Turn Your Dev Teams Into AI-Powered Business Engineers
Most AI training stops at a sandbox. Our AI Enablement Program embeds directly into your codebase, your tickets and your workflows — so your engineers build practical skills against your real business context, not a generic tutorial.
Most AI training fails the moment people open a real repo
Engineers come back from generic AI courses with prompt tricks that work on theoretical problems and break on real ones. The result is partial adoption, suspicious security teams, and a growing gap between the AI hype and what actually ships. We built this program to close that gap by helping your team with the work they already do.
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Faster pull request cycles
Teams that complete the program report code reaching review and merge meaningfully faster than before.
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Daily AI adoption
Across engaged teams, the vast majority of engineers use AI tooling on real tickets every working day.
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From kickoff to scale
A typical engagement moves from foundation to organisation-wide rollout in less than a month.
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Hands-on, in your codebase
No isolated sandboxes. Every exercise runs against your real repositories, your tickets, your stack.
A three-phase program designed to compound
We don't deliver a workshop and disappear. Our enablement program is built as a story your team lives through. From foundations, to applied work in their codebase, towards scaling adoption across your organisation.
The Foundation: Build the muscle, not the hype
We start with workshops and guided practice that teach your engineers how LLMs actually process context, how to prompt for engineering tasks (not marketing copy), how to evaluate AI output critically, and how to configure agents around your real tech stack. By the end of this phase, your team has the vocabulary and the instincts to use AI without superstition.
The Implementation: AI coding agents inside your codebase, not a sandbox
Your team works on real user stories in your code repositories. We pair on AI-assisted code review, refactoring, test generation and CI/CD workflows, and we wire up MCP integrations into the internal tools you already use. This is where adoption stops being theoretical and starts shipping pull requests.
The Scale-up: Turn early wins into organisational leverage
Once a few teams are productive, we help you turn that into a scalable system: governance frameworks, internal tooling and templates, impact measurement, and a group of internal champions who keep the practice alive long after we left. Adoption becomes a capability, not a checkbox.
Tailored for teams making impact
The program is designed for engineering leaders and individual contributors at any level. Whether you're leading adoption or diving deep as an IC, we tailor the experience to your role and context.
For Engineering Leads: You want your team adopting AI on real work, not in tutorials. We work with you, embed in your sprints, and leave behind a team that ships faster with fewer regressions.
For CTOs: You need a AI strategy across multiple teams that sticks. Measurable impact, defensible governance, and a clear answer when asked what AI is doing for the business.
For Security Teams: You need engineers to move fast with AI without leaking data, bypassing review, or shipping unvetted code. We build the guardrails into the workflow itself, not on top of it.
Teams shipping after our program
Don't take our word for it. This is what the AI Champions are saying about their experience.
Hands-on from day one. Not three days of theory, just building real workflows with Claude Code agents, skills, rules and MCP servers. The biggest shift for me was being explicit about what the AI must not do: stricter guardrails, more reliable output, a true foundation for our team.
How is this different from a typical AI training course?
Most courses teach prompting in a sandbox and end at a slide deck. We embed in your codebase and your sprints. The deliverables are real merged pull requests, working agent configurations, and a team that uses AI every day — not a certificate.
Which AI tools and models do you cover?
We are tool-agnostic and work with what your team already uses or plans to adopt — Claude, GPT, Cursor, Copilot, agent frameworks, and MCP-based integrations. We focus on patterns and judgment that transfer across vendors as the ecosystem evolves.
Does our code or data leave the building?
No. We work inside your environment, on your repositories, under your existing security controls. Phase three of the program is dedicated to making sure governance, auditability and data boundaries are explicit before you scale across the organisation.
How long does a typical engagement take?
Most engagements run about 2-3 weeks across the three phases, but we tailor the depth and pace to your starting maturity. Some teams already have foundations in place and skip ahead; others need more time on governance before they scale.
What does our team need to commit?
A small group of engineers ready to use AI on real user stories, an engineering lead or CTO as a sponsor, and access to the codebases and tooling the program will touch. We meet your team in their existing workflow rather than pulling them out of it.
How do you measure success?
We agree on baselines up front — typically cycle time, review throughput, AI tooling adoption, and qualitative engineer confidence — and report against them throughout the engagement. The goal is impact you can defend, not vanity metrics.
Pair Enablement with Protection
Your teams will be more productive with AI. ContextGuard® ensures they are also more secure. Teams that adopt AI coding agents with governance and infrastructure-level security see faster adoption and fewer incidents.
- See every data flow between your codebase and AI agents
- Enforce policies without slowing engineers down
- Audit-ready trail for the EU AI Act and GDPR
Ready to make AI a daily habit on your team?
Start with a conversation. Every team starts from a different place. We will tailor the program to your current AI maturity, toolchain and goals — foundation, applied work, and a path to scale across the organisation.
Talk to our enablement teamWe will discuss your team's current AI usage and recommend a starting point. No obligation.