The problem
Almost every engineering team now uses AI assistants. The organisation rarely gets faster as a result. Individual developers save minutes, but reviews pile up, testing lags behind and nobody can say which code came from whom - or what.
Productivity doesn’t happen in the editor. It happens in the system.
Our approach
We don’t bolt AI onto existing processes as a tool. We redesign the process around it:
- AI Software Factory - a standardised pipeline from requirement to deployment, where agents handle routine work and humans make the calls.
- Agentic Development - agents that read tickets, write code, add tests and prepare pull requests. With clear boundaries and human approval.
- AI Governance - guardrails for models, data and accountability, so speed never comes at the cost of security or quality.
How we work
| Phase | Duration | Outcome |
|---|---|---|
| Reality-check sprint | 2-4 weeks | Maturity, bottlenecks, prioritised use cases and a first measured result on one slice - fixed price, credited towards the pilot |
| Pilot | 6-8 weeks | One team, one product, measurable before-and-after |
| Rollout | ongoing | Factory set-up for more teams, enablement, operations |
We make the first assessment from your description, without confidential content - legacy code included. Code access only after an NDA and your approval.
All of this also works in your environment - in your cloud, on-premises or without internet access. For classified or export-controlled data we work exclusively there, with security-cleared staff.
What you get
- A delivery pipeline in which agents actually contribute - not just suggest code.
- Measurable metrics: lead time, review effort, defect rate.
- Governance rules that stand up to audits.
- Teams that can evolve the system themselves.
AI makes good teams faster. It only makes bad processes louder.
