Weather shifts made demand hard to predict. A self-learning model now uses forecasts and history to align production with real-time usage.
An electricity and heat producer serving Finland's capital, where the majority of homes rely on district heating year-round.
Efficient operations require matching heat generation exactly to demand, but fluctuating consumption made it difficult to plan production accurately....
Energy utility provider of electricity, district heating, and cooling services.
AMD is a technology company that specializes in designing and manufacturing semiconductors, processors, and graphic cards.
Developed an AI solution for Helen to predict energy consumption levels based on historical data.
Related implementations across industries and use cases
Operators chased leaks with scattered data. Now, a digital twin predicts risks, letting teams repair pipes before they fail.
Forecasting required training a model for every single account. Now, a unified ML model predicts for 1M+ customers in minutes.
Staff reacted to weather delays as they unfolded. ML now predicts disruptions 12 hours early, letting teams prioritize interventions.
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.