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About this article

• Audience: Mass transit directors, public safety procurement officers, and system integrators
• Goal: Provide a structured, production-ready RFP template for evaluating and acquiring sovereign edge AI systems
• Type: Reference

Summary

This template details technical specifications, hardware baselines, and data-sovereignty requirements for procuring edge-native AI video understanding solutions. Use this reference to compile procurement requests that guarantee local real-time processing, strict privacy protection, and robust off-grid capabilities.

Prerequisites

• Existing network infrastructure diagram for target transit vehicles or facilities
• Defined agency compliance standards (such as CJIS, NDAA, or local data privacy laws)
• Baseline count of integrated camera feeds (ONVIF or local RTSP sources)

System overview and scope

The procuring agency requires a sovereign, edge-native artificial intelligence system to perform real-time video understanding. The system must process video streams locally on the edge, minimizing bandwidth consumption and eliminating reliance on continuous cloud connectivity. Vendors must deliver a solution that ensures data sovereignty, meaning all video streams and metadata remain within the agency's physical or cryptographically controlled boundaries.

Key capabilities must include:

• Local object detection and spatial reasoning on transit platforms and public areas
• Edge-native video storage with secure, event-driven retrieval
• Cryptographically verified over-the-air (OTA) updates for models and firmware
• Integration with existing Video Management Systems (VMS) via ONVIF and RTSP

Technical specifications

Vendors must meet the following hardware and software parameters to ensure performance and reliability under operational conditions:

Edge processing hardware

• AI acceleration: The system must utilize dedicated local hardware accelerators (such as Intel Myriad X, Jetson Orin NX, or AGX) capable of executing deep neural networks at a minimum of 15 frames per second (FPS) per channel.
• Form factor: Hardware must conform to ruggedized industrial standards, supporting passive cooling, vibration resistance for moving vehicles, and operating temperatures from -20°C to 60°C.
• Power efficiency: Edge nodes must operate within a maximum power budget of 25W under peak inference loads.

Inference and model performance

• Object detection: The local models must detect and classify transit-related events (such as platform intrusions, unattended baggage, and crowd density) with a precision of at least 92% and a recall of 88% under varied lighting.
• Spatial AI: The system must support depth-aware spatial tracking to determine object coordinates and trajectories relative to the sensor.
• Local processing gate: The system must complete frame capture, pre-processing, inference, and metadata packaging within a maximum latency budget of 100ms.

Security and data sovereignty

Given the sensitive nature of public safety and transit telemetry, the system must enforce strict security boundaries.

Local storage and transmission

• Data isolation: Video footage and high-resolution frames must reside locally on edge nodes in an encrypted partition. No raw video may stream to external servers unless specifically requested by an authorized operator.
• Metadata packaging: Edge nodes must package and send only lightweight, structured JSON metadata (containing event tags, timestamps, and bounding boxes) over the network.
• Transport security: All transit communication between edge nodes, local proxies, and central management servers must use TLS 1.3 and cryptographically signed MQTT payloads.

Cryptographic verification

• Firmware integrity: The edge device must verify firmware signatures at boot. The system must reject unsigned or altered binaries.
• Telemetry sanitization: The system must redact private network configurations, home directories, and device passwords from all public-facing log files and external telemetry.

Verification and testing

Vendors must undergo a multi-phase verification process prior to full production deployment:

1. Shadow deployment phase: The vendor must run a non-intrusive shadow deployment (such as a canary run) alongside existing systems to verify pipeline stability and capture accuracy.
2. Soak testing gate: The edge node must run continuously for at least 72 hours under peak operational load without memory leaks, process restarts, or thermal throttling. Average CPU/GPU temperature must remain below the hardware limit under continuous execution.
3. API and telemetry verification: The operator must verify system health, registry membership, and latency metrics using standardized local endpoints (such as /fleet/health and /fleet/mqtt/latest).

Next steps

• Quickstart — Run your first shadow deployment to verify edge node communication
• API quick reference — Learn how to query edge node health and telemetry via the proxy
• Firmware deploy — Review the OTA model and firmware deployment pipeline

Operator depth

Procurement evaluation metrics, financial scoring algorithms, and sample hardware bill of materials (BOM) live in the private operator handoff (not published) (private).