YAZZOOM

Alexis Piron is Sales Manager at Yazzoom, the AI & Machine Learning company based in the Belgian city of Gent where computer science and engineering are combined to solve many different challenges in all industrial sectors. He takes time out to explain what makes this SME tick.

From undergrowth to clearing

Yazzoom has been around for about eight years. Its beginnings can be traced to 2011 when the company was focused on using data from industry for a whole range of purposes. “It was largely a matter of employing our knowledge of modelling and engineering and matching this to market needs,” Alexis explains, “and in the first few years we tended to undertake projects for companies aimed at using their data to improve the quality of production and products. And using the knowledge and experience we gained in this period, we evolved into developing what is our current product line of data analytics solutions, transitioning from project to product. We cut our way through the undergrowth, if you like, and paved a path towards what we have become today. So yes, we are a company in transition. In fact, we are always in transition – we have to ensure that we stay at the forefront in an environment that changes from day to day so that we can come up with solutions to the constantly evolving challenges.”

Into the unknown

Yazzoom’s application domain solution is more or less the same for all its customers whether in the energy, chemical or automotive sectors. But why do they come to Yazzoom, what is it that makes Yazzoom unique? “A good question,” Alexis replies. “What sets us apart is our ability to combine machine learning and AI with engineering disciplines such as physics, chemistry and advanced process control – this helps to run the results of your machine learning models in the actual production environment. So that’s one thing. Another is our focus on unsupervised machine learning, which is not yet that uses supervised learning, for example."

Cutting away the undergrowth again? “Yes, we are in a much less developed area at this point in time. But it’s a vital matter for our customers because production downtime can be a very expensive situation for them, in terms of revenue losses. This refers to something that happens to disrupt production that is previously unknown or unprecedented. So you’re not prepared for the problems that arise. The traditional methods of monitoring and predictive maintenance are based on the things you know, what has happened in the past. With our unsupervised machine learning, we are clearing the ground to establish something that could be a real benefit to production processes.”