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About this episode

“It's interesting because call centers have never been thought of as like these technologically advanced places, right? They've always been people on the phone talking to people, and that's what they still are. But because of this kind of unique dynamic, where all of a sudden, with the kind of advancement of conversational intelligence with AI and just the massive corpus of semi structured data that's generated by a call center, all of a sudden they've become like the perfect place for a lot of this kind of very cutting edge technology, like AI, like what we do with Deepgram.”

— Julian McCarty

Julian McCarty is the CEO and Co-Founder of MosaicVoice, a leading, AI powered call center software provider. Prior to MosaicVoice, Julian was Vice President of Software Investment Banking at Credit Suisse where he oversaw numerous transactions for clients including Snowflake, Avaya, Genesys, Hortonworks, Juniper Networks and others. Julian began his career in a consulting role where he advised telecom companies on network performance and operations.

Julian holds a BS in Applied Mathematics and a BA in Political Science from UC Berkeley. He holds an MBA from the Stanford Graduate School of Business.

Listen to the episode on Spotify, Apple Podcast, Podcast addicts, Castbox. You can also watch this episode on YouTube.

This AIMinds Podcast episode takes you into the world of call centers and customer service. Julian McCarty shares his vision for empowering call center agents with smart AI solutions.

Julian uses his background in telecom and investment banking to add to the conversation, revealing the pressures call center agents face daily. From navigating complex databases to dealing with customer abuse and balancing multiple windows, he outlines the intense demands of the job.

Recognizing these challenges, Mosaic Voice has elevated the potential of call centers with AI. By interpreting customer goals and providing real-time information to agents, they manage to streamline services and enhance customer conversations while ensuring compliance.

He also highlights the importance of capturing real-time data to provide actionable insights. Rather than relying on post-call analytics, Mosaic Voice aims to steer conversations back on track as they unfold, transforming call center capabilities and prioritizing agent performances.

This episode also acknowledges the role empathy plays in call center interactions. While Julian predicts the gradual replacement of certain roles by AI, he emphasizes the irreplaceable value of human understanding -- a factor that the most advanced AI systems struggle to replicate.

In the end, the conversation reiterates the essence of Mosaic Voice's mission. It's not just about integrating AI into call centers; it's about understanding the true pain points and designing solutions that empower agents, enhance customer experience and add real value to operations.

Fun Fact: Did you know that call centers face potential fines reaching up to $1.5 million under HIPAA for unintended disclosures during calls? Julian McCarty pointed to this as one reason for a need for real-time improvements in call center operations, particularly in regulated fields such as healthcare, insurance, and financial services.

Show Notes:

00:00 Introduction
05:31 Reducing call center handle time without compromising quality.
09:17 Agent and company may be limited in flexibility.
10:18 Explaining Bluetooth to older adults with varied cars.
14:58 Real-time transcription is now feasible and affordable.
20:15 Real-time tool prevents regulatory violations, provides insights.
21:10 Improve agent performance through AI and analysis.
26:35 AI tools play an important role in calls.
31:30 Call center uses AI for improved service.
33:39 Improving call center conversations and pain points.
35:36 Significant impact expected, expect long-term benefit.

More Quotes from Julian:

“ I also think where technology is, where AI is, where transcription is, has evolved to such a point where there's a lot that is possible today that wasn't possible at the time that I was sitting in the call center.”

— Julian McCarty

“The goal is always, how can we reduce handle time without compromising the quality of the conversation? There's, in some instances, there are quotas where they're expected to, you know, in outbound sales calls, either book a certain number of meetings move a certain amount of product.”

— Julian McCarty

“For the first time, we're able to transcribe in real time accurately and also at a price point that allows us to transcribe tens of thousands, hundreds of thousands of calls every day.”

— Julian McCarty

Transcript:

Demetrios:

Welcome to the AI Minds podcast. This is a podcast where we explore the companies of tomorrow built AI. First, I am your host, Demetrios, and this episode is brought to you by Deepgram, the number one speech to text and text to speech API on the Internet. Trusted by the world's top conversational AI leaders, startups, and enterprises like Spotify, the one that you probably listen to music on. Twilio, NASA, that one that sends rockets into space, or at least used to. And Citibank. Today we are joined by none other than Julian, co founder of Mosaic Voice. How you doing, man?

