Business challenge
IT & Data Sovereignity
This topic spent years on the IT department's agenda. That's over. New legislation, geopolitical pressure, and sky-high subscription bills have elevated it to board level. The question is no longer whether you need to address this, but whether you do it proactively or reactively.
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We understand your challenge
01
You don't know exactly what's under the hood
Which vendors have access to your data? Under which legislation does that storage fall? You've trusted your cloud provider, but trust is not a policy.
02
You pay year after year for software you hold
Subscription costs rise. The functionality never fits exactly. And leaving is more expensive than staying, because your data, your processes, and your knowledge are trapped in someone else's platform.
03
You are stuck with choices you made years ago
Vendor lock-in only feels like a problem when you want to leave. And then you discover how deep the dependency runs: data, licences, architecture, knowledge.
04
New regulations force you to make decisions
AI Act, NIS2, GDPR — the question is no longer whether you need to address this. The question is when, and whether you do it proactively or reactively.
05
You want to use AI on your own data, but you don't dare
Your business data is your most valuable asset. You don't want to just let an American model process it. But what are the alternatives? And what do they cost?
Let's go
IT and data sovereignty is a strategic issue
IT and data sovereignty is not an IT project. It's a strategic question: what do you want to be able to decide yourself about your technology, your data, and your AI?
Complete independence from American tech is not a realistic goal for most organisations. But conscious dependency, knowing what you use, why, and what your exit option is. That is achievable. And increasingly: financially attractive.
Because with AI, the equation has changed. What used to cost years of development, you now build in weeks. Replacing expensive SaaS subscriptions with software you manage yourself is no longer a utopia. It's a real choice with a concrete business case.
We help you make those choices. Which data stays internal? Which AI models do you run locally? Where do you consciously choose a hyperscaler, and where do you prefer to build on your own infrastructure? And when you build: how do you ensure those choices still hold up in two years?
FAQ
FAQs
We would be happy to answer them in advance. If your question is not listed, please contact us.
IT sovereignty refers to an organization's ability to have demonstrable control over its technological choices - which software it uses, where its data is, which suppliers have access, and how much it sets its own roadmap. It includes data as well as the software platforms and infrastructure on which an organization runs.
Data sovereignty refers to an organization's ability to have demonstrable control over who has access to its data, what legal system that data falls under, and what decisions that access is granted.
Data privacy is about the protection of personal data (GDPR). Data sovereignty goes further: it involves strategic control over all company data, including which parties have access and under what conditions. An organization can be GDPR compliant and yet have little sovereignty over its data.
Vendor lock-in occurs when data, applications, and processes are so deeply integrated into a supplier's platform that migration becomes technically complex, costly, or operationally risky: dependence on pricing policies, limited control during geopolitical changes, reduced ability to comply with future regulations.
When subscription costs are structurally high, functionality is not compatible, the supplier determines your roadmap, whether data independence is a strategic requirement. With AI-native methods, custom software can be built faster and more predictably than ever - making the business case for de-SaaS-ing positive in more and more situations.
The EU AI Act sets requirements for the traceability, transparency and responsibility of AI systems. For high-risk processes, the architecture must demonstrate who makes which decisions - direct consequences for model selection, data storage and logging.
When data is legally prohibited from leaving your own infrastructure, when auditability is required, or when external API dependency is an operational risk. Open source models such as Mistral or Llama offer an alternative that can run entirely within its own environment.
Do you want to know where you stand?
In an initial meeting, we will jointly identify where your data is, what dependencies are at risk and what choices you can already make.
