At its core, Plush functions like a conversational stylist. Users type in a natural description of what they want—no need to fuss with checkboxes or dropdown menus—and the system parses the query for context, color preferences, fabric types, price range, and even occasion. Plush returns results that feel remarkably attuned to the request, thanks to a combination of visual search, LLM-driven language parsing, and fine-tuned recommendation models. The interface is designed to be fluid and chat-like, allowing users to refine their search with follow-up prompts like “make it more summery” or “show me options with sleeves.” Over time, the system gets smarter, picking up on recurring preferences and style cues to deliver an increasingly personal experience. In many ways, Plush collapses the need for both search and curation by blending them into a single, intuitive interaction that feels less like shopping and more like a conversation with someone who knows your taste.