AI training
Training people actually use
Most AI training explains the technology and stops there. Ours is built around your team's real work, so the skills survive past the workshop and show up in how people do their jobs.
Almost everyone has access to AI tools now. What separates the teams that benefit from them is judgment: knowing what to hand over to a model, how to check its work, and when to stop trusting it. That judgment is learnable. It just doesn't come from slide decks.
Every program we run is hands-on and uses your context. Participants work on their own tasks during the session and leave with working prompts, workflows, and habits. They also leave with a clear sense of where these tools fail, which matters just as much.
Programs
Built for the people in the room
Formats and depth adapt to the audience. These are the four shapes training usually takes.
A working understanding of what AI can and cannot do for your organization. Enough to set direction, evaluate proposals, and ask vendors the uncomfortable questions. No code required.
Writing, analysis, research, reporting. Practical sessions on using AI well in everyday knowledge work, including how to verify outputs before they carry your name.
Working with models in earnest: prompt design, retrieval-augmented generation, evaluation, and AI-assisted development. Built around your stack and real codebase where possible.
A bespoke program for a department or a whole company. Sequenced sessions, shared standards for AI use, and follow-up so the practice sticks after we leave.
Tell us who's in the room
Describe your team and what they work on, and we'll suggest a format that fits. If training isn't the right starting point for you, we'll say that too.