Discover our projects

Schneider electric

Using Machine Learning libraries developed with Cartesiam’s NanoEdge AI Studio, we were able to anticipate behaviors that were previously difficult to detect.


We needed a technology for predictive maintenance that was undeniably industrially proven and with rapid implementation. After comparing several solutions on the market, we chose NanoEdge AI from Cartesiam.


Cartesiam’s NanoEdge AI Studio, through its ease of use and the artificial intelligence applied directly in each connected object, enhances our data capture expertise.


Literally, within a couple of weeks after we bought NanoEdge AI Studio, we launched « Wales », a detector of water leakage with embedded Edge AI technology from Cartesiam.

Naval Group

Version 2 of NanoEdge AI Studio, in particular, allows Naval Group’s teams to increase the quality of production data analysis and thus take a new step forward in naval maintenance.

NKE Watteco

Bob Assistant, based on Cartesiam technology, is now installed in dozens of industrial sites across Europe and provides 24/7 predictive maintenance for thousands of equipment such as pumps, air conditioners, engines and HVAC’s.


The integration of the NanoEdge AI learning machine, directly « at the Edge », allows the deployment of systems to improve the performance of old or recent equipment that is being retrofitted. 

Lacroix electronics

We have used Cartesiam’s NanoEdge AI Studio to develop a very promising preventive maintenance solution on our production sites. 


We partnered with Cartesiam on a highly innovative project, involving resources in France and China, and delivered, in a record time, a highly effective predictive maintenance solution on critical motor control applications.

Bosch Connected Devices and Solutions GmbH

NanoEdge AI™ ability to process data for anomaly detection – for one or multiple sensors – is the perfect match.


The NanoEdge AI studio solution has enabled us to launch the realization of the IRMA predictive maintenance sensor, with our own teams, without having to recruit machine learning experts.