AI Services
AI Adoption
Getting started with artificial intelligence (AI): clarify the starting point, select a viable first use case, and define the technical path to it.
AI Services
AI Environments
The environment in which your AI is allowed to work: model access, data connection, access rights, and operations, as the foundation for every further application.
Typical starting point
What many companies lack is not the model, but a place where their own data can and may be processed with AI: with clarified access rights, defined regions, backup, and monitoring.
What we do
- Provision of basic AI environments: in the cloud (for example Azure), in your own data centre, or hybrid
- Integration of existing models and AI services through a uniform interface
- Data connection, interfaces, vector databases, and basic retrieval structures
- User and permission management
- Pilot infrastructure, evaluation, and cost control
- Implementation of the requirements for security, data protection, and operations
What you receive
- An environment that is described as code (infrastructure as code): reproducible, verifiable, transferable
- Separate environments for development, test, and production
- Traceable documentation of data flows and access
The environment is designed to be vendor-neutral. We connect language models through a uniform interface, for example through Azure or through a routing service such as OpenRouter; changing the model is then a matter of configuration, not a rebuild. Where data must remain in defined regions, we restrict the choice of providers accordingly or run models locally.
What an environment consists of
| Building block | Purpose |
|---|---|
| Access | A single public access point, encrypted; everything else cannot be reached from outside |
| User interface and API | Operation in the browser and a documented programming interface for your systems |
| Sign-in | Personal accounts, roles, access log |
| Background processing | Jobs run in a queue and resume after an interruption |
| Database with vector search | Master data, records, and the vectors for knowledge search in one place |
| Models | Language models in defined regions, embedding models running locally wherever possible |
| Monitoring and backup | Metrics, logs, alerting, encrypted backup |
Infrastructure as code
The environment is not created by clicking in an administration console, but from a versioned description (infrastructure as code): servers, network, databases, access points, and configuration are described as code and are built from it automatically.
- Reproducible: Every environment can be rebuilt, for a test, at a different location, or after an outage.
- Traceable: Every change to the environment is a change to the code, with date, author, and reason.
- Verifiable: The description can be read and reviewed before anything is built. This also applies to security and region requirements.
- Transferable: A qualified third party can take over operations on the basis of code and documentation.
Legally compliant and documented
We build the environment so that compliance with the requirements for data protection and data sovereignty can be demonstrated: defined regions, separate environments, a described data flow and deletion concept. As a matter of principle, real data is processed only in production; development and test work with synthetic or anonymised data. The legal assessment of the individual case remains the responsibility of your experts; we provide the technical foundation and the documentation for it.
Further topics
AI Services
AI Operations
Deployment, maintenance, and agreed operations: so that an AI application continues to run reliably after the pilot and can be developed further.
AI Technologies
Document & Knowledge Intelligence
Making large, heterogeneous bodies of information accessible and turning unstructured information into knowledge models that are usable for the business domain.
AI Technologies
Agentic AI
Individual model calls become controllable systems based on a division of labour: specialised agents handle subtasks, use tools, and work together in verified workflows.
Approach
From the first idea to the usable AI solution.
Understand the task and the data
Which question is to be answered, which data is available, and in what quality?
Set up the AI environment and architecture
Models, data connections, access rights and the operating model are defined and set up.
Develop and evaluate the application
A first end-to-end workflow with real data, tested against agreed criteria.
Integrate and advance the solution
Integration into your systems, handover or agreed operation, step-by-step expansion.
Would you like to make AI usable in your company?
Whether a first AI adoption or a complex development task – we discuss the requirements and suitable next steps with you.