Municipalities and infrastructure contractors often rely on manual, labor‑intensive inspections to assess the condition of streetlights, road markings, traffic signs, and related assets. These processes are time‑consuming, prone to human error, and provide limited spatial accuracy, resulting in delayed maintenance and higher lifecycle costs. The lack of a scalable, data‑driven inspection method hampers proactive asset management and safety compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Municipalities and infrastructure contractors often rely on manual, labor‑intensive inspections to assess the condition of streetlights, road markings, traffic signs, and related assets. These processes are time‑consuming, prone to human error, and provide limited spatial accuracy, resulting in delayed maintenance and higher lifecycle costs. The lack of a scalable, data‑driven inspection method hampers proactive asset management and safety compliance.
Solution
Velotech AI delivers AI‑enhanced inspection services that capture high‑resolution stereo imagery of public infrastructure and process it on embedded edge devices. Custom computer‑vision models automatically locate assets, evaluate their condition, and classify damage types such as dents, graffiti, fading, misalignment, or missing components. The resulting georeferenced asset inventory and condition reports are delivered through a secure cloud platform, enabling municipalities and contractors to prioritize repairs, schedule maintenance, and track asset health over time. By eliminating the need for on‑site personnel for routine surveys, the solution reduces inspection costs and accelerates decision‑making while maintaining regulatory compliance.
Target Audience
Primary customers are municipal public‑works departments and city infrastructure managers, as well as private contractors responsible for the upkeep of street lighting, road markings, traffic signage, and enforcement installations.
Features
- Stereo camera rigs mounted on mobile platforms capture synchronized left‑right images for precise 3D reconstruction of assets.
- Edge computing module runs inference locally, minimizing bandwidth usage and providing near‑real‑time results.
- Proprietary AI models detect and classify streetlights, road markings, traffic signs, and enforcement equipment, identifying specific defect categories (e.g., dents, discoloration, graffiti, rotation errors).
- Automated generation of GIS‑compatible asset layers with exact coordinates, condition scores, and damage annotations.
- Cloud‑hosted dashboard offers interactive visualizations, trend analytics, and exportable maintenance work orders.
- RESTful API enables integration with existing asset‑management or ERP systems for seamless workflow automation.
- End‑to‑end encryption and role‑based access controls ensure data security and compliance with public‑sector regulations.