In the competitive Dallas-Fort Worth real estate market, the Top Gun Team at Epique Realty is challenging conventional wisdom about cold calling. Founder Justin Nimergood argues that having licensed agents spend hours dialing unresponsive contacts is a persistent inefficiency that pulls them away from revenue-producing work. Instead, his team has partnered with Angel AI, a service that combines human callers with responsive AI to prioritize and filter cold leads before agents ever engage with them.
Nimergood's framework centers on what he calls commission-generating activities (CGAs) – tasks that directly produce closed deals, such as showings, negotiations, client consultations, and follow-up with warm prospects. Cold outreach, he insists, 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 take one to two hours, time that could be spent on activities that move deals forward.
The Angel AI model operates by having agents submit a call list and script, then the call center handles the outreach. What distinguishes it from a standard call center is a layer of “responsive AI” that scours publicly available data to rank contacts by priority before calls are made. After calls, the AI analyzes recordings 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, the system flags five as priority leads based on conversational cues. Agents receive this filtered list rather than sorting through raw call logs.
The reporting layer is what Nimergood considers most operationally valuable. Instead of making judgment calls about which cold contacts to prioritize, the system surfaces that information automatically, allowing agents to focus personal outreach on engaged contacts. The model also removes stale contacts 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.”
Nimergood is deliberate about one aspect: the callers are human and based in Texas. He acknowledges that fully automated AI voice calling exists but says it 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 says. “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, treating outreach infrastructure as a distinct operational layer is key. Nimergood applies the same principle to his team's broader operations: agents focus on CGAs while support systems handle lower-value tasks that feed the pipeline. “If they want to be top-producing agents, they have to minimize their administrative time, and they have to maximize their CGA time,” he 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 translates into more closed deals or just more unanswered calls. The Top Gun Team's approach illustrates how AI-prioritized call centers can redefine team support, ensuring agents spend their time where it matters most.
