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AI coding assistants
Roll out Claude Code, Cursor, Codex and Copilot with guardrails, and measure what they change.
We help engineering teams adopt AI coding assistants deliberately: the right tool for each team, agentic workflows wired into your codebase and internal tools, clear rules on code and data, and honest measurement of the effect on delivery.
Weekly active developers
Payments team82%
Mobile team64%
Data team71%
Pull request cycle time
Cycle time down since the pilot began
What it means in practice
- Choosing and piloting Claude Code, Cursor, OpenAI Codex and GitHub Copilot against your codebase, languages and security needs.
- Agentic workflows: spec-driven development, background agents and AI code review that fit your pull-request process.
- MCP servers that give assistants safe access to your internal docs, tickets and tools.
- Guardrails for data handling, secrets, licences and code review.
- Enablement: playbooks, workshops and prompt patterns for how your teams actually work.
- Measuring impact on cycle time, review load, quality and developer experience.
What you get
- A rollout plan and a pilot with selected teams
- Usage policies, guardrails and approved MCP integrations
- Enablement sessions and internal playbooks
- An impact dashboard with a baseline to compare against
Typical stack
- Claude Code
- Cursor
- OpenAI Codex
- GitHub Copilot
- MCP servers
- Your IDEs and repositories
We work with the tools your teams already use.
How it runs
- AssessWe review your architecture, data, goals and constraints, and agree what success looks like.
- DesignTarget architecture, a delivery plan and the measures we will be judged on.
- BuildShort iterations, working software early, built alongside your team in your accounts.
- RunWe operate it to agreed service levels, improve it, or hand it over with runbooks.
Tell us where your AI is stuck.
One conversation with an engineer. No sales script.