For decades, commercial real estate brokers have spent countless hours manually tracing property owners through fragmented public records. This tedious process, often taking 10 to 20 hours per week, has now become automated thanks to AI-driven agentic systems. According to Dan Mosher, CEO and Co-Founder of DealGround, the breakthrough lies in chaining together multiple research steps that were previously impossible to connect.
The challenge has always been the structural fragmentation of property records. With data scattered across 50 states and thousands of counties, each with its own update timelines and formats, no single tool could efficiently trace an owner from an LLC filing to a phone number or email. Mosher explains, “Every state is different. Every county is different. There is a fragmentation of the properties because they’re all managed locally.”
The process involves identifying the LLC or trust holding the property, piercing that entity to find the individual behind it, and locating current contact information. Each step relies on different data sources with unique structures and access rules. While technology could handle individual steps, connecting them sequentially was not possible—until now.
Mosher attributes the change to AI-driven agentic processes, which execute multi-step workflows autonomously. This capability has only been viable for about a year, explaining why ownership research remained manual even as other brokerage workflows digitized. The impact on productivity is significant. Brokers can now submit 100 LLCs at once, and the system works through Secretary of State filings, identifies associated individuals, and retrieves current contact information without human intervention at each stage.
The time savings are substantial. Mosher describes customers who were logging 15 hours per week on ownership research, now accomplishing the same output in 15 to 30 minutes. This reallocation of productive capacity allows brokers to focus on calls, pitches, and deal development instead of data gathering.
Accuracy is another critical factor. Property owners often hold multiple assets through separate LLCs and frequently change phone numbers and emails. Manual research that takes days or weeks may yield outdated contact information, costing deals. DealGround’s platform runs ownership lookups on demand, ensuring results reflect current information. The company reports about 95% accuracy in data extracted from documents.
Mosher shares a case where a broker searching for land parcels in Texas, where Secretary of State filings were incomplete, used DealGround to surface an owner name, email, and phone number that manual research had failed to find. The broker noted, “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 problem, Mosher emphasizes their advantage in data accuracy and freshness. The barrier to automation was always the fragmentation of steps across disconnected systems. Now that AI agents can chain these steps, the bottleneck that constrained commercial real estate prospecting for decades is finally addressable.
