About
Cyberwave is the infrastructure layer for Physical AI—a developer-first platform that abstracts the complexity of real-world robotics so teams can focus on building logic, not plumbing. Every robot, drone, sensor, and arm in the platform's catalog is represented as a digital twin: a live, programmable representation that developers can code against, simulate, and then deploy to actual hardware with a single SDK. The platform is built around several core pillars: Hardware Abstraction provides one unified API surface across any supported device; Digital Twins serve as the canonical state fabric capturing what exists, where it is, and what it's doing; Edge Runtime enables distributed, low-latency compute at the point of action with connectivity resilience; and Teleoperation gives human operators a single interface with a full audit trail for any connected machine. Cyberwave also includes a Policy, Safety & Governance layer for enterprise-grade deployment, a composable Workflow Engine for cross-robot and cross-system automation, and an AI Models runtime supporting model-agnostic policy routing, fallback chains, and shadow deployments. Fleet Orchestration handles mission planning and multi-robot coordination at scale. Use cases span autonomous infrastructure inspection, pipeline integrity monitoring, predictive aircraft maintenance, solar farm inspection, warehouse inventory scanning, runway monitoring, and construction site progress tracking. The platform targets robotics engineers, AI/ML engineers, software developers, and enterprise IT and operations leaders.
Key Features
- Hardware Abstraction Layer: A single API surface that works across any robot, drone, sensor, or robotic arm—write once, run on any supported hardware without vendor lock-in.
- Digital Twin Catalog: Every device is represented as a live, programmable digital twin. Browse the catalog, pick a twin, write your logic, and deploy to real hardware seamlessly.
- Edge AI Runtime: Distributed compute runs at the point of action with local execution, real-time command handling, and connectivity resilience for reliable field deployments.
- Fleet Orchestration & Workflow Engine: Coordinate multi-robot missions with composable automation workflows across robots, sensors, and external systems—complete with policy-gated actions.
- Safety, Policy & Governance: Enterprise-grade identity for every entity, signed commands, mutual authentication, full audit trails, and configurable governance policies for safe autonomous operations.
Use Cases
- Autonomous inspection of electrical substations, oil pipelines, and industrial assets to reduce human exposure to hazardous environments
- Predictive maintenance and inspection workflows for aircraft fleets to keep them compliant and flight-ready
- Warehouse inventory scanning with autonomous shelf-scanning robots that track stock levels and verify price accuracy in real time
- Construction site progress monitoring using drones that capture daily site state and compare it against BIM models to generate variance reports
- Solar farm inspection with drones and ground robots detecting panel damage, soiling, and thermal hotspots to maximize energy yield
Pros
- Unified abstraction across hardware vendors: Developers build against one consistent API regardless of robot manufacturer, dramatically reducing integration time and eliminating per-device code rewrites.
- Simulation-first development: Digital twins enable full simulation before physical deployment, reducing costly real-world testing cycles and improving safety for high-risk environments.
- Enterprise-ready governance: Built-in safety policies, signed commands, and audit trails make it viable for regulated industries like energy, aviation, and critical infrastructure.
- Broad use-case coverage: Pre-built patterns for inspection, monitoring, logistics, maintenance, and manufacturing mean teams can accelerate time-to-value across multiple industries.
Cons
- Steep learning curve for non-roboticists: Despite hardware abstraction, teams without robotics domain knowledge may still face challenges designing effective autonomous workflows and physical deployment strategies.
- Pricing transparency lacking: Enterprise infrastructure pricing is not publicly listed, making it difficult to evaluate cost fit without engaging the sales team directly.
- Hardware catalog dependency: Full benefits require using robots and sensors listed in the Cyberwave catalog; custom or niche hardware may require additional integration work.
Frequently Asked Questions
What is a digital twin in Cyberwave?
A digital twin is a live, programmable representation of a physical robot, drone, or sensor. It captures the device's current state, location, and history, and lets you write and test logic against it in simulation before deploying to real hardware.
Which robots and devices are supported?
Cyberwave maintains a Digital Twin Catalog that includes quadruped robots (e.g., Unitree Go2), enterprise drones, industrial arms, open-source arms, IP cameras, and more. You can browse the full catalog on the platform.
How do I get started with the Cyberwave SDK?
Install the SDK, pick a digital twin from the catalog, write your logic using Python (import cyberwave as cw), and use the CLI and Cloud Platform to simulate and deploy. Full documentation is available at the Cyberwave Docs portal.
What industries is Cyberwave built for?
Cyberwave targets industries with physical operations that benefit from automation and AI—including energy and utilities, aviation, construction, retail/logistics, agriculture (solar farms), and critical infrastructure inspection.
Is Cyberwave suitable for non-developers like operations teams?
Yes. While the core platform is developer-focused, Cyberwave also provides a Studio interface and Cyberwave App for operations teams to monitor fleets, run teleoperation, and manage workflows without writing code.
