We train people, teams, and models.
From a single developer to a whole IT department, and from a prompt to a fine-tuned model. Easy doesn't mean slack.
Why training
Most companies bought AI licences and got a few people who use them well and many who do not. The gap is not talent. It is method. We train entire IT departments on the easy but rigorous path to AI results, so the whole team moves, not just the early adopters. And when the bottleneck is the model rather than the people, we train that too.
We are not trainers who read about it. We ship with these tools every day, we built our own methodology around them, and we run AI agents in production. What we teach is what we do.
Four formats
IT department onboarding
A programme for the whole department, from helpdesk to architects. Everyone learns the same vocabulary, the same guardrails and the same way of judging when AI is the right tool. Delivered in cohorts, in your context, with your systems as the examples.
Developer mentoring
We mentor young developers onto the AI pathway: how to work with agents and coding assistants without losing the craft, how to review generated code, how to build the small tools that make a team faster. Regular sessions, real tickets, honest feedback.
Models, not only people
Sometimes the thing that needs training is the model. We fine-tune and tune models on your data and your domain, build the evaluation sets that tell you whether it got better, and shape agents until they behave the way your operators would. Your data stays yours, and so does the resulting model.
Senior teams and leadership
For the people who decide. Perception shift, a hype filter, and a roadmap: what actually matters for your business, what is noise, and what to do in the next quarter. Short, direct, and free of buzzwords.
What we cover
- Prompting that holds up: structure, context, evaluation, and why most prompts fail quietly.
- Agents, tools and automations: when to build one, how to keep it under control, how to test it.
- AI-Driven Development in your repositories: patterns, review gates, and the conventions that make it stick.
- Security and guardrails: data boundaries, secrets, prompt injection, and what agents must never be allowed to do.
- Picking models and providers: cost, quality, privacy, and how to avoid being locked in.
- Measuring results: what to track so you know it is working, and not just feeling faster.
- Training the model itself: when fine-tuning beats prompting, how to build evaluation sets, and how to keep a tuned model honest.
How it runs
- On site or remote. We lead distributed teams for a living, so remote is not a compromise.
- In your codebase and on your systems, not on toy examples.
- Small cohorts, hands on, with homework that is real work.
- Follow-up sessions after the programme, because the questions get better once people start using it.
- In English, Portuguese or German, as your team prefers.
What changes afterwards
- A department that uses AI consistently, safely, and with a shared standard.
- Developers who ship faster and review better, and know the difference.
- Guardrails your security team signed off on, applied in daily work.
- Senior people who can tell a real opportunity from a demo.
- A pathway for the next generation, so the capability keeps growing without us.
Train your team with people who ship
Tell us about your department: size, stack, and where you are with AI today. We will propose a programme that fits.