Sovereign AI Infrastructure for Your Company
AI tools are finding their way into companies and teams, whether approved or not. The AI Trusted Zone counters this with a central, secured infrastructure that preserves data sovereignty, supports multiple models, operates GDPR-compliant and maps costs transparently. We implement your own AI gateway for your company, so your teams can work productively and safely with AI without disregarding company policies.
AI Trusted Zone at a Glance
What?
We implement a central AI gateway (based on LiteLLM) for your company. It connects your teams via a unified API to models from Anthropic, AWS Bedrock, Google Vertex or on-premises solutions. This includes central API key management, rights and role management, complete audit trails and budget limits per team or user. If needed, complemented by your own AI chat, comparable to ChatGPT but in your own environment.
When?
The AI Trusted Zone supports you in building a unified and controlled AI access point across your entire company, and enables you to securely process sensitive company data. It also helps with AI keys for training sessions and temporary projects, or with generating presentations and documents using internal data. It is less suited for occasional, simple text tasks without data-protection needs, or for extensive context engineering that requires its own development work.
Why?
Because a ban rarely works. Anyone without a safe alternative on hand will reach for whatever is available. The AI Trusted Zone provides that alternative: your data doesn’t train external models, stays in European data centers and remains under your control. Billing is based on usage instead of fixed costs, no vendor lock-in arises, and governance runs alongside without slowing down adoption.
Our Approach
Building an AI Trusted Zone is a structured project with a clear outcome: an operational, governance-compliant AI infrastructure that your teams can use from day one.
00
Current-State Assessment & Requirements Analysis
We assess the current state – which AI tools are already in use, unofficially or officially? Which regulatory and data-protection requirements apply? Which models and providers make sense for your context? This forms the basis for a suitable architecture.
01
Architecture & Model Selection
Together we decide which models and providers (Anthropic, AWS Bedrock, Google Vertex, on-premises) make sense, and define the architecture of the AI gateway. We take into account data protection, latency, cost and integration into your existing infrastructure.
02
Gateway Implementation
We implement the central AI gateway based on LiteLLM, including rights and role management, API key management, rate limiting, logging and budget limits. Teams can then access all configured models through a unified API, without having to manage credentials themselves.
03
Governance & Usage Policies
Together with technical leads and compliance owners, we define who may use what, how prompts and results are handled, and where the boundaries lie. The rulebook is practical – not a 50-page document nobody reads.
04
Rollout & Enablement
We support the company-wide rollout, help with internal communication and create practical usage guidelines. Optionally, we help build an internal AI competence center that operates and further develops the Trusted Zone long term.
AI Transformation Journey
Placement in the Journey
This service is part of the embarc AI Transformation Framework. The highlighted tile shows where in the journey it fits in – and which other building blocks complement it well.
01
Orientation & Exploration
Outcome: Shared vocabulary, first experience, prioritized use cases
Building Knowledge
Matching Services
Value Hypotheses, Risk Identification
Matching Services
02
Team Pilots & Validation
Outcome: proven usage patterns and a solid state of practice for scaling
Measurable Value
Matching Services
Evaluating Development Practices, Agentic SDLC
Matching Services
Reference Use Cases, Deployment in Real-World Environments
Matching Services
03
Platformization & Controlled Scaling
Outcome: A "Golden Path" for teams, on which agents can work within clear boundaries.
Architecture, Quality & Observability
Matching Services
Professionalization, Security, Governance & Compliance, AI-FinOps
Current Service
Matching Services
AI Development Platform (Context, Harness, Hooks and Processes)
Matching Services
04
AI-Native Product Organization
Outcome: A new "AI Engineering Operating Model" for the learning, AI-native product organization.
New Role Models, Skills & Career Paths
Matching Services
New Organizational Structures: Teams, Communities, AI Hubs
Matching Services
Intent-Orientation and Outcome-Based Steering (incl. Continuous Validation)
Matching Services