We put AI into production.

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

Potential analysis & consulting

Not every process improves with AI. Some simply get more expensive. Before we build anything, we work with your business units to pinpoint where AI creates measurable value, and we say just as plainly when conventional automation or a better process is the more economical answer.

• Systematic use-case evaluation: We rank your candidates by value contribution, data maturity, integration effort and regulatory risk.

• Data reality instead of data wishful thinking: We check whether the data a use case needs genuinely exists, in the quality and accessibility required. This is the single most common reason AI projects fail.

• Honest recommendation: If a project does not stack up, 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 check incoming documents, schedule orders, monitor telemetry and prepare decisions, all embedded in your existing workflows and systems.

• Integration with landscapes that have grown over decades: Your most valuable data sits in ERP systems, line-of-business applications and databases that are sometimes decades old. We connect AI agents to exactly those systems through clean interfaces, with no risky surgery on the existing estate and no big-bang migration.

• Connected to your data and processes: Agents work on your operational data, not on general world knowledge. Permission models define which agent may see and do what.

• People decide, agents do the groundwork: Critical steps (approvals, orders, customer communication) stay in human hands. The agent prepares the case, explains its reasoning and documents it; your people make the call.

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

Agentic software development

We do not just deploy AI for our clients, we run our own work on it: specialized AI agents support every phase of our development process, from requirements analysis through architecture and implementation to testing and documentation. Always on the same principle: human in the loop.

• Analysis & requirements: AI agents structure requirements, expose contradictions and gaps and produce reviewable specifications, which your domain experts and ours assess and sign off.

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

• Testing & quality assurance: Agents generate test cases for the edge cases too, the ones conventional projects drop when time runs short. Test coverage goes up as a result.

• Documentation: Business and technical documentation is created in step with development, which keeps it permanently up to date.

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 stays in-house

For design data, source code, customer and health data, the path into public AI services is not an option. So we run AI systems wherever your requirements demand: inside your own infrastructure, or hosted in the EU.

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

• Open models, run to production standard: 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. The architecture enforces that boundary technically, rather than leaving it to organizational goodwill.

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

The EU AI Act is in force; its obligations apply in stages and affect almost every company running AI in production. Here is what that means in practice:

• 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 how much work follows.

• You need traceability: For systems in scope, the AI Act demands documentation, logging and human oversight. Retrofitting those requirements into a pilot is next to impossible.

That is exactly our approach: we build governance into the architecture instead of documenting it afterwards. Logging, permissions, human-in-the-loop checkpoints and audit trails ship as part of the system, 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.