Workshops for leadership, team leads and operational teams with examples close to real company work.
AI training for companies.
Applied sessions for leadership and teams on agents, models, RAG, process automation and real use of the main providers.
Agents, prompting, models, providers, RAG, selective automation and responsible usage.
Companies already using AI or about to start, and that need a shared framework before scaling usage.
Four modules to move from curiosity to serious use.
Learning framework
We do not run a catalogue course. We work through a clear sequence: understand the terrain, compare providers, separate agents from automations and land the knowledge in a practical way.
01
Common ground
Understand the current landscape without fear or noise.
What models can actually do today, where they fail and how to read AI in language that is useful for business.
02
Models and providers
Compare ecosystems without getting lost in commercial noise.
OpenAI, Microsoft, Google, Anthropic and other real contexts: when choosing matters and when simplifying is smarter.
03
Agents and automation
Separate chat, copilot, agent and process.
Understand when an agent makes sense, which automations deserve a next phase and where it is better not to force it yet.
04
RAG and knowledge
Connect documents and context without overengineering.
When RAG adds value, how to protect response quality and where unnecessary complexity usually starts appearing.
Concrete technology, explained in business language.
What we land in practice
We do not teach a list of tools. We teach how to read the terrain: which provider is worth evaluating, when an agent makes sense, when RAG is excessive and which automations deserve to move into a next phase.
What we cover when a company wants to go beyond basic prompting.
Agents, assistants and copilots without mixing the concepts.
We clarify the difference between a useful chat, an assistant with instructions, an agent with tools and an automation connected to a process.
What it lands in practice
When a company only needs better everyday usage and when it already makes sense to design agents with steps, validations and context.
Models and providers explained with practical judgement.
We review what really changes between providers such as OpenAI, Microsoft, Google or Anthropic, and what it actually means to talk about multimodal models, context, security or cost.
Where it adds value
It avoids shallow conversations like “which AI should we use?” and helps teams read the real implications behind each path.
RAG, documents and internal knowledge without selling magic.
We explain when RAG makes sense, what it actually does, how much it depends on document quality and why the problem is often not technical but informational.
What it avoids
It avoids building complex solutions when there is still no solid documentation base or when good search would already solve most of the problem.
How to spot automations that deserve a second phase.
We work through simple but serious criteria to separate tasks that only need better AI usage from processes where there is already enough substance to design a selective automation.
Next step
The training leaves leadership and teams better prepared to decide whether to stay in adoption or move into diagnostic and case design.
What it leaves inside the company, and what we are not selling.
What it leaves
A shared framework and a better-focused agenda.
What it is not