Public-Safety Event Detection AI
Xylo-Guard
Overview
Xylo-Guard is a Physical AI solution that detects safety events across large, complex public infrastructure and supports field response.
It analyzes robot, drone, CCTV, location, and environmental data together and selects risk-event candidates at the edge.
Field challenges
Public facilities and disaster-monitoring areas are too large for staff to monitor every zone continuously. Unreliable connectivity limits immediate assessment and response through remote control systems.
How it works
The system combines robot, drone, CCTV, and field-sensor data with location and environmental context. It selects fire, distress, intrusion, and anomaly candidates at the edge, then sends events into control and response workflows.
- Signals
- Video, location, environment, robot runs, flight missions, and field-event logs
- Deployment
- Field edge inference with robot/drone/CCTV integration and control-event delivery
Key capabilities
- Robot, drone, CCTV, and sensor data collection
- Location and environmental-context analysis
- Fire, distress, intrusion, and anomaly candidates
- Edge event selection for constrained networks
- Public-infrastructure control-system integration
- Integration with field response and follow-up actions
Deployment goals
- Safety-event candidate identification
- Narrower control-room review scope
- Field-response integration
Frequently asked questions
- What equipment can it connect to?
- We review available robots, drones, CCTV, and location/environmental sensors, then define the collection and control interfaces.
- Can it work where connectivity is unreliable?
- We define which events to process locally and which data to transmit centrally, then configure the edge system for site connectivity.