Veli develops a manufacturing system that utilizes real-time data analytics and machine learning algorithms to monitor household utilities for irregularities. This technology identifies inefficiencies and potential failures, enabling users to lower energy consumption and prevent costly utility disruptions.
Funding
$853.1K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional emergency response systems often require manual activation, which can be challenging for elderly individuals during a fall or other emergency situations. Existing solutions may also require additional hardware installations, impacting the privacy and independence of residents.
Solution
Veli provides an AI-powered smart home monitoring system that analyzes existing household utility consumption data to automatically detect anomalies indicative of emergencies or potential care needs. By learning typical consumption patterns, Veli's algorithms can identify deviations such as unusual inactivity or continuous water flow, triggering alerts to caregivers or emergency services without requiring any action from the resident. The system uses existing smart meter data, eliminating the need for intrusive sensors or cameras and preserving the resident's privacy. Veli aims to enable seniors to live independently for longer while providing peace of mind to their families and caregivers through proactive, automated monitoring.
Target Audience
Veli targets service providers in assisted living facilities, operators of senior living communities, and private households with elderly individuals seeking to maintain independence while ensuring safety and security.
Features
- Real-time analysis of electricity and water consumption data using existing smart meters
- AI-powered anomaly detection to identify potential emergencies such as falls, inactivity, or water leaks
- Automated alerts to designated contacts (family, caregivers, emergency services) based on detected anomalies
- Non-intrusive monitoring without the need for cameras or additional sensors, preserving privacy
- Machine learning algorithms that adapt to individual household consumption patterns for increased accuracy
- Integration with existing emergency response systems and caregiver networks
- Dashboard for monitoring consumption patterns and alert history