Our solution runs proprietary vision-language models (VLM/VLA) optimized for edge devices through techniques such as model tuning, quantization, and hardware-specific compiling. We incorporate chain-of-thought reasoning to improve task accuracy and robustness in dynamic environments. Our system supports multimodal input (video, sensor, language), and uses lightweight architectures combined with on-device learning for continual adaptation. We also integrate task-specific pipelines and edge-optimized inference engines to minimize latency and bandwidth usage. This allows for real-time understanding and autonomous actions without relying on cloud connectivity.