The Virtual Quality Assistant aims to automate in-line quality measurements. It uses AI models to measure quality characteristics such as dimensions, color, number of defects, type of defects, and dry matter content, all in-line. The results are then used to optimize process parameters or to visualize insights on a dashboard.

The Virtual Quality Assistant can be widely applied. Quality is important both upstream and downstream in production, and it can also be used for packaging inspection. We are already working with companies such as Agristo, Warnez Potatoes, Roger&Roger...

The biggest challenge in AI is the enormous need for high-quality data. Obtaining this data to train the models is very resource-intensive. Therefore, the Virtual Quality Assistant uses "synthetic" data. We simulate synthetic potatoes to train the models. This ensures that the models are highly robust and scalable.

If necessary, we implement a stand-alone hardware unit equipped with the required sensors. This unit is fully connected to existing systems to fully embed the solution into the process.