In the competitive Dallas–Fort Worth real estate market, agent teams are constantly seeking ways to increase productivity. Justin Nimergood, founder of the Top Gun Team at Epique Realty in Southlake, TX, has identified a persistent inefficiency: licensed agents spending hours on cold calls that rarely yield immediate results. His solution involves partnering with Angel AI, a call center service that uses artificial intelligence to prioritize contacts and filter out dead leads before agents ever engage.
Nimergood’s framework centers on what he calls “commission-generating activities” (CGAs) – tasks that directly lead to closed deals, such as showings, negotiations, and client consultations. Cold outreach, he argues, does not belong on that list. “When a lead is cold, they need to be warmed up again before we’re going to be able to have any real effect on them,” he says. A two-hour block spent dialing 100 contacts, most of whom won’t answer, is time that could be better spent on work that moves deals forward.
The Angel AI model operates with human callers based in Dallas–Fort Worth, but the distinction lies in the “responsive AI” layer. Before calls are made, the AI scans publicly available data – including production records and online activity – to rank contacts by priority. After calls, it analyzes recordings for buying signals and generates a report of genuinely interested prospects. For example, out of 100 people called, perhaps 10 answer, and of those, five are flagged as priority leads. Agents receive this filtered list, saving them from digging through raw call logs.
Nimergood considers the reporting layer the most operationally valuable part. “It lets us know if they’re no longer in the market for a home or whatnot,” he explains. “Then we take them out of our funnel, or we archive them. The point is, we don’t waste our time with initiatives that are not productive.” This system helps teams manage stale leads that often clog databases, improving the signal-to-noise ratio in the pipeline.
One aspect Nimergood insists on is that the callers are human and domestic. While fully automated AI voice calling exists, he believes it’s not ready for scale. “I think that will have a place, and that does have a place in our industry, but not quite yet,” he says. “It hasn’t been ironed out or perfected yet.” He also notes that outsourcing to international call centers can create perception issues: “People stereotype. They just do, and so the more we can minimize that, the better.”
This approach reflects a broader trend in agent team operations: treating outreach infrastructure as a separate layer from the work agents do once a lead is warm. For teams scaling up, the ability to process large volumes of contacts without burdening agents is crucial. “If they want to be top-producing agents, they have to minimize their administrative time, and they have to maximize their CGA time,” Nimergood says. By offloading cold calling and lead triage to AI-supported staff, agents can focus on what they do best – closing deals.
The implications for the industry are significant. As AI continues to evolve, the role of human callers may shift, but the principle remains: leveraging technology to enhance efficiency and let agents concentrate on revenue-generating activities. For leaders in business and technology, this case illustrates how AI can be integrated into existing workflows to solve practical problems, not just as a novelty.
