Sovereign AI for Enterprises.
We help mid-sized enterprises and PE portfolios make AI secure, GDPR-compliant, and economically viable—from initial use cases to a sovereign platform architecture.
The reality in enterprises
A gap emerges between experimentation and production AI
Many enterprises are already testing AI. Between initial successes and productive use, new requirements often arise: clear data flows, aligned tools, defined responsibilities, reliable results, and a target state for sovereign operating models.
Unclear data flows
Sensitive corporate and customer data is processed across various tools without those responsible having transparency and control at all times.
Fragmented tool landscapes
Business units deploy AI tools independently, often still without shared quality, security, and integration standards.
Inconsistent output quality
AI outputs without appropriate quality control make productive use more difficult and reduce trust in new solutions.
Lack of guardrails for scaling
If responsibilities, approvals, and standards are not yet clearly defined, the path from pilot to broad rollout becomes unnecessarily difficult.
From pilot to rollout: not happening
Many organizations gain initial experience but have not yet made the transition to productive, measurable AI use.
Unclear business case
If costs, benefits, and the scaling path have not yet been clearly aligned, the investment decision becomes unnecessarily difficult.
Critical platform dependencies
Many enterprises are looking for a realistic path to GDPR-compliant operating models, European infrastructure, and a gradual reduction of critical US dependencies.
The digatus model
Three pillars for a sovereign and effective AI platform.
Only when infrastructure, governance, and concrete use cases work together does AI become an effective building block for the enterprise—with control over data, platform, and operations.
Infrastructure, data sovereignty & operations
Secure hosting models, European platform options, data sovereignty, and integration into existing IT landscapes. Controlled data flows instead of shadow AI.
Organizational governance
Clear roles, approval processes, and responsibilities. Governance that enables scaling rather than preventing it.
Functional effectiveness
Measurable results in real business processes. From validated use cases to production automation.
New service module
Sovereign target architecture for AI, cloud, and workplace
For many mid-sized enterprises, model selection alone is not enough. Anyone who wants to operate AI in a GDPR-compliant and controllable way over the long term needs a realistic target state for platform, data, collaboration, migration, and operating model.
This is exactly where our new sovereignty offering comes in: from European cloud and Office alternatives to the gradual reduction of critical US dependencies—pragmatic, robust, and tailored to mid-sized enterprises.
Two-step entry
STEP 1
Concept paper
Technology, migration, change, governance, and the target state for sovereign IT and AI operating models.
STEP 2
Technical implementation concept
Roadmap to reduce key dependencies—from European cloud and Nextcloud to Office replacement and platform architecture.
AI maturity
Where does your organization stand today?
The digatus maturity model makes it clear why many enterprises remain at the stage of individual chatbots—and what is needed to build production-ready AI capabilities.
Service Components
Our AI offerings at a glance
Infrastructure & sovereignty
Sovereign AI
Secure, GDPR-compliant AI platforms with data sovereignty, controlled operating models, European target architecture, and deep integration into your IT landscape.
Validation & prioritization
AI Transformation Lab
Structured validation of use cases, prioritization based on value potential, and conversion of relevant ideas into concrete implementation components.
Steering & compliance
AI governance
Responsibilities, approvals, quality standards, and guardrails that make AI reliable, scalable, and auditable within the organization.
Automation
End-to-end process chains with AI agents, orchestration, and internal data integration for measurably more efficient operational workflows.
Why digatus
Strategy, platform, and implementation from a single source
digatus combines strategic perspective with technical depth and operational implementation experience. The result is AI solutions that not only convince in concept but function reliably in day-to-day business operations.
Real business processes
We work with existing IT landscapes, established processes, and real data situations as our starting point.
Governance with balance
We design governance to provide orientation and support scaling—with clear roles and lean processes.
M&A experience incorporated
Transformation and integration projects in an M&A context shape our perspective on AI implementations.
Modular approach
We start where your biggest bottleneck is—not with a rigid program, but tailored to your situation.
Initial consultation
Talk to us about your starting point
Whether you want to structure initial AI initiatives, build a platform architecture, or introduce governance—we’ll discuss the next realistic steps with you.
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.
