Automation and AI that survives production

Most AI projects do not fail because of the model. They fail on everything around it: data that lives nowhere in particular, processes nobody ever wrote down, tools that refuse to talk to each other. That is where I start.

I come from infrastructure. Which means I do not build things that only work in the demo.

Services

• Workflow automation

  Turning repetitive work into n8n workflows – including the interfaces to the systems you already run.

• AI on your own hardware

  Running language models in house instead of handing data to third-party APIs. Selection, setup, operation.

• Infrastructure

  Servers, networking and data centre hardware – planned, built and operated across Windows and Linux.

• Assessment

  A look at your processes and a straight answer on where automation pays off – and where it does not.

How it works

1. Conversation. You describe what eats your time. I ask questions until I understand it. Free of charge.
2. Assessment. You get it in writing: what can be automated, roughly what it costs, and what I would advise against.
3. Implementation. In small steps, each one useful on its own. No project that delivers nothing for six months.
4. Handover. Documentation and training so you can run it yourself. I stay on for operations if you want me to.

When I am the wrong person

If you want a chatbot because AI is on the agenda, with no actual problem behind it - I will tell you so in the first conversation instead of selling you a project.

Contact

Tell me briefly what it is about:

Or via LinkedIn.