By: KeyCrew Media

The way people search for commercial real estate has not changed much in 20 years. Type in a city. Set some filters. Scroll through results. For a generation of brokers, that workflow has been the starting point for every deal they have ever sourced.

Dan Mosher, Co-Founder and CEO of DealGround, is changing that.

From Dropdowns to Natural Language

DealGround has introduced two new AI-powered natural language features, property assistant and property list assistant, the equivalent of bringing in a highly knowledgeable analyst to sift through CRE professionals’ private data. Instead of selecting dropdown options, a broker can simply type a sentence: “Show me all the Del Tacos in California that have less than five years left on the lease.” The system constructs the query, runs it against DealGround’s database of over 62 million properties, and returns the results.

“We’re converting from a traditional map and filter-based process to an LLM-powered process,” Mosher explains. “The broker doesn’t have to think about how to structure a search and filter combination. They just describe what they’re looking for the way they would describe it to another person.”

The implications extend well beyond convenience. Natural language search opens up queries that would have been impractical or impossible through traditional filtering, combining tenant type, lease duration, geography, financial metrics, demographics, traffic counts, and off-market status in a single question. A 1031 exchange buyer looking for a quick-service restaurant asset with more than ten years of lease term remaining and a specific cap rate range can describe exactly what they need and receive a list of matching properties, many of them off-market, in seconds.

“That process, finding an off-market asset that fits a very specific buyer profile, used to involve five or six systems, dozens of manual steps, and days of work,” Mosher says. “Now it takes a few minutes. And once you have the list, you can pull ownership contact information on every property with a single click.”

The Analyst at Your Desk

The second and third features, property and list assistant, go deeper. Once a broker is inside a specific property record in DealGround, they can upload any documents (offering memorandums, leases, rent rolls, environmental reports, and title data) and ask questions of those documents directly. The platform draws on both the uploaded files, DealGround’s broader data layer, and the internet to generate answers that go far beyond what the documents alone would reveal. Inside a list of properties, brokers may ask the list assistant to compare and contrast the cap rates of the collection and organize them for simpler processing.

A broker analyzing a retail property can ask how the cap rate compares to similar assets in the trade area, and DealGround returns a contextualized answer: the cap rate is lower than average, but the property sits on a high-traffic corner with a credit-rated tenant and a long-term lease, which explains the pricing. Another broker evaluating an industrial deal can ask about power hookups and parking without opening the OM and get the answer in seconds.

“It’s like having an analyst on your team,” Mosher says. “Instead of asking that analyst to go read through 30 documents, you just ask the assistant. Which of these 20 deals have environmental disclosures buried in the footnotes? Which five can I rule out based on crime statistics or traffic patterns? It answers instantly.”

Early users have pushed the tool into territory Mosher’s team did not initially anticipate. Brokers are asking about household income demographics within a one-mile radius of a property, running crime comparisons for markets they have never operated in, and pulling FBI crime statistics to evaluate neighborhoods before making a site visit. Others are using the list-level analysis to compare cap rates across a curated set of properties and identify outliers worth investigating further.

Your Private Data, Working for You

The underlying architecture of both features is what sets them apart from general AI tools. DealGround’s AI search and property assistant uses the user’s private database, not just the open internet. A broker’s uploaded documents, historical notes, and accumulated deal data are all part of what the system draws on when generating an answer. Information that a broker has gathered over a decade of working in a specific market (and that exists nowhere publicly) becomes searchable and queryable for the first time.

“The private data behind the wall is what makes this different,” Mosher says. “We’re not just asking ChatGPT a question. We’re asking it about data that nobody else has access to, which it can combine with information it already has. This produces unique insights brokers can actually act on.”

A Shift, Not Just a Feature

DealGround’s AI search and property assistant features are rolling out now, with use case templates being developed to help brokers get started quickly. Mosher sees the launch as a turning point not just for DealGround, but for the industry’s relationship with AI more broadly.

“We want brokers to see this and understand: this is not a feature, it’s a shift,” he says. “The way you find deals, evaluate assets, and source ownership data is going to look completely different from here.”

About DealGround: DealGround is an AI-powered intelligence command center for commercial real estate (CRE) professionals. The platform transforms fragmented property, tenant, ownership, and market data into structured, actionable deal intelligence that helps brokers convert insights into opportunity. Built for how brokers actually work, DealGround brings together property intelligence and ownership research to help brokers generate qualified leads and move faster from prospecting to closed deals. DealGround serves many of the nation’s largest CRE brokerage firms. For more information, visit www.dealground.com.

This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.