Julian McCarty:

Good. Good. That was. I get. Yeah, you're right. I don't think NASA is sending a lot of rocket. I mean, they're doing something. So best of whistles.

Demetrios:

What happened?

Julian McCarty:

Yeah. Yeah.

Demetrios:

They stopped, kind of. It is funny how that worked. But to start us off, right, I feel like I need to repeat back something that you just said to me before we hit record, which is you spend 24 hours a day thinking about the call center space. Can you give us a little bit of background into who you are and why you spend so much time thinking about that space?

Julian McCarty:

Yeah, certainly. So, you know, I began earlier in my career, I worked in kind of a consulting advisory role where we were advising telecom companies on operations. And so a big piece of a telecom company is their call center. Right. And so for me, a lot of my time was spent in and around call centers. You know, sitting next to the agents, sitting in training, sitting with management, understanding a lot of the metrics, understanding what success means for a call center. And, you know, the takeaway from that was really, this is a really tough job. Right.

Julian McCarty:

It's a really tough job, and I don't think people necessarily understand that. And my experience has been and continues to be, these guys working in call centers, they're working hard. They want to be successful. They want to have great customer conversations, but they don't always have the tools to do that. Later in my career, I worked for many years in investment banking, and part of that role was covering analytics companies. And so everything from Snowflake to Hortonworks, and that was a great experience. But a lot of that time was also spent in the telephony space and with the call center analytics companies. And so there, it's like you're seeing the same problem but from a very different angle.

Julian McCarty:

Right. From more of the management perspective. And I think the vision and the idea for mosaic voice grew in that gap between the mismatch of the lived experience and then a lot of the solutions that are out there trying to address many of those pain points that we experienced, but fall short and fall short for a number of reasons. I also think where technology is, where AI is, where transcription is, has evolved to such a point where there's a lot that is possible today that wasn't possible at the time that I was sitting in the call center. And so there's a lot of great problems here. There's a lot of great solutions out there, some of which people are aware of, some of which people might not be. But at mosaic voice, we're excited to really, we think, be making a difference in the lives of these agents and the performance of these call centers and ultimately it outcomes of our customers, customers of the call centers themselves. And it's exciting.

Demetrios:

So you just mentioned that working as an agent at a call center is very difficult.

Julian McCarty:

It's very difficult.

Demetrios:

Is that because of a variety of things, I would imagine, but some of them can be the people that you talk to. They're not happy, and so they're trying to get something done and you're trying to appease them, but they can be difficult. And so that is taxing, I imagine. There's also, I would bet that you have some kind of quota that you have to hit where you're trying to get through calls as quick as possible so that you can make sure that you're hitting all the quota. What are some other challenges or unique challenges being an agent and it puts that pressure on?

Julian McCarty:

Well, first of all, I mean, you're absolutely right that a lot of times there's abuse in terms of the customer calling in, and the agents aren't necessarily always empowered to resolve the customer's challenge. That's clearly a point of frustration and a thing that makes it very difficult. A lot of it is just confusing. When someone calls in, you have to very quickly figure out who they are, what they're calling about, figure out where their account is. You know, sometimes you're selling a product, but the actual challenge is with the person processing the credit card, which might be a third party. So it's just a very complex job. Generally, these agents have five different windows open on their screen, as you said. You know, it's not necessarily that they're trying to rush through these calls, but the number one driver of customer satisfaction is often handle time.

Julian McCarty:

And so the goal is always, how can we reduce handle time without compromising the quality of the conversation? There's, in some instances, there are quotas where they're expected to, you know, in outbound sales calls, either book a certain number of meetings move a certain amount of product. And so a lot of this is done in kind of a high pressure environment. And, you know, there's been a number of studies done around this, a lot of it showing that mental health in the call center is not great. Also, studies that have shown that if one agent starts to underperform, the agents sitting in proximity to that agent also start to underperform. And so you get these effects where the weakest link kind of undermines the performance of the rest of the team. And these are also very cost conscious organizations. And so there's always an element of how can we get the most performance out of this team and invest where we're going to see return, but also be cognizant that every dollar counts. And there's a huge amount of just data that comes out of call center, both in terms of the calls themselves, but also in terms of the operations.

