How Scout24 is building the next generation of real-estate search with AI

Scout24 operates Germany’s largest real-estate platform, connecting seekers, homeowners, landlords and agents in one ecosystem. AI has supported areas like fraud detection, marketing efficiency, and property valuation for years, and  the rise of powerful large language models created an opportunity to build something new for customers: an intelligent, conversational real-estate assistant.

We sat down with Gertrud Kolb, Chief Technology Officer at Scout24, to hear how her team built a GPT‑5 powered search experience, what they learned about “intelligent interaction,” and how they balanced innovation with quality and trust before launch.

• GPT‑5 powering Scout24’s conversational search assistant HeyImmo
• Adaptive answer formats: concise summaries, bullet points, or direct listings with image previews
• Custom eval system built on the OpenAI Evals framework to define and measure answer quality
• Company-wide “swarm testing” to refine expectations and stress-test edge cases
• Architecture built for speed and reliability, and rapid improvements
• Tight collaboration with OpenAI Solutions Architects to improve safety, quality, and user experience

Scout24’s first priority was search—the core function of its platform. But as the team began prototyping with OpenAI they realised that customers wanted more than improved results. They wanted guidance. “We saw very quickly it’s not only about search. We needed a real-estate expert assistant—a sparring partner—which guides you through the whole platform experience” said Gertrud.

This insight led to a conversational assistant that can ask clarifying questions, surface relevant listings, summarize options, and adapt the answer format to the user’s needs. Some people want bullet points; others want full summaries; others want listings immediately. Understanding “what intelligent interaction really means” became one of the biggest challenges.

Scout24 also had to find the right technical architecture. Early ideas leaned towards a complex multi-agent framework, but the team intentionally simplified: using function calling and focused components so they could ship quickly and learn from users. Latency, relevance, performance, and the structure of answers became central themes.

One of the most important lessons was understanding quality. To solve this, Scout24 built its own evaluation model inspired by the OpenAI evals framework—helping the team define what “good enough” actually means, and measure it across many scenarios. They combined this with broad internal testing—almost all employees were invited to try the product, provide feedback, and stress-test edge cases. The company delayed launch once to ensure the experience met the bar they had defined.

Throughout development, Scout24 worked closely with the OpenAI team. Gertrud described the collaboration as “immensely helpful” in raising answer quality, shaping response structures, and aligning on what ‘good’ looks like for an AI-powered search experience.

This combination of iterative engineering, rigorous evaluation, and broad testing set the foundation for a customer experience that feels both intelligent and trustworthy.

• Start where AI can transform the core of your product—and iterate from there.
• Quality matters: define “good enough” and build the tools to measure it.
• Internal testing at scale uncovers real user expectations.
• Launch early, learn fast—but know when to slow down.
• A tight partnership between product teams and technical experts accelerates progress.

Scout24’s focus now is on interconnectivity: expanding the real-estate assistant across seekers, landlords, homeowners, and agents. From helping agents produce verified floor plans to giving seekers more personalized guidance, the team sees many opportunities to deepen the experience and strengthen network effects across the marketplace.