We teach AI and we build it.
Six AI projects in production, designed and built by the IAescola team. Pick one and see how it works inside.
The workbench
Where the data comes from, what the AI does, and where a person decides.
Illustrative example: sample data, no real client.
Our team of AI agents
Eight autonomous agents run the academy's operations: leads, email, content and reporting.
Claude API · agent orchestration
- 01A message arrives
Through the form, email or WhatsApp.
- 02The intake agent reads it
It spots the topic, the urgency and the language.
- 03Another agent drafts the reply
From the real catalogue and calendar, not from memory.
- 04A person reviews ithuman review
Nothing leaves the academy without a human look.
- 05Reply sent, follow-up logged
The contact is recorded with its next step.
From the project to the classroom
What we build is the raw material of our training.
We teach what we had to solve
The exercises come from real problems in projects like these: where AI gets it wrong, where a person is needed and what isn't worth automating.
A person makes the final call
When a result reaches a customer or the accounts, someone reviews it. That's how we design our projects, and that's what we teach.
Your task could be the next project
If a weekly, repetitive job comes up in a session, we can prototype it with your team and test it on your own data.
Is there a job you repeat every week?
Tell us about it. In the first conversation we look at whether it makes sense to automate, how to do it, and what your team should learn.
Some projects are our own products or built for third parties, shown anonymised. Individual, non-guaranteed results.