SCANNA Camera Onboarding Automation

SCANNA simplifies the process of connecting third-party IP cameras and NVR systems to the RAD platform through guided discovery, stream validation, live video preview, and onboarding preparation.

AVA Unit with RADGuard
From Camera Discovery To Deployment Readiness
SCANNA helps teams discover, validate, preview, and prepare third-party IP cameras and NVR channels for onboarding into the RAD ecosystem.
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Automated Discovery

Find Cameras And NVR Channels Faster

SCANNA scans local networks or public IP addresses to identify available IP cameras and NVR channels. This helps deployment teams reduce manual discovery work and move faster during initial setup.

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Stream Validation

Confirm Readiness Before Onboarding

SCANNA verifies connectivity and stream availability before cameras are added to the platform. Teams can identify access, configuration, or stream issues earlier in the deployment process.

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Live Video Preview

Validate Camera Feeds Before Activation

SCANNA allows teams to preview live video feeds before devices are selected for onboarding. That creates a cleaner handoff from setup to activation and gives teams greater confidence that cameras are ready for use inside the RAD ecosystem.

Visitor Registration Screen
Entry Interaction Screen
Access Authorization Screen

Built For Faster Deployment

Camera Onboarding

  • Network Scan
    Local network or public IP scan. Finds available IP cameras.
  • NVR Channels
    Identifies supported NVR streams.
  • Stream Validation
    Checks configured and availability.
  • Live Preview
    Confirms video before selection.

Deployment Value

  • Faster Setup
    Reduces manual onboarding time.
  • Fewer Errors
    Catches issues before handoff.
  • Guided Workflow
    Standardizes setup steps.
  • Less Troubleshooting
    Reduces support escalation.
  • Cleaner Handoff
    Moves cameras to client activation.

Team Support

  • Sales Engineering
    Supports pre-deployment validation.
  • Deployment Teams
    Creates repeatable onboarding.
  • Technical Support
    Shortens troubleshooting cycles.
  • Operations
    Improves multi-site consistency.
  • RAD Ecosystem
    Prepares cameras for platform use.
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Robotic Assistance Devices, Inc. (RAD), a wholly owned subsidiary of Artificial Intelligence Technology Solutions, Inc. (OTCID:AITX), develops autonomous security solutions that help organizations move incidents from detection to resolution. RAD combines AI analytics, edge-based deterrence, and agentic AI orchestration to verify threats, initiate response, and document incidents in real time. RAD’s ecosystem of hardware and software is built for complex security environments where speed, consistency, and operational visibility matter. By unifying detection, deterrence, escalation, and incident response orchestration, RAD helps security teams improve outcomes, strengthen coverage, and reduce dependence on reactive, labor-intensive models.

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
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Zero latency ensures instant awareness at the point of capture
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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