Colorado Innovators: Mario Ciabarra Part 2, CEO and Founder at Quantum Metric

[00:00:00] If you remember back into like maybe fifth grade, remember when you had an answer in math? And it could be the correct answer, but your teacher didn't give you full credit because you didn't show the work. Like I don't trust you. I don't know how you got that answer. And if you didn't show me how you got to the answer, I'm not going to give you full credit. And I think the same thing with AI. I think we need to as responsible deliver, you know, software companies delivering agentic AI solutions, we need to show our homework. We need to show how we got to the answer. Because if I question a human analyst, how they got to an answer,

[00:00:29] they're going to give me the facts, they're going to show me how they got there. I think AI needs to do the same thing. In this episode of The Bear Roars, Dan Caruso sits down with Mario Ciabarra, founder and CEO of Quantum Metric and one of the most respected entrepreneur engineers in enterprise software, for a candid conversation about what it takes to lead through one of the biggest technology shifts in decades.

[00:00:51] What started as a company focused on making digital experience data actionable has evolved into an AI first platform delivering answers in seconds. Mario shares how Quantum Metric reinvented itself for the AI era and turned a period of industry uncertainty into three consecutive record breaking quarters.

[00:01:09] Along the way, he explains why transparency is the missing ingredient in most AI products, why trust is becoming the ultimate competitive advantage, and why leaders who understand both technology and business will be the ones shaping the future. Let's poke the bear to kick off the conversation.

[00:01:27] All right, Mario, this is a part two. We did the first one with you well over a year ago. So although we've seen each other a lot of times since I haven't really gotten a deep dive update on your business and where it's going. You're running one of the most successful tech. I'll say startups. It's now a scale up. It's a big company, bigger company now, but a company that was started here in Colorado.

[00:01:50] It's one of the bigger, very software services based, very AI forward, very exciting business. So love celebrating entrepreneurs like you and tech businesses that are built here in Colorado. So I'm really looking forward to this update. It's great to be back here, Dan. I would say it's scary that it was over a year ago that we did this before, but time passes. We'll have fun and excited to be here.

[00:02:16] Yeah. So before we get into what's going on currently with your business, let's do a recap of you and the business that you started, where you started, because you're in Boulder now. You didn't start in Boulder. How you ended up in Boulder itself and what your business is all about. Well, I'll tell you, it sounds weird to say out loud, but I guess you could put me in the box of serial entrepreneur starting somewhere around the age of like eight.

[00:02:44] So you could see it written on my face. This guy is going to be an entrepreneur. And I think part of that is, as we talked that first time about being the son of an immigrant from Italy, there's just something to it. But, you know, the business itself is quantum metric. And what quantum metric does is it captures- Which is not quantum business. Which you have to say in Colorado. I get an email about once every couple of weeks about, hey, we'd love to help out with quantum. I'm really excited about quantum. I'm like, it's a little bit different of a business, but I'm excited about quantum as well.

[00:03:13] But yeah, quantum metric, it's a SaaS company. And what we do is we help organizations collect data around their digital experience. So on their website, on their apps, and we help them understand how can we make it better from the customer's lens. You know, I think one of the things that helps people understand what we do is like, you know, if you're a user and you're on a website for an airline, a telco, a bank, healthcare, a retailer, and you're trying to do something on that website and it doesn't work.

[00:03:41] You know, what do you do? If you think about it for yourself, Dan, what do you do? If you're on a retailer, I think retail is a fun one because like we all have this experience. You're on a website with a retailer and you click add to cart and it doesn't work. What do you do? Well, I usually take my computer, throw it against the wall. Exactly. I swear a few times. I'm not the best example for this. No, no, that's the perfect example because what you don't do is call them up and saying, hey, I'll sit here waiting for your website to work. You know, let me know when it's working. And if you think about all that frustration.

[00:04:11] Do you have any bots I could talk to? Exactly, exactly. Nowadays. So the challenge with these businesses is if you have customers having these negative experiences and they're not calling in, how do you find out about them? Because you want to know. Exactly. You want to know because for a whole bunch of reasons, certainly because your customers are getting frustrated. You want to know about that. If you're losing business, you definitely want to know about that. And you might be losing a customer altogether if it's happening frequently. I love that comment.

[00:04:39] Absolutely. You want to know. It's the lifetime value of the customer, not just the transactional value because you go to a website, it doesn't work. Do you want to go back to that website again? Yeah. And here's the real problem is how do we quantify, you know, most of these sites have a thousand different issues. Which one's the biggest? How do they find out about it? And then how do they prioritize what's important across their team?

[00:05:01] Because there are issues that are affecting marketing. For example, a great marketing campaign driving people to an out-of-stock page. We've all experienced this, right? You know, get excited, click on the link, and then all of a sudden the product's out of stock. Or a technology problem or, you know, a user journey issue. And so we help these organizations discover this, quantify it, and prioritize it across their team. And we work with the world's largest bank, airline, telco, healthcare, gaming, and retailer. So it's been a really exciting business. Yeah. And you've been at it for how long?

