PARTNER
St. Louis Water Division
USE CASE
Water Utilities
LOCATION
St. Louis, MO, USA
Water utility operators managing 1,440 miles of trunk mains, treatment plants, and reservoirs faced increasing pressure from aging infrastructure, fragmented data systems, and manual operational processes. Limited predictive visibility made it difficult to detect emerging risks early, contributing to higher levels of non-revenue water, service disruptions, and inefficient allocation of maintenance resources.
Integrated intelligence combined infrastructure network models, operational data, and predictive analytics to create a digital twin of the water distribution system. AI-enabled analysis improved detection and localization of leaks and breaks, automated prioritization of work orders, and enabled faster identification of anomalies across the network, supporting more proactive system management.
Enhanced system visibility enabled earlier identification of network anomalies, reducing response times from hours to minutes and improving prioritization of maintenance interventions. Improved detection capabilities support reduction of non-revenue water, lower operational costs, and more efficient allocation of capital investment across critical infrastructure assets.