Julian McCarty:

They're constantly looking to optimize. And when you're the item that's being optimized, that can be stressful.

Demetrios:

Yeah, I'm just thinking about one of the last calls that I had to corporation that I was trying to contact or get a hold of.

Julian McCarty:

Right.

Demetrios:

And I'll walk you through the scenario and so that we can go through this together. It's like I call because I have a question about my billing, and I get, first thing that I get right off the bat is an automated message. And it's like, if you'd like to do this, press one. If you're looking for this, press two. And so I'm already like, just give me somebody to talk to because this is a little bit annoying, right? And so I'm trying to get filtered through that right there gets me being like, operator, operator, operator, operator. Or I'm trying to just, like, get through the recording. When I do get to the person, they can't do anything about my situation. And that's probably on me because I was like, operator, operator.

Demetrios:

And so the experience, as you said, it's already like I'm in a situation where I'm sitting there like, oh, my God, this is so painful. Why do they make it so difficult? And then instead, so I get transferred. And inevitably, if I do get things resolved or maybe I get cut off, or maybe when I do get transferred, something happens and they're like, no, we got to transfer you to them. And you get transferred a few times, then maybe you get cut off and you got to start the process again. I get the situation resolved, and then inevitably, the person on the end of the line tries to upsell me on something and it's like, oh, yeah, well, since, by the way, that you're, you're already talking to us since we got you here. Let's see. Do you want to.

Julian McCarty:

Yeah. Why don't you upgrade to a landline phone, which no one has needed for 20 years.

Demetrios:

Exactly. Those are hot. In 2024.

Julian McCarty:

Well, you know, the thing is, first of all, the agent that you're talking to could easily not even be an employee of the company that you call. Right. Often these roles are outsourced. And that's great, you know, because when they're outsourced, you're talking to someone who's a professional call taker. Right. That's what they're trained to do. That's what the organization does. And generally they can do a really good job of it, but they might not be empowered to resolve your problem.

Julian McCarty:

And you know, what I say by that is the agent might not be empowered to solve your problem, but the company that the agent works for also might not be empowered to make changes to the process. And so there is a real limit in terms of the flexibility that agents have in some environments to kind of work with you understand your pain point, you know, be empathetic towards that and then work with you towards the right solution. Oftentimes, they're just constrained there. You know, the second piece is, and just to give you a very basic example, if I buy my mom a car and she listens to books on tape, right, what car are you going to buy in 2024 that has a tape player? Nothing. Right. So you also have to get a little thing that's going to connect via Bluetooth to the tape player or to the CD or not even a CD. Right. The satellite radio player.

Julian McCarty:

Well, how am I going to explain to someone in their mid eighties how to connect something to Bluetooth, right? She's going to end up calling the customer support line and let's say she buys a Honda. Well, how many different Hondas are there out there? And then how many different years and makes are there of that vehicle? And so, although connecting a tape player to a Bluetooth speaker is actually probably pretty easy, when I'm someone sitting there taking the call and I just say, hey, what kind of car do you have? It's going to be different for every single type of car. It's going to be different for every year that car was made, and it's going to be different for different versions of the vehicle. What happens is there's massive amounts of data and a huge knowledge base that this agent has to navigate through. And generally they're doing it by just pressing control f on a PDF somewhere and trying to figure out, okay, control f, Bluetooth 2022 CRV or whatever it is. Right. It's difficult. And so they're trying to do that.

Julian McCarty:

They might put you on hold. You're getting frustrated. You've already gone through this IVR, the interactive voice response system, to try to direct you to the right person. But they're navigating complex databases. They're moving through these knowledge bases. And what mosaic voice does is we're listening to these calls, trying to understand the context, using AI to interpret next best action, to understand what is the goal of this customer and then bringing up the right information for the agent right. At the right time. Right.

