Effective water resource management hinges on a precise understanding of the water balance within a region's hydrological system. However, a significant and often overlooked challenge arises from **unmetered water concessions** – permits granted to private entities to abstract water directly from sources without instrumentalization. This absence of data creates a critical blind spot in the water balance, compromising the accuracy of reserve forecasts and the equity of rationalization policies. The sheer complexity of this data void makes **Artificial Intelligence and advanced inferential modeling** indispensable for transforming this problem into an opportunity for innovation.

At Zyllica, we apply an approach of inferential and strategic intelligence to bridge the data gap. Our methodology transforms the problem of unmetered concessions into a powerful asset for more precise management. Our approach includes Intelligent Consumption Estimation using statistical and Machine Learning models, Remote Monitoring with low-cost proxies like satellite imagery, and Strategic Audits for targeted field verification to calibrate and improve our models.