I help industrial teams evaluate AI cameras, RK3588 boards, edge AI gateways and local video analytics solutions with practical PoC checklists, vendor questions, integration reviews and acceptance criteria.
A real edge AI project depends on camera compatibility, local processing, evidence design, integration, network recovery and vendor support.
RTSP stability, camera angle, night video, glare, occlusion and network quality often matter more than a controlled demo.
A PoC needs clear false alarm, missed detection, latency, evidence and acceptance criteria before vendor selection.
Events must connect to VMS, SCADA, MES, IoT platforms or internal systems with clear API, logs and retry behavior.
The goal is not to sell a magic box. The goal is to reduce technical risk before you commit to hardware, vendors, field deployment or integration work.
Turn a vague AI video idea into camera inputs, event definitions, metrics and acceptance criteria.
Review vendor claims, datasheets, demo videos, API documents and PoC plans from a technical buyer perspective.
Check local inference, RTSP, event evidence, callbacks, network failure handling and deployment boundaries.
When appropriate, identify technically suitable hardware/vendor options and disclose commercial relationships if any.
Boards and AI gateways may share the same chipset, but real projects fail for practical reasons: unclear use case, weak documentation, unstable streams, undefined events, poor evidence design or missing support workflow.
Snapshot, short clip, timestamp, rule name, device status and callback behavior should be part of the PoC.