Abstract: An electronic commerce platform application server comprises an attribute generator application, condition detector application, and a machine learning system. During operation, a device collects sensor data, such as images and/or 3D point clouds, of an item to add to inventory. The attribute generator and condition detector applications receive the sensor data from the device over a network. The attribute generator and condition detector applications process the sensor data using the ML system and predict attributes and condition of the item. In one embodiment, the ML system includes a vision encoder and one or more large language models (LLMs) prompted to generate a description of the item from an image. The ML system also includes one or more neural networks trained to detect condition information, including condition description, bounding boxes identifying defects, high wear regions, and condition classification categories. The ML systems provide scalable and efficient addition of new inventory.