Retail RAG and 3D Furniture Visualization Platform
Dreamers built a retail AI workflow that combined retrieval, recommendation, and 3D spatial understanding for furniture visualization. The system helped turn room imagery and product catalogs into realistic replacement concepts for commerce users.
Challenge
The client needed more than a standard product recommender. The workflow had to understand a room from imagery, reason about scale and layout, retrieve relevant furniture candidates, and produce believable visual replacements without turning the experience into a slow research prototype.
Solution
Dreamers designed a pipeline around monocular depth estimation, object classification and masking, pose and scale estimation, catalog retrieval, and 2D-to-3D asset handling. The team also handled hosting, integration, and performance work so the AI workflow could support a practical user experience.
Results
The project produced a working neural furniture recommender and visualization workflow. A key orientation and scale step was reduced from roughly 300 seconds to roughly 10 seconds, making the experience far more usable for interactive retail exploration.
Technical Approach
The build combined monocular depth estimation, object classification and masking, pose and scale estimation, catalog retrieval, and 2D-to-3D asset handling so product recommendations could respect a real room rather than a flat keyword query.
Commerce Context
This was a retail AI and spatial-commerce workflow, not a generic chatbot. The system had to connect retrieval, recommendation, 3D scene understanding, and believable room visualization inside a usable product experience.
Technologies & Tools
They were highly responsive and always worked hard to get us what we needed.
Anonymous, CEO, AI Furniture Visualization Company
