Sovereign AI with control over Data, models, and operations.
We develop sovereign AI platforms for medium-sized businesses – with controlled data flows, suitable operating models, and clear governance. This allows you to use AI productively without relinquishing control over data protection, operations, and platform dependencies.
Why Sovereign AI
AI governance starts with controlled data flows
For many organizations, the key question is not which model is theoretically available, but how to ensure that no sensitive data leaves the organization uncontrolled. This is exactly where Sovereign AI begins for us: with clear guardrails for data access, roles, routing, approvals, and operational responsibility.
To achieve this, we design architectures in which user interfaces, the model layer, orchestration, and data sources work together in a way that ensures data protection, traceability, and practical usability at the same time—especially when AI is to be used productively in the mid-market.
Architecture layers
- Application layer — user interfaces, APIs, business units
- Governance & access control (RBAC)
- Model layer — LLMs, fine-tuning, retrieval augmentation (RAG)
- Data pipeline & filtering
- Data layer — internal knowledge bases, structured sources
- Hosting perimeter
- Operations layer — on-premise, private cloud, EU region
New service module
Sovereign target architecture for AI, cloud, and workplace
For many medium-sized companies, sovereign AI does not start with model selection, but with a realistic target state for platform, data, collaboration, migration, and operating model.
Step 1
Concept paper for a sovereign IT and AI target architecture
A stable, cost-effective, and secure entry point for the mid-market: technology options, migration, change, governance, and target state consolidated into a robust basis for decision-making.
- Assessment of current dependencies
- Evaluation of GDPR and operational requirements
- Target state for platform, collaboration, and data hosting
- Pragmatic prioritization of next steps
Step 2
Technical implementation concept
Roadmap for reducing critical dependencies and selecting viable platform components – from European cloud to Nextcloud, Office alternatives, and operating models.
- Architecture for Nebius, IONOS, STACKIT, or hybrid models
- Classification of Nextcloud, collaboration, and Office alternatives
- Migration logic, technical dependencies, and sequence
- Implementation framework for platform, identities, and AI operations
Example building blocks
Possible tool and architecture building blocks
Where it makes sense from a functional perspective, we think through the target architecture using concrete building blocks—for example, OpenWebUI as the user interface, vLLM for controlled model provisioning, and n8n or Dify for orchestration and workflow logic. The decisive factor is never the tool alone, but how data flows, roles, approvals, and hosting are cleanly governed.
Our Services
What we develop for you
From target-state development to ongoing operations—including sovereign platform and infrastructure decisions.
Target state & platform architecture
Joint development of the target architecture: which models, which data, which interfaces, and which platform components—aligned with your IT strategy and compliance requirements.
Hosting & operating models
Selection and implementation of the right operating model: on-premise, private cloud, European cloud providers, or hybrid architectures.
Identity & access concepts
Integration into existing IAM systems, RBAC models for AI access rights, and auditability of all interactions.
Connecting internal data sources
Structured integration of knowledge bases, document archives, ERP systems, and other internal sources via Retrieval-Augmented Generation (RAG).
Operations, monitoring & continuous improvement
Establishing an AI operating model with monitoring, quality control, update processes, and continuous improvement of platform performance.
GDPR compliance & AI governance
Technical and organizational measures that ensure data protection compliance, enable controlled data flows, and address requirements for approvals, roles, and auditability.
Operating models compared
The right model for your requirements
We help you identify the right operating model—depending on data protection requirements, IT infrastructure, sovereignty objectives, and scaling needs.
On-premise
Hosted entirely in-house
All components run in your own infrastructure. Maximum control, no external dependencies—for regulated industries and particularly sensitive data.
- Full data sovereignty
- No data leakage to the outside
- Integration into existing IT
- Higher operational requirements
Recommended
Private cloud (EU region)
Managed service in a European, GDPR-compliant cloud environment. A balance of control, scalability, and operational effort.
- GDPR-compliant EU data hosting
- Scalable as needed
- Reduced operational effort
- Clear service-level agreements
Hybrid
Hybrid Architecture
Distribution by sensitivity: critical data in-house, less sensitive workloads in the cloud. Flexible and cost-optimized.
- Granular data segmentation
- Cost optimization possible
- Higher architectural effort
- Existing infrastructure can be leveraged
Our approach
From initial analysis to a production-ready platform
Requirements analysis
Systematically capture the data protection profile, IT landscape, use cases, and compliance requirements.
Architecture design
Develop the operating model, model selection, data flow architecture, and security concept.
Implementation & integration
Platform setup, IAM integration, data connectivity, and onboarding of the first user groups.
Operations & scaling
Ongoing monitoring, quality assurance, and step-by-step expansion to additional business units.
Ready for a sovereign AI architecture?
From target-state development to ongoing operations—including sovereign platform and infrastructure decisions.
Further AI building blocks
Sovereign AI in combination
Validation & prioritization
AI Transformation Lab
Before a platform is built, validated use cases are needed. The lab creates the foundation for Sovereign AI.
Steering & compliance
AI governance
The Sovereign AI platform requires suitable governance structures for approvals, roles, and quality control.
Competent Advice at Your Side
Our Expert for Your Concerns
Thomas Pietrzykowski supports organizations in not only positioning AI strategically, but also making it productively usable. His focus is on developing pragmatic AI architectures, evaluating relevant use cases, and implementing secure, scalable solutions across existing business processes.
With 25 years of experience in software engineering, enterprise architecture, cloud, DevOps, and digital transformation, he combines technological depth with operational implementation experience. He is familiar with modern AI platforms, automation tools, and integration approaches not just from consulting, but from direct practical application—from prototyping and system integration to governance, operations, and scaling.
His strength lies in translating business requirements into actionable technical solutions. In doing so, he brings international leadership experience, experience in regulated environments, and a deep understanding of data, interfaces, security, and operating models.
