Retrieval Augmented Generation for Retail: Solving 3D

We built a retail AI system that could analyze a room from a single photo, infer layout and depth, retrieve relevant furniture from live catalogs, and render replacement pieces back into the scene with believable scale, perspective, and lighting. To make that possible, we combined computer vision, monocular depth estimation, multimodal retrieval, and custom 3D generation under a very aggressive timeline. The result gave Palazzo a practical RAG retail system for AI product discovery, 3D scene understanding AI, and photorealistic, shoppable room transformations. We've also built this as a serious retrieval and spatial-computing problem, combining RAG-style retrieval, computer vision, and 3D scene understanding to make product discovery feel natural, visual, and commercially useful.