The core of this project lied in the integration of cloud platforms and artificial intelligence to enhance prevention of fire risks, that are due to electrical causes and therefore improve building safety. By utilizing machine learning algorithms, we analyzed real-time data from electrical and environmental sensors to identify anomalies and predict high-risk situations. This approach not only allowed us to detect potential threats before they turned into incidents but also optimized preventive and corrective interventions. Through clear data visualization and real-time alerts, we successfully created a safer environment while ensuring regulatory compliance and the sustainability of infrastructures.

This project leveraged artificial intelligence to detect equipment malfunctions using images and temporal data. By analyzing images and temporal trends, the system identifies potential issues and helps resolve them quickly, enhancing maintenance and operational efficiency.

This project aimed to develop an AI solution to optimize comfort and energy efficiency in meeting rooms. By exploring reinforcement learning algorithms and analyzing temporal sensor data, we identified promising methods to enhance user confort while reducing energy consumption.

This project involves a data visualization solution designed to transform complex datasets into understandable information. Using advanced visualization tools, the system enables users to uncover trends, patterns, and hidden insights, facilitating informed decision-making.

The project aimed to support clients in their decarbonization initiatives. The secure backend solution we implemented is based on a robust and maintainable architecture, facilitating the smooth and efficient addition of new features. We developed dynamic questionnaires to assess the specific needs of users, as well as calculation modules for detailed measurements of the environmental impact and estimates of the necessary investments and future revenues associated with decarbonization projects. Support was also provided to assist with project certification, ensuring compliance with environmental standards. This integrated approach served as a catalyst for sustainable practices while ensuring data security and confidentiality.

This project aimed to seamlessly integrate the lifecycle of artificial intelligence models into a predictive maintenance system, spanning from Edge devices to cloud infrastructures. By combining real-time data collected from the field with advanced analytics deployed at the Edge, we created a solution capable of anticipating failures and optimizing operations. This proactive approach not only enhanced operational efficiency but also reduced costs associated with unexpected downtime.

This project involved developing an AI solution to optimize complex processes. By analyzing operational data and applying advanced algorithms, we identified significant opportunities for improvement, increasing efficiency and reducing costs.