Knotch has introduced ACE, an AI-powered layer for Zillow Rentals’ B2B platform. The tool aims to deliver tailored user experiences and deeper customer insights for rental managers.
Rental managers using Zillow Rentals’ B2B platform are now seeing a new level of personalization, thanks to the launch of ACE, an AI-driven layer developed by Knotch. The tool is designed to help property managers—from large enterprises to individuals with a few investment properties—better engage with the platform and receive more relevant content and services.
ACE integrates directly with a brand’s existing technology stack, using natural language prompts and analysis of historical data, brand guidelines, and compliance rules to tailor the user experience. According to Anda Gansca, CEO and co-founder of Knotch, today’s users expect technology to adapt to their needs, and ACE is built to meet those expectations by automating and customizing the customer journey.
Ben Levine, head of marketing for Zillow Rentals, reported that ACE has been in beta for about three weeks and is already helping Zillow understand its customers in greater detail. By prompting users to share specific information about their background and service needs, the platform can deliver more targeted content and keep users engaged for longer periods. Levine noted that the immediate bounce rate has been increasing, indicating users are spending more time on the site.
ACE is powered by Google’s Vertex and Gemini AI models and is supported by Knotch’s intelligence platform, Knotch One. This system determines the best way to target users by leveraging data from the brand’s customer data platform, previous interactions, SEO and GEO partners, and Knotch’s own data partner, Conductor. When users arrive on the site, ACE asks questions such as how many properties they own and allows them to seek advice on issues like handling non-paying renters. The AI then assembles responses from pre-approved Zillow content, ensuring compliance and accuracy.
The technology uses a combination of agentic and generative AI tools to break down brand content into semantically tagged building blocks. When a user asks a question, the system dynamically reassembles these blocks to provide a personalized answer, all within the boundaries of brand-approved information. Gansca emphasized that this closed feedback loop allows users to control their experience while maintaining strict adherence to brand standards.
Levine explained that while customer data has long been available, the real innovation is ACE’s ability to act on that data at an individual level. Previously, Zillow optimized for property managers at scale, but lacked the tools to personalize experiences for each user. Now, B2B customers are prompted with optional questions about their business size and role, enabling the platform to recommend products and content tailored to their needs, such as tools for boosting listing visibility or syndicating listings to social channels.
Gansca observed that large language models have conditioned users to expect dynamic, conversational experiences online. Instead of learning the “language of the internet” through keywords, users now expect platforms to understand and respond to their needs in natural language. This shift is driving platforms like Zillow to adopt AI solutions that bring the internet to the user, rather than the other way around.
Other publishers are also experimenting with AI to improve user engagement. For example, India Today has tested a predictive AI tool to forecast audience engagement before publishing, as detailed in this recent report on AI-driven editorial strategies.
