AI automation for companies

What these flows share is that the rules can be described, but the input does not always look the same. That is where AI makes the difference over ordinary automation.

• Invoices, receipts and delivery notes read, checked and entered into the finance system.
• Orders and requests from email and forms turned into cases with the right customer, product and priority.
• Reports compiled automatically from several systems, with a short note on what has changed.
• Onboarding of customers and staff, where documents are collected, checked and filed in the right place.
• Translation and adaptation of copy for several languages and markets.

Not everything needs AI. When the data is already structured, an ordinary integration is often enough, and it is cheaper and more predictable. I use AI where the input is text, images or exceptions a rule cannot catch, and plain code for the rest.

So an automation is often a chain: code that fetches and checks, a language model that interprets, and a person who approves whatever is uncertain.

An automation has to work on an ordinary Tuesday, not just in a demo. Every run is logged, failures alert the right person, and anything the model is unsure about goes to a review queue instead of being guessed.

I build automations in code, with queues and scheduling that survive failures and restarts, so they can be versioned, tested and reviewed like the rest of your software. If a tool such as n8n is already in place, I can build on it.

I can run the automations for you, with monitoring, updates and steady improvements. If you would rather run them yourselves, your team gets the code, the documentation and a walkthrough of how everything fits together.