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AI Engineering
OpenAI Blog·Dec 9·5 min read
How Scout24 is building the next generation of real-estate search with AI
TL;DR
Real-estate search traditionally forces users to manually filter listings by criteria, creating friction and incomplete results. Scout24 built a GPT-5 powered conversational assistant that asks clarifying questions, synthesizes results, and recommends tailored listings through dialogue.
✦ Why It Matters
Engineers can apply conversational AI to traditionally form-based search interfaces to improve user intent capture and discovery outcomes.
Key Takeaways
- Replace static filter-based search with multi-turn dialogue to capture implicit user preferences.
- Use large language models to synthesize user intent from natural language rather than structured form inputs.
- Generate contextual summaries and recommendations within conversation flow to reduce cognitive load.
- Test conversational search patterns in verticals where user intent is complex and multi-dimensional.
Full Summary
Real-estate marketplaces typically rely on keyword search and faceted filters, requiring users to articulate preferences upfront and manually review results—a process that often misses relevant properties. Scout24 developed a conversational AI assistant powered by GPT-5, a large language model trained on diverse text, that engages users in natural dialogue to refine search intent.
The assistant asks clarifying questions about budget, location, amenities, and lifestyle preferences, then generates personalized listing recommendations and summaries. By treating real-estate search as a dialogue rather than a form-filling exercise, the system captures implicit preferences and context that static filters miss.
The approach leverages GPT-5's ability to understand nuanced language and maintain conversation state across multiple turns. This conversational paradigm enables Scout24 to reduce user friction, improve listing relevance, and increase engagement with the platform.