With the advent of technology, especially in computer vision and machine learning, new findings and optimizations are being achieved and published in monthly releases. However, only a few small-scale business owners know about artificial intelligence and its capabilities. Thus, this paper aims to propose and attempt a sample execution of a framework utilizing the state-of-the-art object detection model for a selected food vendor using two main components: an object detection model and a companion application. The YOLO model family, specifically YOLO v9, was chosen for its proven and state-of-the-art performance level in real-time object detection. A dataset of 1356 images was custom-made and annotated for the selected food vendor, which was then utilized for fine-tuning the model. The experimental results with the dataset and the model present promising potential in the practical use of object detection models, specifically YOLO v9, thus potentially making AI more accessible to a broader audience, including small to medium food and beverage businesses.