Regal Innovation & Technology Culture

What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
Blog: “From Human Agents to AI: How Regal Scaled to Meet the Future of Customer Conversations.” Regal didn’t start as an AI company. We began as a modern contact center solution for high-consideration industries like insurance and healthcare, born from a simple insight: ads drive traffic but conversations close deals. We built for that conversation, combining campaign orchestration, multi-channel messaging and real-time data to help agents reach the right lead at the right time. 

As we scaled, we saw the same challenges: limited agent capacity, high overhead and long wait times. We knew AI could solve this, but the tech wasn’t ready. Then it was. Voice quality, LLMs and latency reached a tipping point, and what we had seen coming became clear to the market: voice was the future and AI agents were crucial.

Today, Regal’s AI agents drive real outcomes: increasing reach by 40 percent, doubling conversions and resolving 100 percent of inbound calls 24/7. Built on our orchestration engine, they adapt in real time, follow up across channels and hand off to humans when it matters. What sets our story apart isn’t that we adopted AI, it’s that we were already built for it.

What are you most excited about in the field of AI right now?
As a product person, I’m most excited to define best practices for building and monitoring AI agents. We’re not trying to catch up to an already defined ideal — we’re creating it. The ambiguity is a challenge; customers don’t know what they need, and no one has the blueprint yet, but it also opens up a world of innovation.

On the build side, we’re focused on empowering non-technical users to effectively prompt and configure their AI agents. We’re teaching them how to fine-tune voice settings such as tone, pace and background noise, and how to structure and test prompts to launch quickly and effectively.

On the monitoring side, we’re defining which metrics matter (e.g., containment rate, success rate and CSAT). Since we own the full data stack across customer profiles, actions and conversations, we’re in a unique position to surface insights our customers wouldn’t know to look for. That’s where I see the biggest unlock: using this foundation to answer questions like which agent voice works best for which audience or which phrases signal low versus high intent. We’re building toward real-time insights to help teams test, learn and optimize with confidence.

How do you learn from one another and collaborate?
Continuous learning starts with staying close to our customers. Even our co-founder is in the field, prompting AI agents, monitoring performance and making real-time tweaks. That level of hands-on engagement helps us understand what’s working, what’s not and where the most significant opportunities are.

We prioritize working with customers who are data-driven and open to experimentation so we can test, learn and iterate together. Our platform’s built-in experimentation toolkit allows us to move fast while minimizing risk for sensitive brands or use cases. We can ship a change to just 1 percent of calls, validate its impact and scale it gradually.

On the development side, we stay nimble, running proofs of concept to assess risk and lift before committing to a full feature set. We discuss complex decisions in impromptu chats with the necessary stakeholders, rather than scheduling recurring meetings. Knowledge-sharing happens constantly via Slack, training and a dedicated budget for taking AI courses. We encourage non-engineers to build their own AI agents, both to validate that the experience is intuitive for non-technical users and to help us catch bugs or usability gaps early.