Funding
Funding not disclosed
Founders
Product
Problem
Traditional indoor incident detection systems often rely on visual sensors that raise privacy concerns or are susceptible to environmental factors like poor lighting or smoke. This limits their effectiveness and adoption in sensitive environments where privacy is paramount.
Solution
AIlicorn offers a privacy-preserving, lens-free sensing technology that utilizes millimeter-wave radar and AI to detect indoor incidents such as falls. The system analyzes spatial data to accurately identify accidents without capturing any visual imagery, ensuring user privacy. Its robust design is resistant to environmental interference, making it suitable for various applications. The platform provides an API for seamless integration into existing enterprise systems, enabling flexible deployment of advanced spatial awareness capabilities.
Target Audience
AIlicorn targets enterprises requiring reliable and privacy-compliant indoor incident detection, including smart building developers, healthcare facilities, and retail environments.
Features
- Lens-free millimeter-wave radar sensing technology for privacy-preserving spatial data capture.
- AI-powered algorithms for accurate detection of indoor incidents, including falls.
- Robust performance unaffected by lighting conditions, smoke, or other environmental factors.
- Spatial data analysis to identify potential health concerns through posture and dwell time metrics.
- Secure data handling with encryption from sensor to end-user.
- Open API for flexible integration with enterprise platforms and third-party applications.
- Modules for both fall detection (FD1) and gesture recognition (GR1).