Most AI projects die between demo and production. We bring them through.

Where AI pays off for you, and where it does not

Potential analysis & consulting

Not every process gets better with AI. Some just get more expensive. Before we build, we analyze together with your business units where AI creates measurable value, and state just as clearly where classic automation or a better process is the more economical answer.

• Systematic use-case evaluation: We prioritize your candidates along value contribution, data situation, integration effort and regulatory risk, instead of gut feeling or hype.

• Data reality instead of data wishful thinking: We check whether the data a use case requires actually exists in the necessary quality and accessibility, the most common cause of death of AI projects.

• Honest recommendation: If a project does not carry its weight, we say so. Our business model is the long-term operation of working systems, not the sale of pilots.

Specialized AI agents for your business processes

Generic chatbots answer questions. Specialized AI agents do work: they check incoming documents, schedule orders, monitor telemetry, prepare decisions, embedded in your existing workflows and systems.

• Integration into grown landscapes: Your most valuable data lives in ERP, business applications and databases that are sometimes decades old. We connect AI agents to exactly these systems via clean interfaces, without risky interventions in the existing estate and without a big-bang migration.

• Connected to your data and processes: Agents do not work on general world knowledge, but on your operational data, with permission concepts that define which agent may see and do what.

• Humans decide, agents assist: Critical steps (approvals, orders, customer communication) remain in human hands. The agent prepares, justifies and documents; your employees decide.

• Measurable benefit: Every agent has a defined process, a measurable KPI and a clear owner. AI as an operating asset, not as an experiment.

Agentic software development: deliver faster, without losing control

We use AI not only for our customers but consistently in our own work: our development process is agentic: specialized AI agents support every phase, from requirements analysis through architecture and implementation to testing and documentation. Always following the same principle: human in the loop.

• Analysis & requirements: AI agents structure requirements, uncover contradictions and gaps and produce reviewable specifications, which your and our domain experts assess and approve.

• Implementation: Agents generate code proposals within our architecture and security guidelines. Every line passes mandatory review by experienced developers and automated quality analysis. Nothing enters the system unchecked.

• Testing & quality assurance: Agents generate test cases even for the edge cases that fall away in classic projects due to time constraints, increasing test coverage instead of replacing it.

• Documentation: Business and technical documentation is created in sync with development instead of months later, making it permanently up to date for the first time.

Your advantage: shorter delivery times and higher, consistent quality, with full traceability. You benefit from the speed of agentic development without carrying its risks, because responsibility for every result lies with a person with a name, not with a model.

Self-hosted AI: your data never leaves the house

For design data, source code, customer and health data, the path into public AI services is not an option. That is why, when needed, we operate AI systems where your requirements demand it: in your own infrastructure or EU-hosted.

• Full data sovereignty: Models, vector stores and processing pipelines run in your environment. No prompt, no document leaves your sphere of control.

• Open models, operated production-ready: We select and operate open-weight models to fit the use case, including updates, hardening and performance tuning across the entire lifecycle.

• Hybrid architectures where sensible: Non-critical workloads in the cloud, sensitive ones in-house, with an architecture that enforces the boundary technically instead of hoping organizationally.

AI governance & compliance: the EU AI Act as blueprint, not brake

The EU AI Act is in force; its obligations apply in stages and affect almost every company that uses AI productively. What that means for you in concrete terms:

• You need an inventory: Which AI systems are in use, including those business units introduced on their own? Without a survey there is no risk classification.

• You need a risk classification: The AI Act regulates by risk classes. An internal research tool is subject to different obligations than a system that prepares personnel decisions. The classification determines your effort.

• You need traceability: For relevant systems, the AI Act demands documentation, logging and human oversight, requirements that can hardly be retrofitted into a pilot.

That is exactly our approach: we build governance into the architecture instead of documenting it afterwards. Logging, permissions, human-in-the-loop control points and audit trails are system components with us, following the same ISMS processes (TISAX) we have been working with for the automotive industry for years. You get systems that withstand an audit because they were built for it.