VeloDX provides edge‑AI software that runs directly on drones, processing sensor streams locally to enable instantaneous autonomous navigation, predictive maintenance alerts, and adaptive mission adjustments without relying on cloud connectivity.
Funding
Funding not disclosed
Founders
Product
Problem
UAV operators often rely on cloud‑based processing for sensor data, which introduces latency, requires constant connectivity, and limits real‑time decision making for autonomous navigation, maintenance, and mission planning.
Solution
VeloDX delivers edge‑AI software that runs directly on drones, processing sensor streams locally to enable instantaneous autonomous navigation, predictive maintenance alerts, and adaptive mission adjustments. By eliminating the need for continuous cloud links, the platform reduces response times and bandwidth costs while maintaining high reliability in remote or contested environments. The AI models are optimized for the limited compute resources of UAV hardware, ensuring efficient operation without sacrificing performance. Integrated with existing flight controllers, VeloDX’s solution provides actionable insights to pilots and fleet managers in real time, enhancing overall flight efficiency and safety.
Target Audience
Primary customers are commercial drone service providers, industrial inspection firms, and enterprises deploying autonomous UAV fleets for logistics, agriculture, or infrastructure monitoring.
Features
- On‑board AI inference engine optimized for low‑power UAV processors
- Real‑time sensor fusion for autonomous navigation and obstacle avoidance
- Predictive maintenance analytics that flag component wear before failure
- Adaptive mission planning that re‑optimizes flight paths based on live data
- Seamless integration with standard flight control systems via API
- Offline operation with optional cloud sync for post‑flight analytics