Treeswift uses robotics and machine learning to optimize vegetation and asset management for utilities along transmission and distribution corridors. The platform combines high-resolution ground measurements with computer vision to create a digital twin of field conditions. This actionable data allows utilities to cost-effectively prevent outages and optimize vegetation management spending for improved reliability.
Funding
$15.6M 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.
PV+1Founders
Product
Problem
Utilities face challenges in maintaining vegetation near power lines, leading to outages, increased operational costs, and potential safety hazards. Traditional methods of vegetation management are often reactive, labor-intensive, and lack the precision needed for effective risk mitigation.
Solution
Treeswift offers an AI-powered platform for vegetation management, leveraging vehicle-mounted sensors and LiDAR data to create a digital twin of utility infrastructure corridors. This digital twin enables precise monitoring of vegetation encroachment, identification of potential failure risks, and optimization of maintenance strategies. By providing actionable insights into field conditions, Treeswift helps utilities proactively prevent outages, reduce operational expenses, and improve the reliability of their transmission and distribution networks. The platform integrates seamlessly with existing GIS and work planning systems, ensuring data is readily available for informed decision-making.
Target Audience
Treeswift primarily serves utility companies responsible for managing vegetation along transmission and distribution corridors, as well as organizations involved in renewable energy infrastructure development.
Features
- High-resolution data collection via vehicle or on-foot patrols, adaptable to diverse environments from urban to remote locations.
- LiDAR-based digital twin creation for accurate representation of vegetation and infrastructure.
- AI-driven analytics to identify encroachment risks and predict potential failures.
- Integration with existing GIS and work planning systems for streamlined data accessibility.
- Emergency preparedness and response tools to optimize pre- and post-storm operations.
- Scalable data collection methods that integrate into existing utility workflows.