Trusted AI Integration
We integrate AI into enterprise processes with a clear focus on security, transparency and productive value in complex and regulated environments. Our work goes beyond isolated chat applications and focuses on AI solutions that fit into existing software landscapes and business processes.
Our focus is on AI solutions that are controlled, governable and production-ready. This includes data sovereignty, access control, governance, compliance, system architecture, evaluation concepts and stable operations.
AI integration for production enterprise systems
Trusted AI Integration connects AI technologies with the requirements of real enterprise architectures. The focus is on secure use of internal knowledge sources, semantic search and assistant solutions, LLM orchestration and integration into existing processes.
We address privacy, security, compliance and operations — including requirements related to GDPR, DORA or NIS2 — and design architectures that are robust enough for productive use.
How we support
Use-Case Discovery
Assessment of suitable AI use cases with regard to value, risks, data availability and feasibility.
Knowledge Integration
Connecting internal documents, data sources and knowledge repositories for semantic search and assistant systems.
RAG Architectures
Design of retrieval-augmented generation solutions with controlled data and access paths.
LLM Orchestration
Integration, control and safeguarding of language models, services, prompts and processing chains.
Governance & Compliance
Addressing data sovereignty, access control, traceability, privacy and regulatory requirements.
Production Readiness
Definition of testing, evaluation, observability and operating concepts for stable AI solutions.
Typical Questions
- Which AI use cases create real value and can be implemented responsibly?
- How can we use internal knowledge sources without losing data sovereignty or access control?
- How do we integrate LLMs securely into existing processes and software landscapes?
- How do we ensure traceability, quality, evaluation and stable operations?
- How do we avoid AI prototypes that never become production-ready solutions?
Successful AI solutions require a robust Enterprise Architecture and a well-founded Solution Design.