Ctrl provides fleet-wide asset management solutions that utilize remote sensing, geo-spatial data, and inspection analytics to monitor asset conditions in real-time. This technology reduces maintenance costs and enhances asset utilization for businesses in energy and infrastructure sectors.
Funding
Funding not disclosed
Founders
Product
Problem
Energy and infrastructure companies face challenges in effectively monitoring and managing geographically dispersed assets, leading to increased maintenance costs and suboptimal asset utilization. Traditional inspection methods are often time-consuming, costly, and provide infrequent data updates, hindering real-time decision-making.
Solution
Ctrl provides a fleet-wide asset management platform that leverages remote sensing, geospatial data, and advanced analytics to deliver real-time insights into asset conditions. Integrating data from satellites, drones, LiDAR, and thermal sensors, Ctrl's platform creates 2D/3D models, detects defects like cracks and corrosion, analyzes vegetation encroachment, and maps predictive risks. The Asset platform uses a custom data fusion engine to align multi-sensor inputs, providing a comprehensive view of infrastructure health. This enables clients to optimize resource allocation, reduce operational risks, and improve the lifecycle management of critical assets.
Target Audience
Ctrl's primary customers are energy operators, public safety agencies, and infrastructure companies responsible for managing and maintaining large-scale assets, such as power grids, pipelines, and transportation networks.
Features
- Multi-sensor data fusion, integrating satellite, drone, LiDAR, and thermal imagery
- AI-powered defect detection for identifying cracks, corrosion, vegetation encroachment, and sag analysis
- 2D/3D modeling of assets for visualization and analysis
- Predictive risk mapping to anticipate potential failures and prioritize maintenance
- Autonomous drone flight capabilities with remote deployment from drone docks
- GIS-native outputs compatible with ArcGIS and other enterprise systems
- Edge processing with NVIDIA Jetson for real-time data analysis at the source
- Cloud-based CUDA-powered pipelines for rapid data processing and analysis