A leading utilities provider in the Netherlands needed a cost-effective way to monitor distributed assets and improve field operations. Aeologic deployed a Bluetooth Low Energy (BLE)-based monitoring platform integrated with utility infrastructure, enabling real-time asset tracking, workforce visibility, and predictive maintenance while reducing operational complexity across geographically dispersed networks.
The utility managed substations, field equipment, maintenance tools, and mobile workforces across multiple service regions using legacy tracking methods. Operations teams lacked real-time visibility into critical assets, making it difficult to coordinate field activities, optimize maintenance schedules, and respond quickly to service interruptions.
Rather than replacing existing government systems, Aeologic implemented an AWS SageMaker machine learning layer that consolidated operational datasets, automated data preparation, trained predictive models, and generated real-time forecasts for service demand and resource planning. The solution integrated with existing government applications, providing secure analytics dashboards, automated reporting, and AI-powered recommendations for policy and operational teams. AWS SageMaker is widely used to build, train, and deploy machine learning models at scale.
A centralized intelligence dashboard provided administrators with predictive demand forecasts, operational KPIs, anomaly detection, and interactive analytics, enabling departments to make faster decisions while improving collaboration across government services.
"We moved from reacting to operational issues after they occurred to planning for them days in advance. Predictive analytics has significantly improved how we allocate resources and deliver public services."
— Program Manager, Utilities Initiative (illustrative quote)