How we work.
Your off-the-shelf software does eighty percent. We focus on the twenty percent that makes your business distinctive. To do that, we translate the trade, build applications and use AI where it adds value. We look at where the friction is first, and start there.
Translating
The trade translated into a design.
Where the friction is. The off-the-shelf software has been chosen or is already in place, but the trade is not in it yet. The vendor builds what is in the requirements, and the requirements do not yet describe how your business works.
What I do. I sit on your side of the table, as product owner or business analyst, usually around Microsoft Dynamics. I write the requirements from the trade and make sure the system follows your work.
What you get. Off-the-shelf software that does what your business needs, and a foundation that applications and AI can later build on.
Building
Applications for the twenty percent.
Where the friction is. What makes your business distinctive runs in Excel or lives in the heads of a few people. Rebuilding the off-the-shelf software makes every update expensive and nerve-racking.
What we do. We build purpose-built applications for planning, trading and production: a dedicated cloud application next to your off-the-shelf software, with a solid integration. Your software stays as it is, your trade gets its own place. Our own planning application, ViQ Planner, runs in production.
What you get. An application that works the way your business works.
Making AI work
AI where it adds value.
Where the friction is. The demo was impressive, but on your own data it produces noise. Or the licences are running and the team works around it.
What we do. We use AI in three places. In the application itself, for example to assess a product from a photo or to forecast what is coming in. In your processes: automating manual work that takes time today. And in your data: structure and meaning, so answers are right.
What I do. Alongside building applications, I advise on AI, at three levels. Strategically, on direction and ownership: where AI pays off first and who decides. At product level, on AI in your software: which AI features fit and how they work safely with your data. Operationally, on less manual work: which tasks AI takes over and how the team works with it. The Work page has two examples: the semantic layer and the AI strategy.
What you get. AI that gets used, and answers you can explain.
AI in our own work
We work with a team of AI agents: in research and analysis, in building and in looking after applications. That lets us move fast with a small team, and you see AI working in practice during the project itself.
No fixed order.
Sometimes it starts with translating the question, sometimes we build on what is already there, sometimes AI itself is the answer. We look at where the friction is, and that is where we start.