Extend AI offers a spatial AI platform that generates real-time 3D digital twins and objective statistical models from live sensor data, enabling businesses to visualize and analyze their physical operations. This technology identifies inefficiencies and automates compliance verification, enhancing operational performance and decision-making.
Funding
$1.9M 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
Many businesses lack comprehensive visibility into their physical operations, leading to inefficiencies, compliance issues, and missed opportunities for optimization. Traditional methods of monitoring and analysis are often manual, subjective, and unable to provide real-time insights. This lack of objective data hinders effective decision-making and process improvement.
Solution
Hypern OS is a spatial AI platform that creates real-time 3D digital twins and objective statistical models from live sensor data, providing businesses with unprecedented visibility into their physical operations. By connecting to existing cameras or deploying Hypern-ready cameras, the system captures large spaces over time and generates a dynamic 3D model of the environment. The platform identifies events occurring in the environment, classifies tasks, and builds objective statistical models, offering detailed insights into physical processes. This enables businesses to bridge the gap between the physical and digital worlds, identify inefficiencies, automate compliance verification, and simulate alternative scenarios for optimization.
Target Audience
The primary target audience includes businesses in industries such as manufacturing, logistics, and healthcare seeking to optimize their physical operations, automate compliance, and improve decision-making through spatial AI.
Features
- Real-time 3D modeling from live camera feeds
- Dynamic environmental monitoring capturing changes over time
- Identification and classification of events occurring in the physical space
- Generation of objective statistical models of physical operations
- Spatial event rule engine linking physical events to digital actions via API calls
- AI-driven agents providing actionable insights tailored to specific needs
- Predictive modeling to anticipate future scenarios
- Simulation capabilities to validate efficiency gains through interactive scenarios