Learning to use AI in real work, not in theory.
Training is useful when a company wants to understand how to use ChatGPT, Gemini, Claude, AI agents, automation and RAG tools in a practical, governable way that fits its work style.
Operational prompts
Reusable requests, instructions, templates and procedures.
AI Agents
From a simple assistant to a controlled agentic workflow.
Documents and RAG
How to make AI work on company sources and content.
Safe use
Privacy, data, limits, human review and control.
The point is not “taking an AI course”, but creating autonomy: understanding which activities can be improved, which tools to use and which rules to adopt so AI does not become confusion.
Many companies use AI; few are truly integrating it into work.
The most common risk is using AI as an occasional text generator, without method, control or continuity. Good training must turn initial interest into operational skills: what to ask, when to trust, how to verify, how to document, how to protect data and how to connect AI to real processes.
From curiosity to daily use
The team learns to recognize where AI is truly useful: analysis, drafts, document control, summaries, customer care, marketing, procedures and decision support.
From prompts to flows
Writing better requests is not enough. It is necessary to understand how to build work sequences, checklists, templates, standardized outputs and verification steps.
From enthusiasm to control
Training clarifies limits, risks, hallucinations, sensitive data, copyright, privacy and responsibility: AI must help, not create chaos.
Different training for different roles.
An entrepreneur, a salesperson, an administrative department and a marketing team do not need the same path. That is why training must be adapted to the role and the real work of the people involved.
Entrepreneurs and managers
To understand where to invest, which processes to improve, which tools to choose and how to estimate costs, risks and benefits.
Marketing and communication
To use AI on editorial plans, competitor analysis, content, campaigns, reports, SEO and digital strategies.
Administration and operations
To read documents, build checklists, summarize procedures, check anomalies and prepare operational summaries.
Professionals and firms
Accountants, consultants, lawyers, technicians and professionals can use AI to organize information, drafts and preliminary analyses.
What you learn in practice.
The path can be introductory, operational or advanced. The structure is defined according to the team's level and the company's objectives.
Practical use of AI models
How to use ChatGPT, Gemini, Claude or other tools for analysis, synthesis, writing, brainstorming, checking and preparing materials.
Reusable prompts and procedures
How to build clear instructions, templates, role prompts, checklists, output rules and repeatable operating sequences.
AI agents and automation
What distinguishes a chatbot from an agent, when automation makes sense, which controls are needed and how to design an agentic flow.
RAG and company documents
How to make manuals, procedures, contracts, knowledge bases and internal content queryable without losing control of sources.
Privacy, security and limits
Sensitive data, file use, internal policies, hallucinations, human review, model limits and output responsibility.
Use cases and operational ROI
How to identify high-impact activities, estimate time saved, quality improved and sustainability of API or local costs.
AI training applied to concrete projects.
Courses are not limited to explaining artificial intelligence in theory. They can be built around very practical objectives already addressed in previous paths: creating a website, designing an AI agent, developing business software, generating images and videos, conducting market research, preparing presentations, qualified reports or marketing plans.
Each path can be adapted to the team's level, sector, tools already used and the result the company wants to achieve.
Create websites
Use of AI for structure, content, SEO, wireframes, pages, HTML/CSS code and website review.
Create AI agents
From use-case definition to PRD, through agent logic, tools, memory, RAG and human control.
Develop business software
AI support to design dashboards, internal applications, automation, Python scripts and operational tools.
Create images and videos
Visual prompts, concepts, storyboards, campaign images, corporate visuals and AI-assisted video content.
Market research
A method to collect data, read competitors, summarize sources, identify trends and turn analysis into decisions.
Presentations and reports
Creation of qualified reports, strategic documents, commercial presentations, reports and materials for clients or management.
Marketing plans and operational strategies
Use of AI for market analysis, personas, positioning, editorial plans, campaigns, SEO, advertising and commercial materials. Other paths can be built on request, based on the company's real needs.
Three formats, depending on the company's maturity level.
Training can be a short orientation session, a practical workshop or a more structured path linked to internal processes.
Introductory session
For entrepreneurs and teams who want to understand opportunities, limits and first use cases without immediately entering technical development.
- current AI scenario
- risks and good practices
- first business applications
Operational workshop
For teams that want to work on real examples: prompts, documents, flows, checklists and tools already used in the company.
- practical exercises
- reusable templates
- use cases by department
Tailored path
For companies that want to build an internal method, train internal contacts and connect training to concrete AI projects.
- initial call and analysis
- training roadmap
- adoption support
Training starts from your processes, not generic slides.
First we understand who must use AI, on which activities and with which constraints. Then we build a practical path, with examples consistent with the way the company already works.
Introductory call
We collect objectives, roles involved, tools used, team level and concrete problems to improve.
Path design
Modules, examples, exercises, tools and level of depth are defined: introductory, operational or advanced.
Practical sessions
We work on realistic cases: email, documents, reports, analysis, procedures, prompts, workflows and operational simulations.
Materials and follow-up
The team receives guidelines, templates or work schemes to continue using AI after training.
The most common questions about AI training.
Do you need to be technical?
No. The path can be designed for non-technical people. The goal is to use AI well at work, not turn everyone into developers.
Do we work on our own examples?
Yes, whenever possible. Training becomes more useful if it uses documents, cases, procedures or scenarios similar to the company's real ones.
Does it also cover AI agents?
Yes. It can include a section on AI agents, automation, RAG, integrations and the difference between assistant, workflow and operational agent.
Is it standard training?
It can be for an initial introduction, but the greatest value comes when the path is adapted to the company's roles, software, processes and objectives.
Do you want to train your team on AI in a practical way?
We start from a call to understand level, departments involved, objectives and tools already used. From there, we build a concrete training path compatible with real work.