For decades, commercial real estate ownership research has lagged behind other brokerage workflows in digitization, relying on manual processes that consume hours of a broker's week. The reason, according to Dan Mosher, CEO and Co-Founder of DealGround, is structural fragmentation: property records are scattered across 50 states and thousands of counties, each with its own update timelines, formats, and accessibility. This made automation nearly impossible until recent advances in AI-driven agentic processes.
The core challenge lies in the multi-step nature of ownership research. Most commercial properties are held by LLCs or trusts, not individuals. To find the actual owner, a researcher must first identify the entity holding the property, then 'pierce' that entity to uncover the individual behind it, and finally locate current contact information. Each step relies on different data sources—county records, Secretary of State filings, and contact databases—each with unique structures and update cadences. "Every state is different. Every county is different," Mosher explains. "There is a fragmentation of the properties because they're all managed locally."
While technology could handle individual steps, chaining them together into an automated workflow was the missing link. Mosher notes, "You could probably build technology in each of the three steps, but you could never chain all the steps together previously. Now you can chain them all together, and that's what we offer, which has never been done before." The capability to execute multi-step workflows autonomously, without human intervention at each stage, has only been viable for about a year, which explains why ownership research remained manual even as other parts of brokerage operations digitized.
The impact on broker productivity is significant. Mosher reports that brokers engaged in active prospecting can spend 10 to 20 hours per week on ownership research alone. He has spoken with customers who logged 15 hours weekly on this task before adopting automated solutions, and now achieve the same output in 15 to 30 minutes. This reallocation of time allows brokers to focus on higher-value activities like searching for properties, making calls, and developing deals.
Accuracy compounds the problem. Property owners managing multiple assets through separate LLCs often change phone numbers and maintain multiple email addresses. Manual research that takes days or weeks may yield stale contact information by the time a broker uses it. For brokers whose income depends on reaching owners first, outdated data can cost deals.
DealGround's platform replicates the manual process but executes it through AI agents that run multiple lookups simultaneously. A broker can submit 100 LLCs at once, and the system works through Secretary of State filings, identifies individuals, and retrieves current contacts without step-by-step management. Mosher cites a case where a broker searching for land parcels in Texas, where filings were incomplete, used DealGround to uncover an owner's contact information that manual research had missed. The broker told Mosher, "The fact that you're able to discover this, this could be the difference between no deal and a deal."
While DealGround is not the only platform addressing this issue, Mosher emphasizes its focus on accuracy and freshness. The system runs ownership lookups on demand, ensuring results reflect current information rather than a static snapshot, and currently achieves about 95% accuracy in extracted data.
The barrier to automating ownership research was always the fragmentation of steps across disconnected systems. With AI agents now able to chain these steps into a single workflow, the bottleneck that constrained commercial real estate prospecting for decades is finally addressable. This development could reshape how brokers allocate their time, potentially increasing deal flow and efficiency across the industry.