Julian McCarty:

So our focus is how can the agent focus on having a great customer conversation and let us do a lot of the work around finding the information, taking the notes, summarizing the call. But at the end of the day, it's a complex task where agents aren't always empowered to, to resolve it. And so you have to have a little bit of grace when you talk to these people, understanding in their position, they want to help you. They don't always have the ability to do that.

Demetrios:

That is such a great point that even if the person is restrained, it might not be because of that specific person being restrained. It's more because of the company that's restrained. They're a contracted company that then goes and sets up this call center. And so, yeah, a little more empathy.

Julian McCarty:

It happens all the time, you know, it happens all the time where we'll be talking to an outsourced call center. They'll say, hey, you know, we need to reduce, handle time on these calls by 15%, but we're not allowed to change anything about the script. We're not allowed to change any of the rebuttals. We have to ask these five questions at the beginning of the call, and there's just not a lot of room to make edits. So there's oftentimes and not always, but there's distance between the people that make a lot of these decisions that directly affect the call center, the agents, and the customer experience. And, you know, those people that are actually on the call with you. And so there's not necessarily a lot of the people that you're talking to aren't necessarily the ones making the decisions. And that's, you know, that's a challenge.

Demetrios:

And I guess this comes back to the different problems that you were seeing when you were looking at the specific agents at the beginning of your career versus later on when you were in the financial. Was it a financial analyst looking at more of the management side of things?

Julian McCarty:

Yeah. Well, you know, a lot. A lot has. It's interesting because call centers have never been thought of as like these technologically advanced places, right? They've always been people on the phone talking to people, and that's what they still are. But because of this kind of unique dynamic, where all of a sudden, with the kind of advancement of conversational intelligence with AI and just the massive corpus of semi structured data that's generated by a call center, all of a sudden they've become like the perfect place for a lot of this kind of very cutting edge technology, like AI, like what we do with Deepgram. You know, when I was first working with these call centers 15 years ago, that didn't exist. And a lot of the tools that were developed in that era were developed in an architecture that accommodated the capabilities of that time. Right.

Julian McCarty:

Things like real time processing, transcribing in real time in the browser is impossible because the architecture was designed such that any call recording is sent to a server, it's processed after the call's over, analytics are extracted from that transcript. Those are then kind of pushed to some reporting tool, and you get this very delayed kind of workflow. Now, you know, it's different. It's a different era. And for the first time, we're able to transcribe in real time accurately and also at a price point that allows us to transcribe tens of thousands, hundreds of thousands of calls every day. Right. Whereas historically that would have been cost prohibitive and the quality would not have been there. And so, you know, as that has evolved, you see this bifurcation in terms of the types of solutions that exist, solutions that were developed prior to these real time capabilities, and then solutions that were developed with that ability in mind.

Julian McCarty:

And what's different about mosaic voice and many of our peers is that because we were developed in a world where these capabilities existed, the architecture that we use, a lot of the computes that's in the browser, it's designed to be quick, it's designed for real time. And so, you know, we've seen there's a lot of difficulty for these call center software solutions that were developed ten years ago to really move into this modern era, because real time, it's not something you can turn on and off. It requires just a fundamentally different approach to how the software is built, and that will be a challenge for a lot of these legacy vendors.

Demetrios:

And so then talk to me about the insights that you can now provide because you can do it real time. See the delayed gratification in a way of the story that you're telling of. Yeah. It would sit server side. You would analyze it. Maybe you have some data scientists or some analytic analysts or a certain whole nother tool that is helping you figure out, like, did we do well or not? And then at the end of the month, you can get a report, say, like, x amount of our calls did well or x amount didn't or were dropped. Whatever it may be, whatever your metrics are that you're looking at now, that it's something you can get in real time, is it just like being able to see that at any given moment? How is that empowering the call centers to take different actions?

Julian McCarty:

Well, one way to think about it is it's. And this was coined by someone on our team. The phrase, you know, real time is like disaster avoidance. Post call is like disaster recovery. Right. Providing real time analytics and insights during the conversation is what you need when it comes to how can we take this conversation that's going off the rails and redirect it? And, you know, one thing we look at often is we think about companies like Gong. Gong is a very powerful tool, largely for enterprise sales teams that does the post call analytics. The reason they can be so powerful is because a lot of these agents, or customer success team members or salespeople that they work with are going to be in that role for a year or two.

