AI tools and platform rollout
AI-assisted engineering that your team actually ships with
Claude Code, GitHub Copilot and Codex change how software is built, but only when the setup, the standards and the review gates are right. We set engineering teams up to get the gain without the mess, from a team that builds with these tools every day, including this website.
Engineers are already using these tools. The question is how well
In every discovery programme, R&D is the function already furthest ahead: engineers writing and checking code with AI, generating tests, asking general models to make judgement calls on standards, and running into usage limits, inconsistent results and the occasional confident mistake. The appetite is there. What is usually missing is the setup that turns individual productivity into team productivity: shared project instructions, coding standards the tools follow, review gates that catch what they get wrong, token budgets that stop the bill surprising anyone, and a security posture that keeps proprietary code where it belongs.
From individual hacks to team practice
Tool selection and configuration
Claude Code, GitHub Copilot, Codex or a combination, chosen on your stack and your work, configured for your repositories, with enterprise terms that keep your code out of training data.
Project instructions and standards
The instruction files, conventions and architecture notes that make the tools produce code that looks like yours: naming, structure, testing, the libraries you use and the ones you have banned.
Review gates and security
What AI-generated code must pass before it merges: tests, static analysis, human review proportionate to risk, and secret and dependency scanning. Plus the rules for what may be pasted where.
Test generation and QA
Using the tools where they pay back fastest: unit and integration test generation, test-coverage optimisation, and end-to-end tests with frameworks such as Playwright.
Token budgets and measurement
Usage limits that match how the team works, cost visibility by project, and measurement of what changed: cycle time, review load, defect rate.
Agentic workflows for engineering
Where the team is ready: agents that triage issues, draft pull requests, run in-house security testing, or maintain documentation, with the same review gates applied.
We build with these tools, including this site
Enceladus and the PureNet Group engineering teams use Claude Code daily, with the project instructions, review gates and standards we recommend to clients. This website was built that way, in a repository with a content model, a page editor for the marketing team, and a Lighthouse score of 100 across performance, accessibility, best practice and SEO. The advice is what we do, not what we have read.
Two weeks to a working setup
One week of assessment with the team: the stack, the repositories, current tool use, the pain points and the security constraints. One week of setup: tools configured, instruction files and standards written with the team, review gates in place, budgets set, and a measurement baseline taken. Then enablement sessions as the team adopts it, and a review at thirty days. Scoped and fixed before it starts.
About developer tooling
Claude Code, Copilot or Codex?
They suit different work. Copilot is strongest as in-editor completion across a wide range of languages; Claude Code and Codex are agentic, working across a whole repository on multi-step tasks. Many teams run one of each. We decide on your codebase, not in the abstract.
Is our code safe?
With enterprise terms, yes: business tiers of all three commit that your code is not used to train models. We confirm the exact terms, set the rules for what may be shared, and keep secrets out of prompts by design.
Will it lower code quality?
Left unmanaged, it can. With project instructions that encode your standards and review gates that enforce them, teams generally see quality hold or improve while cycle time falls. The measurement baseline tells you which.
Do you train the team?
Yes; developer enablement is part of the training service and runs alongside the setup, on your own repositories.
Give your engineers a setup, not just a licence
Two weeks from assessment to a team that ships with AI and can show what changed.