"We were recommended some large organisations for AI training. But in the end, we used Chris, and he's been great. We've had some fantastic outcomes so far."
VenturEd had made a solid start. Microsoft 365 Copilot was available across the company, and engineering teams had access to the strongest AI models via GitHub Copilot. But access to tools and knowing how to unlock value from them are different things. When we surveyed the teams before training, that gap was clear.
The engagement began with a full day in-person for the UK engineering team in Leicester. The content was built around VenturEd’s own tech stack, their own products, and real examples drawn from across the business. The following week, remote training ran across two cohorts to reach the US and India teams. The same principle applied throughout: exercises came from their world, not a generic curriculum.
Within weeks, engineers were applying the techniques to live work, and the most striking results came from people working well outside their comfort zone.
Ravenna saw more besides. Christian Fayolle shipped its first AI feature, in a stack he had never used before.
"The stack was PHP and Apache, and I'd never used either before. But I knew what I wanted the outcome to be, and it took about two days to get the feature built. The key change is just knowing what you want to do rather than how to do it."
The same pattern showed up across the India team. A product that had not been touched in six months, because there had never been the right people with the time to take it on, was suddenly moving again. An engineer who had never seen the language worked through the entire codebase with AI, documented it, and started shipping changes.
"One of my team members from India picked up a language that was completely new to them, went through the entire codebase, documented it, and now they're making changes to a product that hadn't been touched in six months. Massively impactful."
The wins came from every direction. Dave Gardner was running multiple agents in parallel and built himself a system to manage them, alongside daily summaries, pull-request reviews, and DevOps backlog work. One engineer pointed an agent at a Swagger file and had it build a working MCP server for VenturEd’s XporterAPI, so anyone could ask plain-English questions like how many students are in year seven at a given school. Another built context-aware setup skills for the Groupcall product that spin up the right dependencies on demand, and a code-vetting flow for the WisePay payments codebase. Across the teams, shared skills libraries, planning workflows, and custom MCP servers became part of how people worked.
"I've just been surprised at how much I've been able to do. I'm having it review code, pull backlog items out of DevOps, summarise my day. It's incredible."
Monthly follow-up coaching sessions kept the momentum going across both cohorts, working through real production problems, token efficiency, and the habits that make AI compound rather than plateau. Lunch-and-learn sessions in April brought the wider organisation into the conversation, and the engagement extended to a leadership session in Leicester with the CEO and senior leadership team, getting hands-on with the latest tools themselves.
The work went beyond delivering training. Alongside the sessions, the engagement has included advisory support: reviewing AI adoption strategy, helping map the constraints across departments, supporting the onboarding of a Principal AI Engineer, and helping the product and engineering teams think about how they work, not just what tools they use.
Want to build a team that improves itself? Chris works with organisations to turn AI tool investment into productivity gains, from first training sessions through to ongoing advisory. All training is customised around your team’s real work and where people are right now, whether that is ten people in one office or three hundred across multiple time zones.
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