Julian McCarty:

You have this horizon that you can develop. The salesperson over in the call center, the average agent tenure might be a year. You can't necessarily wait for six months to really start seeing improvement in their performance. You need to know, how can I take this agent who's on a call right now and make them better? And this is particularly important in call centers that exist in regulated environments, call centers that sit in healthcare, healthcare, insurance, you know, financial services. For these types of businesses, there's huge legal overhang around what they do and what their agents say on these calls. You know, we talked to one of our customers who was selling insurance, and they said, if an agent says, this is a great plan for you, that's fine. If an agent says, you know, Demetrios, if my mother lived exactly where you lived, had your exact same kind of characteristics, this is the plan I tell my mom to get. Somehow, that is also fine.

Julian McCarty:

If they were to say, but Demetrios, this is the best plan for you. That can incur up to a $1.5 million fine from HIPAA. Right? So, yeah, so, you know, if, and these are fines that are paid all the time. They're paid by, you know, Navient paid $1.7 billion and went bankrupt. Wells Fargo, capital one, all of these guys have paid hundreds of millions of dollars in fines stemming from actions that took place in the call center. And when you think about, all right, I have a call center, maybe there's, I'm in a building with five floors. There's 100 people on each floor. I got one guy on floor, three aisle, a row, 17 that forgets to say the call is recorded for a day.

Julian McCarty:

Right. And he's forgetting to say that someone on the other side of the line records that. And then, you know, turns that over to some kind of regulatory body, you're exposed to a fine. Of course there's complexities there around, you know, how you will be prosecuted. But unless you have some type of real time tool that's going to help you proactively prevent those situations, it's going to be tough. And so, you know, to your original question, the types of insights that you get from using a tool like mosaic voice is, we're going to make sure you stay on message. If the customer asks the question and says, you know, how much is this product going to cost me? It'll hear that. It's going to understand that the customer has a concern around expense and it's going to bring up a very brief script about, why don't you say it's going to cost this much over the lifetime of the contract? It's going to be this much per month.

Julian McCarty:

You're actually going to save this much over time, making sure that your agents are saying the right things and then also making sure they're delivering it in the right way. Are they being empathetic? Are they asking the right questions? Are they talking too much? Or maybe they should listen more. Right? Are they on a long monologue? And so really, how can you take what you're seeing with your best performing agents and institutionalize that across your entire agent base? And then there are a lot of post call analytics, right? Every call center has a scorecard for their calls. You know, you're supposed to say this, you're supposed to say this. We use AI to automate that. And then you can extract insights from that data and then take those insights and push them back into the real time guidance. And so you get this system where you're constantly learning from the calls and then pushing those learnings out to the agents, seeing where the improvements are. And it's just a constant kind of, how can we make these calls better? And ultimately, how can we also build more rapport, elevate the role of the agent from an agent to more of an empathetic partner on the conversation? And it all comes back to, how can we make this simple? How can we have great customer conversations? How can we make sure our agents are compliant? How can we make sure the managers have the tools they need to understand that a lot of their agents are doing a great job? How can we use QA, not just to reprimand people, but to highlight great performances? And, you know, I think mosaic voice sitting at the center of all that conversational data positions us well to provide a lot of that value.

Demetrios:

So I was expecting when you were talking about the different ways that you can say something and the ways that are okay and then not okay, as you were going to say something along the lines of, yeah, when somebody starts going off script, you get, like, what you would see back in the day when you would get a virus on your computer and, like, it would be a big stop sign. And then all of a sudden, many different stop signs are coming. The more that you continue to go off script, next thing you know, there's.

Julian McCarty:

Those exist, right? If an agent asks for a credit card number when they're not supposed to, they get a little pop up, and their manager also gets a pop up in real time that says, hey, Julian's asking about someone's credit card number when they shouldn't be. They're asking about a Social Security number when they shouldn't be. But the people that are going to go off the script the most, unfortunately, are the customers. Right? It's the people that call in and start yelling. And it's important to track that as well, right. Because these agents, like we said earlier, they take a lot of abuse. And sometimes just knowing that an agent is getting yelled at, having the manager go over and say, you know what? You're doing a good job. Stick to the script, and you're going to be fine.

