We Think

Maiton is always looking to improve our services. Therefore we actively develop methodologies and standard procedures. This improves quality and speeds up the time-to-market.


We are an AI agency with a strong focus on value creation. Our service offer can cover the complete life cycle of your project: from inception to prototype over realization and finally maintenance. However, each of our services can just as well stand alone. The choice is yours.

The first step is to sit together with you and see how you can get the most out of your project.

Next we build a fast prototype that demonstrates that the goals of the mission can be done. Often resulting in a easy to use interface such as Excel or Google Sheets, that allows you to get experience with the model. This phase is a phase of rapid iteration and the focus is on value creation.

Once the business value is established, we build a technical more robust version. Preferably, we run the model on our cloud infrastructure but can also deliver a docker container you can run on your premisses. The goal here is a high-quality service.

Some models might need monitoring their performance. Certainly when the business conditions change compared when they were developed. We also take care that the underlying service is reliable and fast.


Data Science
The core of our business. Maiton has been early on the game and has delivered projects for many clients around the world.

One way we differ from most other companies is that we are as well business people as engineers. In fact, it will be hard to find a company that has taught more business school students how to use data science inside their company and engineering students how to do data science. We try to stay close to the original meaning of data science. This is also directly translated in the missions we have, a good part of them is about managing or starting a data science division.

Design Approach
The focus on having an impact, translates directly into a design approach. We will look for the value first, before diving into deep modelling. Our experience has learned that if there is potential in a project, it shows early on. Getting the best out of it, however, that takes the craft-man ship only education and experience can give.

Natural Language
Over the years, we also developed a keen knowledge about working with natural language. It is probably the hardest field in data science. Where as a horse will look like a horse in France, the UK and in Belgium, the language representation of that horse looks very different.