We build AI applications that are production-ready - a cleanly structured data foundation, embedded in real enterprise context.
In mechanical and plant engineering, quality is built over decades. It sits in service reports, in bills of materials, in operating manuals from 1998 - and in the heads of colleagues who simply know things.
So the knowledge exists. It is just rarely where the decision is being made: at the machine, in the quote, in the handover to the next generation.
That is exactly what we work on. Not a chatbot on top, but a data foundation underneath - so every answer comes from your own documents and you can see where it came from.
Whether special-purpose machinery, series production, plant engineering or contract manufacturing: the starting point differs less than you would think. Pick the situation closest to yours.
Fewer callbacks to head office, more time at the machine.
Four clear phases, each with a deliverable your team builds on. After go-live, your team takes over - clean handoff, not dependency.
On site: the state of your data, your systems and your workflows. The result is a solid basis for a decision - including when it argues against a project.
One use case with your data in your environment. Measured from the first week, not only at the final presentation.
Connected to ERP, PLM, DMS and the service portal. Rights, logging and monitoring are part of it.
Your team operates and maintains the system itself. We stay reachable - we are just no longer necessary.
We build benchmarking and quality measurement in from day one. So you always know how well the system really performs - not just whether it responds.
For companies that know AI is relevant - but not yet which case pays off and what is missing from the data foundation.
Systems that turn your documentation, your drawings and your history into something you can query - in the office, on the shop floor and at the customer site.
Workflows that have so far failed on unstructured input - enquiries, drawings, inspection records, complaints.
Internal teams are often too close to day-to-day operations to consistently implement clean data pipelines, quality assurance, monitoring, and access control.
Years of hands-on project experience - the shortcuts that seem tempting internally end up being expensive later.
A roadmap is valuable - but only once someone consolidates the data, builds the system, and hands it over to operations. That's the gap we work in, often hand in hand with strategy partners.
We know the pitfalls between concept and production - and we deliver the implementation that turns concept decks into real impact.
The question isn't whether AI will become relevant, but who builds a reliable data foundation first that others can't catch up to.
Every quarter without a structured AI foundation is a quarter of advantage for your competitors.
Every system we build is GDPR-compliant and EU AI Act-ready. Governance is built into the data layer, not around it - saving your IT team rework later.
No sales funnel. You speak directly with someone who has built systems - not an account manager.
