The team delivers more, and not only maintenance.
New features and improvements ship sooner because gathering context, writing tests and documenting stop consuming the day.
If you lead IT
More technical capacity without growing the team.
AI goes into the repository, the review cycle and the systems you already have. Not into a separate tool.
Fifteen years building with data and more than two hundred projects delivered, for companies that cannot afford for it to fail.
AWS technology partner
The starting point
What repeats in a technical team.
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Scattered adoption
Everyone uses AI their own way and none of it reaches the repository.
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Stalled pilots
They work in the demo and stop at the first permission.
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Context outside
Prompts and guides live in a supplier account, not in the repo.
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Interruptions
The business asks for a figure and someone drops what they were doing.
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The queue grows
And the headcount does not. Delivering more no longer depends on the model.
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No governance
Nobody has decided yet which data may leave and which may not.
None of this calls for more people. It calls for AI to come inside the workflow and under your rules.
What changes
Four changes in the team.
The code stays yours and holds up over time.
Conventions, architecture and review criteria live in the repository and are applied on every change. AI works inside your standard, not alongside it.
Your systems connect over MCP, with permissions.
An MCP server in your environment and one connector per system: the agent queries with the permissions of whoever is asking, and the business stops asking you for every figure.
Secure by design, not by trust.
Credentials, database and documents never leave your environment; the agent only sees what MCP exposes to it, and every access is logged. That is why you can audit it.
Architecture and governance
Where each piece lives and who operates what.
The MCP server runs in your environment, with one connector per system and your guidelines alongside it. Credentials never leave it.
Integrations
API first. SQL only where the API does not reach. Always on what you already have.
Credentials
They live in your environment. Never in our repository.
Traceability
There is a record of what was queried, with which model and with what result.
When we finish
Code, rules and context are yours and remain versioned.
A working day
Real tasks from an ordinary day.
See all the cases we implementThese are hours from our own projects, not an average across clients.
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.
How we work with you
The method is the same for any case.
A path in three phases. Each one closes with something reviewable: a written decision, a case in production, an autonomous team. Until that is there, the next phase does not start. It is what keeps a pilot from staying a pilot.
The services
- AI implementation We look at how you work, put the first case into production and stay until the team sustains it without us.
- Training Your teams learn to work with AI on their own tasks, not on textbook examples.
- Custom development We build the product or the software when what you need does not exist, and the code stays in your repository.
A technical team also starts at the beginning: we look at your repository and your workflow, and choose the first case for what it delivers, not for how much it covers.
Shall we look at it with your team?
Tell us how your technical team works today. We will tell you where AI would fit and where it is not worth it, and if it fits we carry on with a session on your repository and your working cycle.
No commitment
You talk to the person who will write the code
If it does not fit, we tell you
Frequently asked questions
What IT leaders ask us.
Who owns the code, the prompts and the data?
You do, from the first commit. Code is written in your repository and rules are versioned with it. Data does not change hands. There is no licence of ours to renew.
What happens to our current stack?
It stays. We work on your repository, your ticketing system and your CI, with their permissions, whatever the language or the age of the system. If something is worth changing, we propose it with its reason and its cost, never as a precondition.
How do you deliver something new?
Most valuable first. The version that is already useful goes to production and into use; what comes next is decided with it in front of you, not over a document.
Who maintains this a year from now?
Your team. That is why the implementation leaves written rules, explicit dependencies, tests and operating documentation. If you would rather we carried on, that is the ongoing support phase and it is decided one stage at a time.
What do you need from my team?
An environment to work in and some hours from whoever knows the domain, mostly at the start. Nobody has to leave their job. If more is needed, we say so beforehand.
How will we know it is improving anything?
What gets measured is agreed before starting: real usage, cycle time, rework after review. If the number does not move, we say so and change the case.