built and tested these skills across more than 100 products.
The agent roster
Every agent applies our best practices, and every one works beside a specialist who signs off its work.
| Specialist | Agent | Skills it carries (examples) |
|---|---|---|
| Business analyst | BA agent | User stories from workshop transcripts · acceptance criteria · gaps and open questions · requirements traceability · business documentation from existing code · BA audit: every spec checked against our standards for gaps before the business analyst reviews it |
| UX designer | UX agent | Screens from our UX principles and your brand · user flows · screen book designs · your component library built in · edge cases handled (empty states, errors, slow connections) · accessibility checks |
| Architect | Architect agent | Structure and data model · API design · integration planning · scalability and security review · test strategy: which automated tests and how much coverage, to our standards for your stack |
| Developers | Developer agents (back-end, front-end, mobile) | Code to our standards for your stack · planning the work on each ticket · unit tests with every change · first review of every pull request · security checks (secrets, dependencies) · refactoring · upgrades and migrations · bug investigation and root cause · documentation |
| QA engineer | QA agent | Test plans · automated tests, including front-end tests · regression checks · tests optimised to run fast in the CI/CD pipeline · performance testing |
| DevOps engineer | DevOps agent | Pipeline set-up · scripts that run production checks · monitoring and health checks · release checklist |
We choose the right agents and the right combination of skills for your stack and your industry at the start of every project, and adjust them as it grows. Our skills stay with us; everything they produce for your project is yours.
Every discipline, working at once.Agents and skills behind every one.
On a real project, the work doesn't happen one step after another. Analysis, design and architecture start together in week one; developers and QA join as the plan takes shape; and every specialist's agent is applying its skills the whole way through. Run several pods side by side and it starts to look like magic.
People stay in charge, accelerated with AI.
- 01
People plan and make the decisions. AI makes the building faster once the plan is set.
- 02
Nothing merges without a named engineer's approval. They use AI and our review skills to improve the code and check it against our standards, and against the principles of what can go wrong.
- 03
Your code and data are never used to train AI models. Our AI tools run on commercial licences that don't allow it.
- 04
If your company has an AI policy, we follow it, including not using AI on your project at all.
Where it ends: in production
All of this has one job: getting your software into the hands of real users, secure, scalable and easy to change, and faster than a traditional team could. Our teams deliver 2–4× faster with this set-up than we could a year ago.
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