
Aerial Insights
Asset management software powered by drone data and machine vision our software solutions are the future of drone inspection.
Founded in 2015 by Harrison Knoll and based in St. Louis, United States, Aerial Insights operates as a provider of AI-based drone software for aerial inspections. The company’s technology is designed for industry and utility inspections, leveraging drones equipped with advanced sensors and thermal imaging to serve the construction and maintenance sectors of critical infrastructure. The platform's features include utility inspection and detection, as well as the classification and organization of inspection data and ensuring asset security.
Aerial Insights' services find applications in monitoring the structural condition of infrastructure, such as wooden-H frames, and insulator inspections. The technology aims to provide cost and time savings for infrastructure companies by enabling predictive maintenance, for instance, by identifying potential issues like cracks in roadways caused by erosion. The use of drones offers safer and more accessible damage assessments, particularly in the wake of natural disasters, by allowing operators to remain on the ground. The company processes raw sensor data collected from project sites into spatially accurate, high-resolution visual models. Clients can then use these models to perform survey measurements from their desktops.
The company's offerings are relevant to a range of clients, including real estate firms, construction companies, insurance companies, and emergency services. While the company had not publicly disclosed any funding rounds, it was noted as being deadpooled. The competitive landscape includes companies such as Airspace Link, SkyGrid, and AirMap.
Keywords: aerial inspection, drone software, utility inspection, infrastructure maintenance, asset security, predictive maintenance, damage assessment, visual data models, construction technology, drone services, AI inspection, thermal imaging, data classification, remote sensing, geospatial data