For decades, commercial real estate ownership research remained a manual process, even as other brokerage workflows became digital. The reason, according to Dan Mosher, CEO and Co-Founder of DealGround, lies in the structural fragmentation of property records across 50 states and thousands of counties. Until recently, no technology could efficiently connect the multiple steps required to trace an owner from an LLC filing to a phone number and email address.
County records update on different timelines—some within hours, others take weeks. State Secretary of State filings vary in format, accessibility, and completeness. Because most commercial properties are held in LLCs or trusts rather than individual names, identifying the actual person behind a property requires navigating multiple distinct systems in sequence. “Every state is different. Every county is different,” Mosher says. “There is a fragmentation of the properties because they’re all managed locally.”
The process involves at least three separate functions: identifying the LLC or trust that holds the property, piercing that entity to find the individual behind it, and locating current contact information. Each step draws on different data sources with their own structure, access rules, and update cadence, with accuracy varying by region.
According to Mosher, the core problem was never that technology could not handle any single step; it was that no technology could handle all steps in sequence. Filtering down to the right set of properties was possible, as were tools to search Secretary of State filings and look up contact information. However, connecting these functions into a single automated workflow was not. “You could probably build technology in each of the three steps, but you could never chain all the steps together previously,” Mosher says. “Now you can chain them all together, and that’s what we offer, which has never been done before.”
Mosher attributes this change to AI-driven agentic processes—systems that execute multi-step workflows autonomously, moving from one function to the next without human intervention. He notes this capability has only been viable for about a year, explaining why ownership research remained manual even as other parts of the brokerage workflow were digitized.
The impact on broker productivity is significant. Mosher says brokers doing active prospecting can spend 10 to 20 hours per week on ownership research alone. He describes customers who logged 15 hours a week on this work before adopting an automated approach and now accomplish the same output in 15 to 30 minutes. This time savings represents a reallocation of productive capacity, not just a convenience upgrade. Hours previously spent on research can now be directed toward calls, pitches, and deal development.
Accuracy compounds the problem. Property owners managing multiple assets through separate LLCs often change phone numbers and maintain multiple email addresses. A manual research process that takes days or weeks may produce contact information that is already outdated by the time a broker uses it. For brokers whose income depends on reaching owners before competitors, stale data can cost deals.
DealGround’s platform replicates the manual research process but executes it through AI-driven agents that run multiple ownership lookups simultaneously. A broker can submit 100 LLCs at once, and the system works through Secretary of State filings, identifies associated individuals, and retrieves current contact information without managing each step. “We replicate the manual process today, so it’s not so much different, but we do it much faster because it’s all AI agentic initiated and executed,” Mosher says.
In one case, a broker searching for land parcels in Texas, where Secretary of State filings were incomplete, used DealGround to surface an owner name, email address, and phone number that manual research had failed to produce. The broker noted, “The fact that you’re able to discover this, this could be the difference between no deal and a deal,” because the parcel had been unreachable through conventional methods.
Mosher acknowledges DealGround is not the only platform addressing this problem but positions the company’s advantage around accuracy and freshness of data. By running ownership lookups on demand, results reflect current information rather than a static snapshot. He says DealGround is currently running at about 95% accuracy in the data it extracts from documents.
The barrier to automating ownership research was always the fragmentation of steps across disconnected systems. Now that AI agents can chain these steps into a single workflow, the bottleneck that constrained commercial real estate prospecting for decades is finally addressable.
