Our expertise covers end-to-end data management, from defining relevant metrics to monitoring industrial systems and processes, to integrating data processing within DevOps/MLOps processes. Our goal is to determine the current status and future prospects of each system and process to optimize their availability, efficiency, and sustainability.
We are specialists in the entire data lifecycle, working from the definition of the most relevant metrics for a given problem, the generation of synthetic data, the inclusion of virtual sensors, data analytics, as well as in the productivisation in a DevOps cycle of digital twins, artificial intelligence models (MLOps) and algorithms in general that involve a data flow.
We use simulation and artificial intelligence techniques to develop algorithms and digital twins. This enables us to diagnose and predict the performance of components, systems and processes, within the framework of predictive and prescriptive maintenance.
Our pioneering and high-level facilities offer the essential tools to carry out tests and validations of different technologies and thus address challenges such as sustainability, energy efficiency and cybersecurity.
Thanks to the DataOps concept, we can improve the integration and automation of data flows by addressing the entire data lifecycle, from its conception to the productisation of services for the exploitation of these data flows, such as digital twins, with a special emphasis on monitoring and predictive and prescriptive maintenance.