Edge AI deployments received optimizations that reduce inference latency while preserving accuracy on constrained devices. Improvements span model architecture, quantization strategies, and pipeline scheduling to better use available compute and memory. The team benchmarked scenarios typical of industrial and retail vision workloads, and validated results under changing lighting, occlusions, and motion. Observability was improved with lightweight telemetry and reproducible test clips so regressions can be detected early. The program’s philosophy is pragmatic: maximize benefits on the hardware customers have today, provide clear guidance on when to upgrade, and keep safety and privacy considerations central when deploying in sensitive environments. Detailed notes accompany the release to help teams replicate the setup and adapt it to their use cases.