This page answers a practical question: where we operate and what we take responsibility for. Five levels, from the hardware in the datacentre to the line of code that was missing. Between us and the client, there is no one else.
Most industrial projects do not fail on technology: they fail at the handover points between different suppliers. When the entire chain is in the same hands, those points do not exist.
The physical place where data lives. Ours, in Switzerland.
Making machines of any age talk, without touching them.
Correlating what today lives in separate systems.
Anticipating rather than reacting, on data that actually exists.
Bending what exists to fit the process. Building only the rest.
The data entrusted to us remains the client's: it is not monetised, not resold, not delegated. Held in Switzerland, in a datacentre we own, because this responsibility cannot be transferred to anyone.
This is not a marketing argument: it is an architectural consequence. Owning and managing the infrastructure directly allows us to answer precisely where the data is located, who accesses it and under which jurisdiction it falls.
For a manufacturing company this matters more than it might appear: production data reveals how much is being produced, at what margins, for which clients. This is information that one rarely wants to entrust to infrastructure one does not control.
The data entrusted to us remains the client's. Held in Switzerland, because this responsibility cannot be transferred to anyone.
About half the energy in a traditional datacentre does not serve computing: it serves cooling. Acting there is the choice that truly makes a difference — more than any downstream offset.
Cooling makes use of deep lake water, naturally cold throughout the year. The energy required for climate control drops significantly compared to a traditional compression system — and the heat is not dispersed into the city air.
All power comes from renewable sources, with no residual share from fossil fuels. This is a contractual condition of the infrastructure, not an annual average compensated after the fact.
What remains after reducing consumption is offset through reforestation projects in South America, with verifiable certification. First reduce, then offset — never the other way around.
Not for environmental positioning: because it becomes your data. Manufacturing companies face growing reporting obligations throughout the supply chain, and the footprint of the digital services they use falls within that scope. On request, we provide consumption data and certifications for your reporting.
The main technical challenge in integrating a real factory is the variety of protocols: OPC-UA, Modbus, Ethernet/IP, Profibus, MQTT, proprietary protocols from individual manufacturers, legacy SQL databases, CSV files generated by software from the 2000s.
No machine is left out. Most platforms require plants already set up for Industry 4.0. We have no such prerequisite: whether the facility is new or the result of decades of layered investment, every machine can become a data source.
Collecting data serves no purpose if it remains in different formats that nobody puts in relation. The real work is building the context: making comparable quantities that originate from systems that do not communicate.
The PLC knows the machine has stopped. The ERP knows the order is behind schedule. The purchasing department knows the materials arrived late. Rarely does a system know that those three things are connected, that they happened within two hours of each other and that together they generated a three-day delay in delivery.
The result is not another report. It is the ability to answer, in real time, questions that previously required hours of manual work or simply had no answer.
The platform with which we implement this model is called arteMES. It is not an off-the-shelf product: it is the software infrastructure that allows the method to work, configured on the real process of each company.
Artificial intelligence is not a module added at the end of a project. It is what becomes possible when the data model exists — and remains impossible until it does, however sophisticated the language model placed on top.
The question we ask before any discussion about AI is always the same: on which data? An AI trained on fragmented information produces fluent but wrong answers — which is the worst way to be wrong, because nobody notices.
This is the rule we apply in every system we build, and it is what separates a tool on which decisions can be made from a demo that impresses in a meeting room.
Where it runs. Processing involving the client's operational data takes place on our infrastructure in Switzerland. When a project requires external models, we say so explicitly and define together which information may leave and which may not. The client's data remains the client's — even when talking about AI.
Building from scratch what already exists is the most expensive way to arrive late. When there is a product on the market that solves the problem, you install it and shape it to the company's real process — rather than shaping the process to the product.
This is the most uncomfortable position for a supplier to hold, and the most useful for the client. An integrator that lives on licences has an interest in getting you to buy their product. A pure developer has an interest in building everything from scratch, because that is what they do. We earn nothing from either choice: we earn from the fact that the system works.
For SME management systems we appreciate Dolibarr — it is modular, open source, and adapts well to the scale at which we work. But it is not a constraint: if the right product for you is something else, or if the right one is what you already have, that is the answer and it changes nothing for us.
Between us and the client there is no one else. We do not resell anything for margin, we do not accept agreements that steer our recommendations. The only interest we defend is the client's.
Why this truly matters: when nobody earns from the answer, the technical recommendation becomes a technical recommendation again.
The order is not negotiable. You move to the next level only when the previous one has been honestly ruled out.
In industrial digitalisation projects, a significant portion of the overall effort is absorbed by systems integration — not by their function. This cost is almost always invisible in initial budgets and is rarely attributed correctly. That is precisely the work we take on.
From infrastructure to decision. And if the missing piece does not exist, we build it.