Julian McCarty:

Don't worry about it. That stuff goes a long way. You know, a kind of less fun example that we had recently was one of our customers is a very large university, and they have several hundred people that work in a call center that deal with applicants, students, financial services, et cetera. And over the weekend, someone had called and placed a bomb threat at the school. Right? Now, because of the way mosaic voice is structured, we see that and in real time, we give the agent the appropriate response to say to this person. We also escalate that directly to their manager and because of the severity of the item, directly to leadership. And so, you know, this is a process where leadership has now been made aware of this threat literally within seconds of it taking place, you know, ten levels below them with one agent across this pool of several hundred. And so, you know, these can be powerful in ways.

Julian McCarty:

These tools can be powerful in ways that maybe we don't expect at first but ultimately can make a big difference.

Demetrios:

So there is something here where it is almost like the insight machine that you've created, and I've heard a lot of people talk about action machines. And so it's almost like you've said, we're going to graduate up this important information to whoever needs it as soon as they need it. But we're not going to try and get our system to actually be the one that takes action. We just want to give the information. We don't want to be the one that instantly calls the police. Also, how do you think about that? Like, augmentation versus just totally getting the human out of it?

Julian McCarty:

Yeah, certainly, you know, mosaic voice is a tool to empower agents, to empower the human being that is an agent. Right. We're not a tool that aims to replace them. We're a tool that aims to let them do the parts of their job that they're going to be great at, which is talking to customers, building a rapport. And let us take notes. Let us summarize the call, let us update the CRM. But we believe in the power of the agent. Now, that's not to say that the future, in the future that some agents won't be replaced by AI powered software because we already see that happening in certain verticals.

Julian McCarty:

And those tools have also demonstrated that they do a great job with certain types of calls. But, you know, when you're calling to talk to someone, you know, in the insurance space about a medical problem, when you're calling to talk to the university about, you know, something related to your education, when you're talking to someone in the financial services space, a lot of these conversations deal with very personal items that need to be understood and they also need to be understood correctly. You know, in a lot of the types of customers that we serve, there isn't room for error. Right. You know, if someone's standing on the side of the freeway, their car broke down, it's the middle of the night, and they need support. You need to make sure that you're going to be able to get that to them in an expeditious way without having to frustrate them by having them repeat themselves or hang up and call back. And so there's certainly, without question, a place for these agent replacement tools. But I think when we think about the promise of AI and promise of AI in the call center, we haven't gotten to kind of what has necessarily been marketed to people, which is, don't worry about it.

Julian McCarty:

A robot's going to take all your calls, and it's going to be great. We've gotten to a place where, listen, AI can do a good job with certain types of conversations, just like chatbots did a great job with certain types of conversations. But if you look historically, there's more call center agents now than there were when chatbots were first developed. Right. What role have chatbots played? They've resolved some of the simple questions, and they've pushed the more complex questions onto the agents. And now when the agent answers the call because someone's already talked to the chatbot, they're already frustrated. It's only made the stakes of the conversations that the agents take higher. And in those environments, the agents need as much information at their fingertips as possible, and that's what mosaic voice does.

Demetrios:

So in these times of very sensitive or very important conversations, it feels like, yeah, the stakes are really high, and you want somebody. You want to talk to somebody. You definitely. Until AI, we see, like, a gigantic shift in the capabilities of the AI voice bots. If I'm talking about money stuff, and, like, especially if it's like, hey, where did this money go? I do not want to talk to a computer at all. I want that empathy that you're talking about. Like, I want to be able to connect with the person. And really, I'll probably ask the same question three different ways to make sure that I understand and make sure that things are super clear.

