We turn European AI capabilities into products that deliver measurable value — combining product strategy, model intelligence, economics and scalable delivery.
Models like Kolibri from Aleph Alpha and Mistral Large 4 open new options for AI products that allow more control over models, data and operation. The real value only comes from the right application, though: which use cases create real benefit, which requirements apply — and how can the product be run economically for years?
Aleph Alpha · released 3 October 2026
A German open-weight model for professional use in controlled environments.
Interesting for document-heavy, regulated processes with high demands on language, traceability and own operation.
Mistral AI · public preview since 6 October 2026
A multimodal model for complex use cases and agentic workflows.
Interesting for demanding products where capability and technological freedom of action have to come together.
Our stance: we do not choose models by origin or hype. We choose them by what the product has to do, and by its economic and regulatory frame.
As of October 2026. Figures are the vendors' own.
We do not start with a model, but with the business problem. Different AI tasks call for different approaches — from classic retrieval solutions to complex agents. That is how solutions come about that really fit: technically, regulatorily and economically.
Knowledge access, semantic search, RAG solutions
Information extraction, classification, structuring
Analysis, inference, complex decision logic
Multi-step tasks, tool use, autonomous processes
Text, code, documents, data transformation
Scenario analysis, recommendations, optimisation
Our Decision Lab helps find the right option for your use case. We compare frontier APIs, European managed solutions and a fully sovereign AI pipeline — on capability, control, latency and cost.
e.g. GPT, Claude, Gemini
e.g. Aleph Alpha, Mistral
On-prem / private cloud
Independent benchmarks
Model capability from standardised tests
Product economics
Realistic cost for your use case
Customer validation
Experience from real projects
In regulated, sensitive or operationally critical environments, an AI product is more than a question of model performance. Traceable results, reliable processes and control over the technology decide whether AI can be used productively at all.
Find expert knowledge faster, prepare cases better, and support staff working through large documents and rule sets.
Product goalLess research effort, higher quality, traceable case handling.
Make knowledge available where reliability counts, from technical manuals to maintenance and operating documentation.
Product goalFaster access to reliable information, better support for operational decisions.
Make technical knowledge usable: open up documentation, specifications and experience to teams systematically.
Product goalLess searching and coordination, faster technical workflows.
Speed up complex document and review processes while keeping traceability and control.
Product goalShorter lead times, less manual routine work, documented quality.
Not every process needs the same model, and not every application needs the same sovereignty architecture.
We help find the use cases where value, technical feasibility and economics actually come together.
We combine product strategy, AI technology and economics to turn a concrete use case into a secure, economical and scalable AI product.
A sound basis for the decision, in a few weeks.
A capable model can deliver impressive results and still be the wrong choice for a given use case. AI products do not only cost money while they are built. They cost with every call, every integration, every quality check and every additional user. Fixed infrastructure cost, low utilisation or heavy maintenance can turn a supposedly cheap model into an expensive one. So we build the business case in from the start.
Our goal is not the cheapest model. It is the best economic decision for the whole product.
Platforms like Microsoft Foundry (formerly Azure AI Foundry) let you build demanding AI applications in European regions, with EU data boundaries and regional deployment options. Its catalogue is not limited to American models either.
Which sovereignty requirements that meets depends on the model, the configuration, the vendors involved, and the legal and operational control you keep. An American model in a European data centre is still an American vendor's model. That can be the right solution. It is not automatically the same as digital sovereignty:
It works the other way round too: a European open-weight model is not sovereign by origin alone if the application calls external services unchecked, passes on sensitive data, or depends on a single operator.
Demanding tasks where the sovereignty risk is manageable.
Trade-offQuality and speed versus dependence on the vendor.
Sensitive internal processes, expert knowledge, specialised applications. European, American or Chinese weights — each with its own checks on origin, training data and alignment.
Trade-offControl versus your own operating effort.
Particularly high requirements for sovereignty and resilience.
Trade-offMore independence versus effort and possible functional limits.
Product and architecture profiles, not legally certified sovereignty classes.
Not every AI product has to be built as sovereign as possible. It needs the right degree of sovereignty for its use case, and the remaining risks made visible. Our guiding question is not
“Is this AI sovereign?”
“Which control does this product need, and how do we put it in place effectively and economically?”
You have a concrete use case, you are evaluating European AI models, or you want to bring AI into existing products and platforms? We help you assess the opportunity, set the right degree of sovereignty, and turn it into a product strategy with a clear business case and a roadmap you can execute.
