Computer Software - Madrid, Community of Madrid, Spain
MonoM, a subsidiary of Grupo Álava, is a provider of a data platform for Industry, Energy, and Water sectors. Its primary objective is to digitize the management and monitoring of assets and production processes for each organization, eliminating information silos and enhancing productivity. Our platform is a SaaS solution, leveraging cloud computing, big data, edge computing, artificial intelligence, and generative AI. In conjunction with machine learning algorithms and the expertise of our predictive analysts, it is capable of forecasting and monitoring asset conditions. The goal is to deliver essential and appropriate information to stakeholders, enabling them to make decisions that increase profitability, performance, and asset longevity. The platform is specifically engineered for advanced water cycle management, addressing key use cases (from smart meter reading to leak detection, quality control, and beyond). It is also designed for energy generation assets, such as wind and hydroelectric facilities, as well as process industry assets. This enables production chains to be easily digitized and, more importantly, maintained predictively based on requirements, regardless of the hardware utilized, as the platform is hardware-agnostic. To achieve this and offer the market's premier data platform, we possess a wealth of experience across multiple IT domains, as well as in OT technology, which we have acquired over decades. We have accumulated expertise and knowledge in specialized fields such as maintenance processes, reliability, and performance optimization. Our experience extends to assets including motors, turbines, pressure groups, wind turbines, and many others, as well as technologies in vision systems, sensors, auscultation, and digitalization, among others. Our solution stands as the best-in-class, uniquely positioned to address the specific use cases of your industry. It has been constructed with a modular and scalable architecture to grow in tandem w
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