Commercial real estate ownership research has long been a manual bottleneck in an otherwise digitized brokerage workflow. According to Dan Mosher, CEO and Co-Founder of DealGround, the root cause is structural: property records are scattered across 50 states and thousands of counties, each with its own update timelines, formats, and access rules. Until recently, no technology could efficiently connect the necessary steps to trace an owner from an LLC filing to a phone number and email address.
The fragmentation makes automation daunting. County records update on different schedules—some within hours, others weeks. State Secretary of State filings vary in format and completeness. Because most commercial properties are held in LLCs or trusts, uncovering 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 three separate functions: identifying the entity holding the property, piercing that entity to find the individual, and locating current contact information. Each step draws on different data sources with unique structures and update cadences, making the overall task highly regional and complex.
According to Mosher, the core problem was never that technology couldn't handle any single step; it was that no technology could handle all steps in sequence. Filtering properties, searching Secretary of State filings, and looking up contacts were each individually possible, but connecting them 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 breakthrough 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 been viable for about a year, explaining why ownership research remained manual while other brokerage tasks digitized.
The time savings are significant. Brokers doing active prospecting can spend 10 to 20 hours per week on ownership research alone. Mosher cites customers who logged 15 hours weekly on this work before automating, and now complete the same output in 15 to 30 minutes. This reallocation of productive capacity is not just a convenience; it directly impacts how many deals a broker can pursue.
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 can yield outdated contact information by the time it's used. For brokers whose income depends on reaching owners quickly, stale data costs deals.
DealGround’s platform replicates the manual research process but uses AI agents to run multiple ownership lookups simultaneously. A broker can submit 100 LLCs at once, and the system processes Secretary of State filings, identifies individuals, and retrieves current contact information without manual oversight. “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.
Mosher shares a case where a broker searching for land parcels in Texas, where filings were incomplete, used DealGround to surface an owner name, email, and phone number 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,” because the parcel had been unreachable through conventional methods.
While DealGround isn't the only platform addressing this, Mosher emphasizes its accuracy and freshness. The platform runs ownership lookups on demand, ensuring results reflect current data rather than a static snapshot. DealGround currently reports 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, the bottleneck that constrained commercial real estate prospecting for decades is finally being addressed.
