Realize
You know what you want to achieve. Now it has to work.
Maybe you already have a clear idea for an AI application. An internal assistant that can work with your knowledge. Something that analyses documents. An agent that combines information from different systems. Or software that has suddenly become feasible thanks to AI.
The idea is there. Maybe even a prototype.
But between “this ought to be possible” and something people can actually use, there is quite a bit of work.
Going from a good idea to working AI takes more than a good demo.
From desired outcome to technical solution
You don't need to arrive with a fully worked-out technical specification.
Tell me what it ultimately has to do, who has to work with it and in which environment it has to function. Coming from systems engineering, I translate that into requirements and a technical approach. For example, we look at:
- what the application actually has to do;
- who has to work with it;
- which information and systems are needed;
- what AI may do independently;
- where human control is needed;
- which existing software we can use;
- what we need to connect;
- and what we may have to build ourselves.
After that, we can actually build it too.
Not just thinking it up. Building, testing and using.
Depending on the question, I can build a prototype to test an assumption quickly, connect existing AI tools and systems, develop software or agents, open up information sources, and test with users whether it does in practice what it should.
The goal is not an impressive AI demo.
The goal is something usable in your real environment — and where along the way we can show it actually solves the problem we started with.
Example — Systemmatic
At Systemmatic, AI was applied to processing ICAO State Letters. Incoming PDFs were read and interpreted automatically; AI extracted ICAO annexes, deadlines, official and internal references and other metadata from the documents.
Work that required about three administrators could largely be done by one.
Not just an AI prototype, but new operational capacity.
Start small. Make it work. Then go further.
A first version doesn't have to deliver the full picture right away. With AI especially, it is often wiser to make the most important part work first and test it with real users and real information.
If it works, we build further. If an assumption turns out to be wrong, we find out early.
That keeps the investment manageable while something concrete exists from the start.
Not ready for a call yet?
Send me one or two sentences about what you would like to build.