Service
Your team learns by solving real work.
Small-group sessions on the tasks your team needs to solve that same week, with the tools it already works with.
Not a course. Each person finishes with something of their own working and knowing how to repeat it.
Fifteen years building with data. More than two hundred projects delivered for companies where failing is not an option.
AWS technology partner
The two tracks
Two ways to learn. The same result: real work solved.
Both tracks start from your team’s own tasks and both end with something working. What changes is the language, the tools and the criteria each profile needs to master.
For sales, marketing, operations and admin teams
Business and operations
What gets worked on
- Giving AI the context it needs to answer with your criteria and not with generic answers.
- Solving one real task of each participant, end to end.
- Turning the way the company works into reusable instructions, while working and not afterwards.
- Reviewing the results: what is accepted, what is corrected and what is never delegated.
By the end, your team knows how to
- Solve with AI the tasks that eat up hours of work today.
- Document the company’s criteria and apply them the same way every time.
- Tell what can be delegated from what a person reviews.
For development, data and systems teams
Technical teams
What gets worked on
- Working with AI inside the repository, on a real change of the team’s.
- Defining the context and the conventions that must be applied on every change.
- Reviewing the work with your own criteria, not the model’s.
- Identifying where it is not worth using and how that decision is taken.
By the end, your team knows how to
- Work with AI inside the repository itself.
- Prepare the context so every change stays consistent.
- Decide where it adds value to use it and where it does not.
How we work
Working sessions, not lectures.
- Learning by doing Nobody sits through a presentation: from the first minute your team works on real tasks, in hands-on sessions where everyone leaves with something done.
- Everyone brings a real task Not an exercise. Without real tasks, the session does not take place.
- With your tools and your access Not with a demo account and data that is not yours.
- Every session ends with something working And with what was decided in writing, at the time and not afterwards.
Before starting, someone on your team chooses the cases: it is the only preparation we ask for and the one that decides whether the training is useful. Afterwards we review what is still in use.
Who has already done it
In the words of the team that received it.
We wanted to thank Pleasepoint enormously for the agentic AI training they gave our team. It has been a genuine turning point in how we understand and apply agentic AI day to day. Thanks to their vision and their method, we have not only streamlined processes, we are getting far more out of tools we were never using to their full potential. A ten out of ten for how practical and how inspiring it was.
What stays
Criteria stop depending on one person.
When one or two people master AI and the rest carry on as before, the knowledge leaves with them. Our job is to write those criteria down and put them where they are used.
The company’s criteria, in writing
How it arrivesA living document written during the sessions, not afterwards.
Who uses itWhoever joins tomorrow and was not in any session.
Loaded into the tools you already use
How it arrivesWhere the team works, so it is applied without having to remember it.
Who uses itAnyone who asks, whether they attended or not.
The cases from the sessions, in use
How it arrivesWhat was built in the sessions stays running.
Who uses itThe same team, the next day.
Shall we set it up with your team?
Tell us which teams they are and what they have pending. We will tell you what could be worked on in the sessions and what would be written down by the end.
Frequently asked questions
What people ask before setting it up.
Do people need to know how to code?
On the business track, no: they work with the tools they already use. On the technical track we assume the team codes.
What if the team goes back to working as before?
It happens when what was learned is not used in real work. That is why we start from tasks that are actually pending and why there is a review afterwards.
Can you train only part of the team?
Yes, and it is usually the best way to start. When a few people work differently and write it down, the rest come on board with less effort.
Does this have anything to do with the obligation to train in AI?
The European AI Regulation asks that whoever uses or develops AI systems in a company knows how. These sessions are practical training on your own tasks, and what gets worked on is written down and in use.
Does it work if each team uses different tools?
Yes. What gets learned is not a tool: it is how to give it context, how to review what it returns and what not to delegate. That carries over.
Does training replace implementation?
No. Training teaches the team to work this way on their own tasks; implementation leaves a case running inside your systems. They can be bought separately and they work well together. Read what an implementation agency does