Operators chased leaks with scattered data. Now, a digital twin predicts risks, letting teams repair pipes before they fail.
Finland's utility infrastructure includes over 16,000 kilometers of district heating pipelines and 3,000 kilometers of water lines in the Helsinki area alone.
Operators relied on reactive methods that located leaks only after they occurred, causing service interruptions and significant resource waste....
“Silo AI’s service will increase energy efficiency, performance, and productivity of [our] customers’ network systems by double digit percentages.”
AI lab providing custom AI solutions and sovereign large language models.
AMD is a technology company that specializes in designing and manufacturing semiconductors, processors, and graphic cards.
Collaborated with Silo AI to build a web-based interface for visualizing its pipeline data assets
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Weather shifts made demand hard to predict. A self-learning model now uses forecasts and history to align production with real-time usage.
Plastic pipes don’t resonate, silencing standard tools. AI analyzes audio to pinpoint leaks, cutting non-revenue water from 27% to 10%.
Manual sampling led to chemical overuse. AI now calculates precise dosages and predicts failures, saving 30,000 pools of water daily.
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.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.