Startup Uses AI to Match CRE Lenders and Borrowers

Finance Lobby wants to do for commercial real estate financing what Amazon did for retail—transform the business of matchmaking into something faster, smarter, and infinitely scalable. Instead of moving capital itself, this startup has set out to connect borrowers and lenders by leveraging technology, aiming to create opportunity in a marketplace that’s long depended on who you know rather than what you know.

“We’re a marketplace for commercial real estate finance,” Finance Lobby Chief Executive Chaim Schwartz explained in an interview with GlobeSt.com. “It’s a complex system to match supply and demand, as is true for any marketplace.” In the traditional world of CRE, deals often hinge on personal networks. “If you’re a real estate investor, a broker looking for financing, where do you start?” Schwartz asked. “You start making phone calls. You may know a couple banks, you may have a few contacts, but obviously you’re limited to your Rolodex, right? Now, any industry that is purely working off your Rolodex is extremely inefficient. Even if you have the best Rolodex, it’s very hard to stay up to date,” Schwartz told GlobeSt.com.

Finance Lobby’s answer is a technological one. Rather than leaning on relationships and endless outreach, the company uses databases and artificial intelligence, including machine learning and large language models, to match lenders and borrowers based on detailed preferences. Borrowers input information about their property and proposed deal, while lenders see only the projects that meet their lending criteria. It’s the technology that sparks the conversation—leaving both sides to determine what, if anything, makes the most sense.

The journey started simple. “Our initial matching system was geography, dollar amount, asset class, okay, and that’s it,” Schwartz explained. But the platform quickly evolved. “We introduced an AI system where the lender could come and upload their entire terminology.” This allowed for a more nuanced match—maybe a lender is strict on loan-to-value ratios but more flexible on monthly payments, for example. Machine learning then refines these matches, incorporating feedback from lenders who pass on a deal. “Now that factor goes into the machine learning, and the same thing on the quote side, so we know next time, when we push it to the other lenders, what is this guy looking for?” Schwartz told GlobeSt.com.

Schwartz drew a comparison between Finance Lobby and data aggregators like Zillow, noting the power that lies in a wide variety of data. When President Trump announced tariffs, for instance, one type of data Finance Lobby collected was the reason a lender declined a particular deal. During that period, Schwartz shared, the number of lenders who cited market uncertainty as their reason for declining jumped 500%.

The broader vision for Finance Lobby, however, extends far beyond today’s deals. Schwartz sees a platform that could eventually connect all corners of the CRE world—title insurance, property insurance, inspections, valuations, and many other services. “When you’re sitting at the forefront of the transaction, commercial real estate has so much around it,” Schwartz said. “You own a property, you need your cleaning, you need your maintenance… there’s just so much around it of ancillary potential, ancillary services, God willing, in due time,” he told GlobeSt.com.