• Health status and predictive maintenance capabilities through a blend of AI-based predictions. The integration of cutting-edge technologies, different assets, data and roles enhances the efficiency, accuracy, and sustainability of maintenance processes. Extend the assets lifetime by means of predictive maintenance and early detection of failure modes​ using GEN AI reports.
• Evaluate and improve the reliability of your assets by identifying risks, analyzing root causes of failures, and providing a comprehensive view of the health status and functional loss risk to optimize lifecycle management and minimize operational disruptions.
• Optimize maintenance management by integrating platform information with CMMS/GMAO systems, calculating the Remaining Useful Life (RUL) of critical assets, and prioritizing maintenance activities based on criticality, to maximize asset availability and reduce operating costs.
• Enhance performance and maximize productivity and profitability of operations through continuous optimization of asset and process performance.
• Chat Bot – Virtual Agent – Get quick and accurate answers about health status, operational and financial risk and performance, and asset-related events 

María José Gómez García, a distinguished professional in the field of predictive maintenance and reliability engineering. As a key member of the team at PREDICTEC, María José has been instrumental in driving innovative solutions that enhance the efficiency and reliability of industrial operations. Her expertise lies in leveraging advanced technologies to monitor equipment condition, anticipate potential failures, and optimize asset performance.

María José’s work reflects a deep commitment to advancing Industry 4.0 principles, integrating tools such as data analytics and intelligent monitoring systems to create smarter, more sustainable maintenance practices. Her contributions have not only improved operational reliability for numerous organizations but also underscored the importance of predictive strategies in modern asset management.

Beyond her professional endeavors, María José is an active participant in technical forums and conferences, where she shares her insights on the evolving landscape of reliability and predictive maintenance. Her dedication to the field makes her a valuable voice in the ongoing conversation about the future of industrial innovation.

Jesús Puebla Guedea is an engineer specialized in reliability and predictive maintenance, currently playing a key role at PREDICTEC, where he focuses on the development of strategies and services aimed at predictive maintenance. His expertise lies in optimizing industrial equipment performance through early failure detection, helping to reduce operational costs and minimize downtime. Jesús is an active contributor to the field of condition-based maintenance, integrating cutting-edge technologies such as data analytics and machine learning within the Industry 4.0 framework. His work emphasizes practical applications, providing companies with efficient solutions to improve asset reliability and sustainability.

In addition to his professional achievements, Jesús is a frequent speaker at specialized conferences, sharing his insights on implementing predictive maintenance programs and advancing reliability engineering. His contributions reflect a deep understanding of the challenges and opportunities in managing critical systems.