What We Learned Building Conversational Real Estate Search in France

What We Learned Building Conversational Real Estate Search in France At Kazaki , we replaced the classic filter-based real estate search with a chat interface. You describe what you want in plain language, the platform finds matching properties. Simple premise. Harder to get right than it looks. The real problem isn't search — it's intent Traditional platforms make users think in database terms: pick a city, set a price range, choose a surface area. But people don't think about buying a home that way. Natural language carries signals that don't map to columns: "somewhere quiet" , "good schools nearby" , "charming but not too much work" . Before you can search anything, you need to understand what the user actually means — and that changes with every message in the conversation. We spent more time on intent extraction than on the search layer itself. Getting a clean, structured representation of what the user wants, from a messy conversational context, is the real bottleneck. Hybrid sea