In the competitive Dallas-Fort Worth real estate market, the Top Gun Team, led by Justin Nimergood at Epique Realty in Southlake, Texas, is challenging conventional wisdom about cold calling. Most coaching emphasizes consistent phone outreach to build a pipeline, but Nimergood argues that this approach carries a hidden cost: licensed agents spending hours dialing contacts who rarely answer, time that produces no revenue and detracts from work that closes deals.
Nimergood’s framework centers on what he calls commission-generating activities (CGAs) – tasks that directly lead to closed transactions, such as showings, negotiations, client consultations, and follow-up with warm prospects. Cold outreach, he asserts, 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. Even rapid-fire dialing through 100 contacts can consume one to two hours, time that could be better spent on activities that move deals forward.
To address this inefficiency, the Top Gun Team has partnered with Angel AI, a company that provides an AI-supported call center staffed by human callers based in Dallas-Fort Worth. The process begins when Nimergood’s team submits a call list and a script. The AI then scans publicly available data, including production records and online activity, to rank contacts by priority. The most promising prospects are called first and most frequently. After calls, the AI analyzes recorded conversations for buying signals and generates a report identifying which contacts showed genuine interest.
Nimergood describes the output: of 100 people called, perhaps 10 answer, and of those 10, the system identifies five as priority leads based on conversational cues. Agents receive this filtered list rather than sorting through raw call logs. This reporting layer is what Nimergood considers the most operationally valuable part of the service, as it automatically surfaces which contacts warrant personal outreach, allowing agents to focus on those who have already demonstrated engagement.
The system also removes contacts from the pipeline entirely. When AI analysis indicates a prospect is no longer in the market, that contact is archived rather than recycled. “It lets us know if they’re no longer in the market for a home or whatnot,” Nimergood says. “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 approach improves the signal-to-noise ratio in a team’s pipeline, ensuring agents inherit a smaller, more accurate list rather than a bloated database full of dead numbers.
Nimergood is deliberate about one aspect of the Angel AI model: the callers are human and based in Texas. He acknowledges that fully automated AI voice calling exists and will likely play a role eventually, but says the technology is not ready for deployment at scale. “I think that will have a place, and that does have a place in our industry, but not quite yet,” he explains. “It hasn’t been ironed out or perfected yet.” He also notes that outsourcing to international call centers introduces a perception problem. “People stereotype. They just do, and so the more we can minimize that, the better.”
This domestic, human-caller model threads a needle between full automation and in-house agent calling, addressing both reputational risk and opportunity cost. For teams scaling quickly and managing large lead databases, this middle-ground approach allows agents to focus on CGAs while support systems handle lower-value tasks. “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. For teams managing growing lead volumes, the ability to process and triage large numbers of contacts without burdening agents determines whether adding more leads actually translates into more closed deals, or just more unanswered calls piling up in an agent’s queue.
