Show notes
Voice AI is moving from simple call routing into work that used to require trained human agents. The harder question is what happens when those conversations involve lending, collections, servicing, compliance, and real customer risk.In this episode of The Tech Trek, Amir Bormand speaks with Joshua March, founder and CEO of Veritus, about building AI voice agents for regulated financial services. Joshua shares why consumer lending is a demanding test case for voice AI, what makes regulated conversations different, and why the next version of the contact center may be built with much smaller teams overseeing AI systems.Practical takeawaysAI voice agents only matter if they can actually resolve the issue. Joshua argues that users have been trained to distrust automated phone systems because most IVRs block progress instead of helping.Regulated communication is not just about what the agent says. It also includes who can be contacted, when they can be contacted, call frequency, TCPA rules, QA, and post call compliance.Complex voice agents require more than a prompt. Joshua talks about context engineering, state management, specialized background agents, compliance monitoring, KYC workflows, latency, and turn detection.AI changes startup execution. Small teams with experienced people can build and ship much more than before, but that also raises the pressure to move faster.Venture backed AI companies face a bigger bar. Joshua makes the case that higher seed valuations and larger funds increase the need for very large outcomes.Timestamped highlights00:00, Why Veritus is focused on AI communications for financial services and voice agents in consumer lending02:10, Joshua’s path from Facebook apps to social customer service, messaging, bots, and now voice AI05:05, Why AI voice agents may replace a large share of traditional call center work07:00, Why customers have learned to fight IVRs and what changes when AI can actually solve the problem10:00, The compliance layers around regulated voice conversations in lending, servicing, origination, and collections14:00, Why production voice agents need context engineering, state machines, background agents, observability, and monitoring20:30, How AI has changed startup hiring, management, productivity, and the role of experienced individual contributorsOne Line That Stuck“This isn’t a crappy IVR that’s just trying to get in my way, this is an intelligent system that can actually take actions and actually resolve my issue.”Practical lens for technical teamsIf you are building AI into customer operations, the hard part is not only getting the model to speak well. The harder work is making sure it knows what it can do, when it can act, what rules apply, how it is monitored, and when humans need to step in.That matters even more in regulated industries, where the conversation itself is only one part of the system.Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.
