Overview
Speco Technologies N16WNRXM24TB 16 Channel Wall Mount Facial Reco
Speco Technologies N16WNRXM24TB Wall Mount Facial Recognition RecorderOverviewThe N16WNRXM24TB is a 16-channel wall-mounted network video recorder wit…
Speco Technologies N16WNRXM24TB 16 Channel Wall Mount Facial Reco
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Bundle Summary
Overview
Speco Technologies N16WNRXM24TB Wall Mount Facial Recognition Recorder
Overview
The N16WNRXM24TB is a 16-channel wall-mounted network video recorder with integrated facial recognition and smart analytics capabilities. Designed for small- to mid-scale deployments, it combines recording functionality with built-in intelligent video analysis to support both security monitoring and operational intelligence workflows. The 24TB storage capacity provides extended retention for 16-channel surveillance operations.
Key Specifications
This recorder supports facial recognition and smart analytics processing. Wall-mounted form factor accommodates installations where floor or rack space is constrained. 24TB onboard storage supports extended recording retention across all 16 channels at configurable quality levels.
Compatibility
Suitable for integrations requiring network-based recording with analytics capability. Compatible with IP-based surveillance systems and ONVIF-compliant camera deployments. Supports standard network infrastructure and VMS platforms that recognize RTSP and ONVIF Profile endpoints.
System Design, Deployment & Technical Support
Support services and planning resources for commercial surveillance, access control, and infrastructure deployments.
Fixed scope • Fixed price
System Design Assistance
- Get help validating product compatibility
- Coverage requirements
- Storage planning and deployment architecture before you buy.
Deployment & Configuration Support
- Access fixed-scope support for rollout planning
- User setup guidance
- Migration and system standardization across single-site or multi-site deployments
Guides, Tools & Calculators
- PoE requirements
- Storage retention
- Camera selection and deployment methodology