Telemety offers a predictive robotic intelligence platform that fuses omni‑sensor data with geospatial mapping and machine‑learning models to enable autonomous navigation in dense urban environments. The system provides full‑stack motion planning, safety‑policy compliance, and fleet management for robots used in last‑mile delivery, emergency response, and municipal maintenance.
Funding
Funding not disclosed
Founders
Product
Problem
Robotic systems for urban tasks often lack reliable perception and decision‑making capabilities needed to navigate dense, dynamic city environments, limiting their use in last‑mile delivery, emergency response, and municipal maintenance.
Solution
Telemety provides a predictive robotic intelligence platform that fuses omni‑sensor data with geospatial awareness and machine‑learning models trained on extensive real‑world ground‑truth datasets. The system delivers full‑stack control, motion planning, and safety‑policy compliance for both large fleets and individual micro‑robots. By integrating exteroceptive and proprioceptive sensing with a “Sentient Engine” module, robots can anticipate obstacles, adapt routes in real time, and execute complex urban mobility tasks safely and autonomously. The platform supports deployment across logistics, rescue, and public‑service applications, enabling robots to operate reliably in crowded, unstructured city settings.
Target Audience
Primary customers are companies and agencies deploying autonomous robots for last‑mile logistics, emergency rescue, and urban maintenance services in densely populated city environments.
Features
- Omni‑sensor fusion combining vision, audio, touch, and proprioception for comprehensive environment perception
- Geospatial mapping and physics‑based sidewalk navigation models tailored to dense urban terrains
- Predictive AI motion planning trained on over two years of real‑world data (10K+ miles of walking and delivery experiences)
- Safety‑policy engine that enforces human‑first interaction rules and industry best practices for customer service and rescue operations
- Macro‑fleet and micro‑robot management layers for coordinated multi‑robot teamwork and individual robot autonomy
- Real‑time route optimization and adaptive behavior adjustment through continuous machine‑learning inference