AI & data processing with measurable value

Yes. We choose architectures that preserve data protection and data sovereignty, from on-premise models to EU/CH-hosted services, depending on requirements.

We assess that honestly. If a classic approach fits better, we say so. AI is a means to an end.

Usually within a few weeks via a focused proof of feasibility on real data.

Yes. Projects often start with an assessment: where does AI pay off in your processes, what is hype, what adds up? You then decide whether we handle the implementation too — consulting and engineering come from one team.

An assessment with a concrete action plan is manageable in scope; implementation projects depend on your data and integrations. After a free initial call you receive a clear estimate — and we tell you if AI is not worth it for your case.

For broad, open-ended tasks, frontier models stay strong. But if you only need AI for one or two clearly defined cases and have the data, we train an open model on exactly that. It handles the job reliably, at a fraction of the token cost.

At sufficient volume, our clients typically save 80 to 95 percent of their running model costs, because each request no longer pays the full frontier price list. Whether it pays off depends on your volume. We work that out honestly upfront, including training and hosting costs.

Examples of your use case, the way they occur in production. A few hundred to a few thousand good examples are often enough. If you already run a frontier model, your existing requests and responses are usually the best training material.

For a narrow use case, yes. A small model trained on exactly one task regularly beats a large general-purpose one there. We prove it before the switch with an eval set on your real data, so the quality is measurably right.

Your model runs wherever you want: in your own cloud, on-premise, or with a CH/EU host. The data and the trained model are yours, no detour through someone else's API.