The agent a chatbot surfaces when someone types “best real estate agent near me” into ChatGPT, Perplexity, or Google’s AI Overview is not necessarily the most experienced or the most active in that zip code. That agent may simply have a website built to be readable by a large language model. The gap between “AI-recommended” and “best for you” is real, and growing, as more buyers turn to AI tools early in their home search.
Dave Carter of Lofty, a real estate technology company that builds AI-optimized websites and CRM tools for agents, works on this problem from the agent side. His explanation of how AI search differs from traditional search clarifies why buyers should treat AI-generated agent suggestions differently from conventional search results.
Traditional search engines like Google rank web pages using links, keywords, and hundreds of other signals. AI-powered answer engines work differently. As Carter puts it, “They don’t rank pages the way that Google does.” Instead, these models pull from sources they consider trustworthy and current, then assemble a single synthesized answer. The result feels like a recommendation, but it is a composite built from whatever content the model found easiest to parse.
An agent’s visibility in AI search depends on technical factors most buyers never consider. Carter describes three that matter most: structured data markup that makes a web page easy for a model to read, identity consistency across directories and websites, and content written in full sentences rather than keyword-heavy fragments. When an agent’s contact information differs between their own site and a directory listing, “we see LLMs tend to omit those results,” Carter says. The agent might be excellent – but invisible to AI tools.
Carter says that independent agents and small brokerages are the most likely to be excluded from AI results. “AEO Geo is hard because it requires different signals that most small operators simply don’t have the resources to build and maintain,” he says. These are sometimes the agents with the deepest local knowledge – but their websites were not built to be consumed by language models.
Unlike a Google search, where a buyer can scan multiple results and read reviews side by side, an AI answer engine often presents a single suggestion with no visible ranking or comparison. There is no way for a buyer to see why the AI chose one name over another.
Lofty’s response is a standalone product called Lofty Front, designed to build what Carter calls “neighborhood pages” – hyperlocal content optimized for AI answer engines. Rather than trying to rank for broad search terms, the pages target specific queries tied to a zip code or neighborhood. Carter says the pages include local restaurant information, school data, upcoming events, walkability ratings, and median home prices. “When you go to these pages, these people genuinely look like local real estate experts,” he says.
Carter draws a distinction between this approach and generic AI-generated content at scale. The concern with mass-produced AI content, he says, is “volume of generic unoriginal sort of keyword driven content” with “no differentiation or verification.” Lofty Front’s pages are built around specific neighborhoods, with ongoing optimization services that adjust as AI models change their criteria.
The product is CRM-agnostic – agents do not need to use Lofty’s broader platform to adopt it. Carter says the barrier to entry was kept low intentionally: “We’ve intentionally done that so that we can uncomplicate it for people and they don’t have to give up their entire tech stack.” It can function as a standalone website or alongside an agent’s existing one, though Carter says it works best as a primary site.
Other firms and independent web developers offer similar AI optimization services for real estate professionals. The market for these tools is expanding as AI-driven search becomes a larger share of how consumers find local service providers.
For buyers, the takeaway is practical. AI tools are fast and convenient, but the agent they surface first is the one whose web presence best matches what a language model looks for – not necessarily the one who has closed the most deals in a given neighborhood. In areas where experienced agents still rely on referrals and older websites, the AI’s blind spots are wider. In markets where most agents maintain modern, well-structured sites, the overlap between AI visibility and actual expertise is likely greater.
As of early 2025, none of the major AI answer engines publish how they weight real estate sources or what “trustworthy” means in their ranking logic. Until buyers can see that methodology, an AI-generated agent recommendation is best treated as a starting point rather than a verdict.
About the Expert: Dave Carter works on AI-powered search optimization at Lofty, a proptech company serving real estate professionals.
This article is intended for informational purposes only and does not constitute legal, financial, or investment advice. The views and opinions expressed herein reflect those of the individuals quoted and do not represent an endorsement of any company, product, or service mentioned. Readers should conduct their own due diligence and consult qualified professionals before making any investment decisions.
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