Cyberwave provides a unified Physical AI platform that abstracts any robot, drone, arm, or sensor behind a single Python SDK and API, allowing developers to write code once and run it on any supported hardware. The platform offers digital twins for simulation‑first development, an edge runtime for low‑latency AI inference, and integrated fleet management features such as OTA updates, telemetry, and safety enforcement.
Funding
$8.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developers and robotics teams face fragmented tooling, vendor‑specific drivers, and a disconnect between simulation and real‑world deployment, leading to costly rewrites, long integration cycles, and limited scalability of robot fleets.
Solution
Cyberwave offers a unified Physical AI platform that abstracts any robot, drone, arm, or sensor behind a single Python SDK and API. Users select a digital twin from a catalog, develop and test code in a physics‑accurate simulation, then deploy the same logic to any supported hardware without code changes. The platform’s edge runtime runs AI inference locally, ensuring low‑latency decisions and offline operation while providing centralized model lifecycle management and safety enforcement. Real‑time telemetry, OTA updates, and fleet orchestration enable production‑grade operations from a single console.
Target Audience
Primary customers are robotics developers, AI/ML engineers, and operations teams building or managing fleets of autonomous robots, drones, and industrial arms across manufacturing, logistics, energy, and inspection sectors.
Features
- One‑API hardware abstraction layer that automatically maps capabilities (locomotion, manipulation, perception, flight) to the appropriate twin subclass
- Live digital twins that synchronize bidirectionally with physical assets, supporting simulation‑first development and seamless code reuse
- Edge AI runtime with containerized drivers, deterministic inference, hot model swapping, and offline resilience on supported hardware (Jetson, Intel NUC, Raspberry Pi, etc.)
- Integrated fleet management: OTA updates, rollback, telemetry streaming via MQTT/WebRTC, and policy‑driven safety enforcement
- Unified messaging namespace for telemetry, commands, events, and video streams, enabling consistent monitoring and control across heterogeneous devices
- Plug‑and‑play driver framework (ROS 2 bridge, custom BaseEdgeNode) that eliminates custom driver development for new robots
- Secure, signed model deployments with audit logging and role‑based access control for enterprise governance