Forecasting required training a model for every single account. Now, a unified ML model predicts for 1M+ customers in minutes.
A Midwestern retail energy provider serves over 1 million residential and business customers with electricity, natural gas, and solar solutions.
Daily purchasing decisions require forecasting hourly consumption for contract durations, generating datasets of up to 50 billion rows. The legacy...
“Data is very important for our mission because it helps us make our offerings more reliable and more competitive for customers.”
Independent energy supplier of electricity, natural gas, and home protection services.
Cloud-based data warehousing, processing, and analytics platform.
Related implementations across industries and use cases
Legacy systems bottlenecked data from 60M endpoints. AI models now predict usage to cut 1,200MW from the grid and halve dev cycles.
Silos trapped data across 15 countries. Now, teams forecast hourly energy demand and personalize offers for 10M households.
Weather shifts made demand hard to predict. A self-learning model now uses forecasts and history to align production with real-time usage.
Permit teams drowned in 5,000 monthly requests across fragmented sites. Agents now navigate the web maze to process tasks in 2 minutes.
Service gaps left rural areas behind. AI agents now guide 9M users through 44 tasks on WhatsApp, unifying fragmented support.
Sonar review meant checking raw footage frame by frame. AI now scans hours of data to pinpoint targets for recovery.
Fragmented server data bottlenecked scaling. A unified model cut hardware SKUs by 50% and eliminated dozens of manual processes.