Autonomous Security
Starts With Detection

RAD’s AI analytics engine helps your team to cut through the noise, verify threats instantly, and respond with confidence through clear, actionable insights.

RAD's AI analytics adapt to different environments and scenarios, providing accurate detections across a wide range of real-world security challenges.
Human Detection
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Detects the presence and movement of people in restricted or monitored areas. Enables accurate perimeter alerts, crowd monitoring, and occupancy awareness across facilities. Use cases: Perimeter intrusion, loitering, access control, crowd detection
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Vehicle Detection
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Recognizes vehicles entering, exiting, or stopping within secured zones. Supports license plate recognition and situational awareness for gates, parking, and patrol routes. Use cases:Entry/exit verification, parking lot monitoring, tailgating detection
Object Detection
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Identifies items such as firearms, PPE, or unattended packages in view. Adds early awareness to prevent escalation and enhance safety compliance in critical areas.Use cases:Firearm detection, PPE compliance, unattended object alerts
Here’s how it works
Multi-Layered Detection Intelligence
RAD’s AI analytics operate in three layers: object, behavior, and validation. Each layer ensures detections are accurate, contextual, and ready for response.
Layer 1
Recognition
Finds people, vehicles, firearms, plates, PPE, and more. Filters motion, glare, and shadows to reduce false alerts from the start.
Layer 2
Analysis
Understands what is happening: loitering, tailgating, perimeter intrusion, unsafe behavior. Adds context so operators see the why, not just the what.
Layer 3
Validation
Each detection is verified in real time to confirm accuracy. Devices with audio and visual deterrence can autonomously issue warnings to prevent unwanted behavior.

At The Edge

Accelerated by NVIDIA GPU technology, RAD’s AI analytics deliver real-time threat detection, classification, and response across thousands of connected devices.

Inside ROSA™ and RIO™, NVIDIA processing runs AI analytics directly at the edge, enabling instant detection and autonomous deterrence where it matters most. From single-site to enterprise scale, NVIDIA’s architecture integrates seamlessly with RAD systems to meet growing security demands without losing speed or accuracy.

Edge-to-Cloud Processing

RAD analytics run across both edge and cloud environments. This hybrid design delivers instant, on-device detection with the added intelligence and validation of cloud processing.
Edge
Processing
Performs instant on-device threat detection through local AI processing for continuous awareness
Ideal for remote or high-security sites that require autonomous operation even without network connectivity
Executes preliminary threat detection and classification directly at the device
Zero latency ensures instant awareness at the point of capture
Cloud
Processing
Expands computational power for advanced model training, verification, and pattern analysis
Continuously enhances recognition accuracy across all deployed RAD devices through centralized learning
Delivers more precise behavioral analytics and situational awareness through large-scale data modeling
Enables cross-site learning, historical pattern comparison, and global update distribution across the ecosystem
Hybrid
Processing
Combines the instant response of edge detection with the analytical depth of cloud-based validation
Dual-layer verification ensures every detection is confirmed before escalation, reducing false positives
Proprietary algorithms balance accuracy, scalability, and performance across varied environments
Provides superior reliability by merging local speed with cloud-scale intelligence and oversight
Detection is only the first step. RAD’s AI analytics connect edge devices, cloud intelligence, and Agentic AI orchestration into one continuous security workflow.

Detection To Resolution

AI Detection. Edge Deterrence. Agentic AI Orchestration.