Sensorita utilizes radar and machine learning algorithms to create digital twins of waste containers, providing real-time data on fill levels and locations. This technology addresses the issue of poor asset management in waste management companies, enabling proactive planning and efficient resource allocation.
Funding
$3.3M 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
Waste management companies often lack real-time visibility into the fill levels and locations of their waste containers, leading to inefficient resource allocation and reactive rather than proactive planning. This lack of insight results in wasted resources on unnecessary container counting, inefficient purchasing, and increased customer support inquiries.
Solution
Sensorita provides a solution by creating digital twins of waste containers using radar and machine learning algorithms, offering real-time data on fill levels and locations. This technology enables waste management companies to gain a comprehensive overview of their container network, facilitating proactive planning and optimized resource allocation. By tracking and monitoring containers in real-time, Sensorita empowers sales, dispatchers, and customer service teams to provide better solutions and offer customers dedicated dashboards displaying current container status.
Target Audience
Sensorita primarily targets waste management companies seeking to improve asset management, optimize resource allocation, and enhance customer service through real-time container monitoring.
Features
- Radar-based sensor technology for measuring fill levels in open waste containers.
- Machine learning algorithms to create and maintain accurate digital twins of waste containers.
- Real-time tracking of container fill levels and locations.
- Customer dashboards providing visibility into container status.
- Data-driven insights for optimized logistics and production planning.