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    AI for Engineers

    AI Training for Software Engineers

    Practical, team-focused AI training for engineering teams using Claude, Cursor and other AI coding tools. We help you get consistency across the team, governance you can actually enforce, and agreed measures you can review afterwards — including how to design and build agents that work in a team environment, not just impressive demos.

    Two software engineers pair-programming with an AI coding assistant

    Why engineers need their own AI training

    Most AI training is aimed at knowledge workers — writing emails faster, summarising documents, tidying up spreadsheets. That is not the problem an engineering team has. Engineers are shipping code into production, working in shared repos, on-call for the outcome. The stakes and the workflows are different.

    When Claude, Cursor and other AI tools land in an engineering team without a shared way of working, you get three predictable problems: inconsistent code style and quality across pull requests, quiet governance risk (IP, licensing, customer data pasted into the wrong place), and a lot of impressive demos but very few agents or workflows that actually run in production. This programme is built to fix all three.

    What the programme covers

    Six areas we work through with engineering teams — tailored to your stack, your tools and the way your team already ships.

    Tool fluency for engineers

    Hands-on training across Claude, Cursor, GitHub Copilot and other AI coding assistants — what each is good at, where they fail, and how to switch between them without losing flow.

    Consistency across the team

    Shared prompts, house rules, project conventions and review patterns so ten engineers using Claude or Cursor produce code that looks like it came from one team — not ten.

    Governance and safe use

    What is safe to paste into an AI tool, how to handle IP, licensing and customer data, and how to keep human review in the loop on anything that ships to production.

    Building agents that actually work

    Design patterns for agentic workflows — planning, tool use, memory, evaluation and guardrails — so your team ships useful agents instead of impressive demos.

    Productivity, not just code generation

    Using AI across the full engineering lifecycle: spec writing, ticket refinement, code review, tests, debugging, docs and incident response — where the real hours are.

    AI in a team environment

    How AI changes pair programming, PR review, on-call, onboarding and knowledge sharing — and the working agreements that keep quality high as velocity climbs.

    Who it's for

    • Engineering leaders rolling AI coding tools out to a team or a whole org
    • Platform and staff engineers setting standards for how AI is used in the codebase
    • Product engineers who want to move faster without breaking review discipline
    • AI/ML and applied engineers building internal agents and AI-powered features

    Outcomes you can expect

    • A shared way of working with Claude, Cursor and other AI tools across your engineering team
    • House rules and prompt patterns committed alongside the codebase — not lost in Slack
    • Clear guardrails for IP, licensing and customer data when AI touches the repo
    • Repeatable patterns for building agents: planning, tool use, memory, evals and safety
    • Measures we agree up front and review together — for example PR cycle time, review quality and how quickly new joiners reach their first commit
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    Frequently asked questions

    AI training for engineers is a structured programme that helps software engineers use AI coding tools such as Claude, Cursor and GitHub Copilot consistently, safely and productively as a team. It covers tool fluency, prompt and workflow patterns, agent design, governance and how AI changes review, testing and on-call — not just typing prompts in an editor.

    We train on the tools your team actually uses. That most commonly means Claude (including Claude Code), Cursor, GitHub Copilot, ChatGPT and increasingly agent frameworks. We are vendor-neutral and tailor the mix to your stack and licences rather than pushing a single product.

    Generic AI training teaches everyone the same prompts. Engineering training goes deeper into the codebase and the team — repo conventions, PR review, testing strategy, agent architecture, safe use of customer data and licensing. It is built for people who ship software for a living.

    Yes. Alongside training we help engineering teams design and build internal agents — from scoped automations to more ambitious multi-step workflows — with the right patterns for planning, tool use, memory, evaluation and guardrails.

    In-person or live online workshops for a team or cohort, bespoke multi-week programmes for larger engineering orgs, and AI hackathons where engineers ship something real with AI in one or two days. We tailor format and length to team size and how deep the change needs to go.

    We agree measures up front with your engineering leadership — typical ones include PR cycle time, review quality, time-to-first-commit for new joiners, AI tool adoption and self-reported time saved on defined workflows. Course completions are the least useful number and the one we care about least.

    Plan your engineering AI programme

    Tell us about your engineering team, the tools you use and where AI needs to land first. We will come back with a tailored plan.