Approach
Clarity for data, automation and AI.
We work with data, automation and applied AI under one set of criteria: proportionality, control and clarity. No hype, no alarmism, and no turning every project into red tape.
Three principles
There is no method without criteria.
Before we talk about tools, we agree on how to decide. These are the three principles we apply on every project.
Automation
Automating is not delegating blindly.
A good automated process is an understood process, not a hidden one. So before automating we define the input, the output, the owners, the limits, what counts as an error and how it stops. Then we measure: what it saves, what fails and when it stops being useful.
Before automating
- Input and output
- Owners
- Limits
- What counts as an error
- How it stops
Useful automation is the kind the team controls, not the kind that replaces the team and nobody reviews.
How we apply it
Concrete practices, not slogans.
What these principles mean day to day on a project.
Responsible AI for SMEs.
We design AI use cases around the real size of the problem: which decision it supports, with what data, what happens when it gets it wrong and who reviews it. No abstract frameworks — concrete criteria, written into the project itself.
Data governance without bureaucracy.
We agree the bare minimum: who owns each dataset, what quality is expected, how it is updated and when it is deleted. We are not after a data committee; we want every dataset to have a clear owner and a clear shelf life.
Traceability and minimum documentation.
Every automated flow records what came in, what went out and what it decided. We document just enough for the team to maintain, audit and change it without us. If it cannot be understood without us, it is not finished.
Human oversight.
On each flow we define where a human reviews: before deciding, before communicating, before acting. Review lives in the design, not as an add-on once something breaks.
Clear limits on generative AI.
We decide which tasks use generative AI, what data it may work with and which outputs require human validation. We also decide what is not done with it. These limits are written down and shared with the team.
Tools and vendors chosen with criteria.
We choose tools for how well they fit the case, not for hype. We weigh where data is processed, what dependencies we create and what happens if a vendor changes terms. Portability matters.
European framework
The AI Act: when it comes into play.
The AI Act is the European Union's regulation on artificial intelligence. It classifies AI systems by level of risk and sets different obligations depending on that level. Most SME projects fall into lighter categories — productivity, internal assistants, analysis — and others, depending on the use case, may fall into more demanding ones.
On each project we identify which category a use case might belong to and which good practices make sense to apply from the design stage: transparency, oversight, documentation, risk management. We do it as an engineering and management criterion, not as a legal opinion.
The EU AI Act is already in force, and its obligations arrive in phases. The hard part usually isn't legal — it's not knowing what applies to you, or where to start.
What worries you
What we do
You don't know whether your AI systems fall under the regulation, or which risk category they're in.
We map which AI systems you use and which ones actually apply to you — most SMEs fall into the lighter categories.
You're asked for an inventory, documentation and traceability — and you don't know what's enough.
We turn each requirement into something concrete: inventory, controls, traceability, oversight, evidence.
You fear a huge, expensive legal project disconnected from how you actually operate.
We treat it as what it is — a data, process and systems matter — not a legal opinion.
We don't issue legal opinions or certify compliance. We turn AI Act requirements into systems, controls and evidence that work — and where legal interpretation is needed, you or your advisors validate it. The assessment already looks at this; the projects already build it.
Let's talk
If this approach fits, let's talk.
We start by understanding where the friction is and what is worth prioritising.
A first 30-minute call with direct senior input, no commitment and no sales pitch.