Industries where we are at home
Each industry has its own boundary conditions – legal, technical and cultural. Here it says what is technically concerned and what projects usually depend on.
Focus
Automotive
This is about two very different worlds that belong together: the software in the vehicle and the data that comes from it. On the one hand hardware-related development for control units and their communication via CAN and Automotive Ethernet, on the other hand the application of calibration data with their A2L, HEX and DCM stands. In addition, everything that happens afterwards: evaluate production data, predict failures, establish transparency over supplier chains. It is the industry in which we are deepest – so deep that it has created its own product. More about Automotive ›
Machinery and plant engineering
Here our interlocutors sit on two sides of the same plant: companies that build machines and companies that operate them. For manufacturers, it is about machine data from the field, service portals and spare parts catalogues that fit the specific serial number. For operators around maintenance that plans instead of responding, and around technical knowledge that can be found in seconds instead of flipping after half an hour. The connecting theme is almost always the same: the data is available, but no one sees it at the right moment. More about mechanical engineering ›
Construction industry
Construction projects rarely fail due to technology and often due to coordination. Information is distributed among construction managers, trades, planners and builders, everyone keeps their own list, and the current status is the one that someone has recently sent by e-mail. What arises here are specialist applications that perform exactly this comparison: processes that are documented in a comprehensible manner, data that is recorded only once, and evaluations that show where a project really stands – not where it should stand according to the plan. More about the construction industry ›
Medical sector
Hardly any environment places higher demands on the handling of data. Health data belongs to the special categories of personal data, the framework is correspondingly narrow. This shapes every architectural decision: processing in-house instead of in external data centers, traceable accesses, clear deletion concepts. At the same time, it is a field in which AI can contribute much – in the evaluation of large amounts of data and in finding knowledge that is contained in findings, guidelines and documentation. Both are only possible if the models run locally. More about Health Care ›
Public administration
Administrations digitize under conditions that no company knows: procurement law, file regularity, accessibility, plus a specialist process landscape that has grown over decades. What counts here is not the fastest solution, but the proven one. Our orientation fits well: we are not bound to any manufacturer, and our solutions run completely on-premise when data sovereignty demands it. No vendor lock-in is not a sales argument here, but a requirement. More about public administration ›
Largest share
Medium-sized enterprises
Most of our work. Typical is a starting point in which the standard software almost fits – and the remaining gap has been filled with Excel, call and manual work for years. Or an old system that runs, but no one dares to change anything anymore. What emerges here is rarely spectacular and almost always effective: an application that fully maps a process, a data link that resolves conflicting numbers, or a first AI use case that pays off. New addition is a topic that affects many of these companies for the first time: the security obligations from NIS2 – more about this NIS2 advisory services.
Industry independent consultancy services
Whether an established industry or a new field: Our strength is individual, cross-industry consulting. Talk to us – we will find the right solution together.
Why there are no customer names here
Many service provider websites have a wall of customer logos. Not with us, and there is a reason: in the areas where we mainly work, confidentiality is the rule. Anyone who works on control units, health data or administrative procedures usually signs a confidentiality agreement – and that also applies to our marketing.
We therefore do not publish names, logos or project details without express written approval. That costs us a few arguments on this page. But it is the same promise you get from us when you become a customer.
What we can offer instead:
- A conversation with a reference customer – Upon request, we establish contact if the customer agrees. That says more than any logo on a website.
- An anonymised project description – Starting position, approach and result without name – in conversation, not on a public side.
- Insight into your own product – At VersaCal, we can show how we work without touching someone’s confidentiality.
Frequent questions about references
Because we are not allowed to show them without written approval – and in our main industries, secrecy is the norm. Automotive development, health data and administrative procedures are areas where non-disclosure agreements are standard. We stick to it, even if a logo wall on this page would look more convincing.
In many cases, yes. We ask suitable customers if they are ready for a short conversation. This takes a few days, but it's the most honest proof we can provide. You also hear what was not going on.
Yes, and this is more often the case than this site suggests. The processes behind software, data and AI are the same across the industry. What differs is the technicality – and you bring that with you. Our task is to ask the right questions and understand quickly enough what matters.
Experience shows that one or two workshops are sufficient for a viable common understanding, in which we go through the process completely once – preferably with the people who carry it out on a daily basis, not only with the management level. Misunderstandings stand out where someone says: We do not do this in practice.
Yes. If it is foreseeable that a standard solution solves the problem better than an in-house development, we say so – even if we lose an order. Likewise, if a schedule is technically unsustainable or if an AI project in the intended form would not be legally clean.
Usually yes. Many good projects start with a narrowly defined step: an evaluation, a prototype, a delineated work package. This is the least risky way for both sides to find out whether cooperation is beneficial.