Julian McCarty:

And that's. And that's exactly right. And it's not to say you're not someone that's eager to adopt technology. I think one thing for us has always been, how can we use AI in a way that's going to add real value to our customers and focus on the value that we're trying to add less. So the technology that we use to get there, and we do use a ton of AI. We use call summaries, we use sentiment, we use topic and intent detection, but we use those because those are valuable tools for people that are trying to understand what's happening on conversations, trying to understand what the drivers are of these customer conversations. We don't use those because that capability exists. Right.

Julian McCarty:

And I think there's a fine line there of, you know, adopting powerful technology that can change outcomes is great. Adopting technology because it exists can ultimately cause frustration with customers. And so we try to be mindful of that because just like everyone else, we think AI is huge and it's going to unlock a massive amount of opportunity, but we don't necessarily want to apply it in environments where it's not ready yet.

Demetrios:

One thing that I have not seen, and maybe this is more common, it's just that I haven't encountered it, is the ability for after I have a call with whatever the provider may be, for me to get that information back, too, like they have my email. I would love this summary of the call and what the next steps are and what the last time I called was. Is that something that you do or you allow the call centers to do? Like it feels like, oh, can we just push this to the customer also so they're informed?

Julian McCarty:

You know, it's something that's certainly possible, but it's, that decision sits with the call center itself. But, you know, one thing that we've done which I think is kind of unique but is actually touches on this, is, let's say, you know, you call your insurance company on Monday, you get something resolved. You call again on Wednesday, and it turns out the thing wasn't resolved. Well, when mosaic voice sees the same phone number calling in, within a certain amount of time, as soon as the agent picks up that phone, they're going to see the AI summary of the last interaction they had. So, you know, before they even say hello, they'll know, hey, Demetrius called on Tuesday. He had, you know, he was frustrated because his insurance premium had gone up. He didn't understand why we opened a ticket. The ticket was closed.

Julian McCarty:

So before I even say hello, I have the context of your last conversation. And that, I think, is a very powerful way to build rapport, build empathy with the customer, knowing, like, hey, listen, I know you called earlier this week. I know we told you that this issue was resolved. You're calling back, so obviously it's not. I'm sure that's frustrating. Let's just work through it without having you repeat everything you've told me in the past. And that's a way that we kind of resurface customer data that has been generated historically and bring it to kind of a real time context.

Demetrios:

Yeah, that's very helpful. Again, it really feels like, and I like the way that you put it where you said, we're trying to make the customer experience top notch. And whatever technology or whatever ways and means that we can make the customer experience better, we want to have that happen. So we're empowering the call centers with everything that they need, whether that's AI or it's just call recording or might be some old school tech or not even tech at all. Low tech, whatever you want to call it, but just making it easier for them to make that end user happy.

Julian McCarty:

Exactly. I mean, it's, you know, it's not like a friend's NASA, right, where we're not. The goal isn't pioneering a new type of conversation. It's taking what these guys are doing every day, and what can we do to make it better? I think sometimes some of the companies in our space kind of get confused and think of it differently. And I think oftentimes customer or companies or vendors that sit in, you know, the Bay Area or in Austin or in one of these tech hubs can be a bit out of touch with the actual pain points of these call centers. Call centers, they don't care about technology. They care about conversations. They're less concerned if it's AI versus some rule based logic versus something that's just written and pops up on the screen.

Julian McCarty:

Right. They just want to have good conversations, and they don't care what it is that gets them there. And so I think, like I said, our approach has always been, how can we add value with whatever tool it takes?

Demetrios:

Excellent. Well, this has been a fascinating conversation. I really appreciate you coming on here and schooling me a bit about the call centers and also getting to dive into what is obviously your passion.

Julian McCarty:

Well, thank you. Thank you for having me. And I would just leave everyone by saying, have, you know, patience. The next time you're on hold at the call center, know that these guys are working hard and it's a difficult job. They really want to help, and they're doing their best. They don't have the tools. So please be kind, but thank you so much for having me.

Demetrios:

I'm going to try and strike up a conversation with the next call center agent that I talked to, hopefully with the intent of brightening up their day and helping them have a good day or a little ray of sunshine.

Julian McCarty:

It will go a long way.

Demetrios:

Excellent. Thanks for doing this.

Julian McCarty:

Awesome. Thank you.