[00:05:30] So 2015 started it, as you mentioned, started in Colorado Springs, moved up here to Boulder about closing in on three years now. So having a great time up here. So, okay, we'll come back to business in a second, but tell us about your experiences in Boulder. How have you found Boulder to be?

[00:05:45] You know, I think, you know, Colorado is a diverse mix of different styles across the state. I spent 20-some years in Colorado Springs, had a great time, raised three kids early on, and then moved them out here. And I think all of us have just settled into the Boulder lifestyle. We've always been an active family. We love hitting the slopes. I do a lot of running.

[00:06:12] So I think there's just a little bit more, it's a different kind of community, a little bit more outdoorsy of a community here in Boulder. And I think, you know, it's easy to say that the entrepreneurial community is a bit stronger up here in Boulder. Well, on that last one, I think it's probably a lot stronger in Boulder than Colorado Springs. But Colorado Springs is a beautiful community as well. There's different types of business that goes on there.

[00:06:36] There's a lot of veterans down there, a lot of active businesses designed around the defense and aerospace community. So it's certainly a vibrant ecosystem, but it's the vibe there relative to, you know, entrepreneurial vibe is, you know, very different. I'd say a lot lesser so than you get in around Boulder. And they make a, you know, it's not a judgment call. It's just a very different characteristic.

[00:07:06] And it's more conservative there. We're more liberal up here. Creates a good balance, I think, in the state relative to that. Yeah, I think it's always great for people to mix their minds in different perspectives. So whether you're here in Boulder or in Donovan, Colorado Springs, obviously there's different perspectives. And I think we all learn from that. And as far as the entrepreneurial community, as you mentioned, defense industry is strong.

[00:07:31] There's I definitely have connected with and friends with folks that have started their own business in those communities. So it is a different vibe. I also think that if you shake the trees in Boulder or Colorado Springs, phenomenal people fall out of it. Right. And so whether they're serving the defense industry or just the tech industry, wherever it is, I think you can find people of all backgrounds in both areas. You just have to look a little bit harder depending on what you're looking for in each city.

[00:08:00] Yep. And so it was probably not quite a year and a half ago when we last did this. And even though that's relatively recent, the impacts of AI in that time frame have been dramatic. I mean, even though AI had already been, quote, unquote, around in the post-Chat CPT era for a while by then, it does feel tremendously differently.

[00:08:24] So I really want to focus on how that is affecting what you're doing, both to the positive, creating new opportunities, maybe some threats that poses. But also because of your business and because of who you are, you also, I'm sure, have a lot of thoughts about AI in a more general sense and would like to capture those as well. But before we go there, bring us more up to speed on Quantometric. Where is the business now? How much have you grown?

[00:08:55] Are you still a small business? Are you a medium business? Paint the picture. Yeah, I think it's a great backdrop for our conversation. If you take us back a year and a half, things were going phenomenally well. I think there's my board member Lonnie Jaffe from Insight Partners and I published an article a few weeks ago about the uncomfortable middle and how I think that's disappearing. Because what we found ourselves in early 2015 is three quarters of growth.

[00:09:24] I love to claim that we've had 31 quarters of consecutive growth. It feels great. But if we're very direct in 2015, things are starting to flatten out. It became a little bit harder to grow because of both the economic climate and the AI climate that we found ourselves in. And I think this uncomfortable middle that we found ourselves. Tell us what you mean about uncomfortable middle.

[00:09:46] Yeah, I think it basically means that we had enough momentum that we didn't have to panic because of the economic climate we were in and this world of AI. But it also, you know, we didn't have enough of a breakaway velocity to find ourselves in the lead. And I think that you could say maybe we're in the lead in our space, but not, you know, when your competitors aren't doing well, it's him. At least I'm doing better than them. That doesn't sit well with anyone, right? It's not where we're not sure what to be. It's not where we live.

[00:10:15] You know, high-digit growth is what people get excited about. And so that's the uncomfortable middle that I think a lot of SaaS companies have found themselves in the last 18 months, some of which, and I'm excited to say Quantum Metric, has figured out how to cross that chasm. And some of which are still trying to navigate because AI is a difficult beast. There's a lot of folks that are coming to meetings with a great AI demo.

[00:10:43] But when rubber hits the road and can they deploy it and get action and customer satisfaction out of it, I think we're seeing, you know, this bell curve where everyone in the middle is not quite making it. And so you either have to be on one end of that bell curve to success or you're going to find yourself where it's flattened out and it's just like it's a zero-one situation. So what's exciting about giving you the update about Quantum is that we are seeing that reacceleration.

[00:11:08] We're seeing customers engage with us, accelerate their relationships with us, both on new conversations and, you know, new prospective customers as well as existing customers expanding relationships, extending their contracts to three to five years. I think those are all phenomenal signals for me that it's a durable business, that our customers are finding tremendous value in it. But that wasn't a given. And I think that's what a lot of SaaS companies are struggling with in this AI world.

[00:11:37] You know, you have to – I think it's, you know, if I reflect on what we did over the last 18 months, it feels a lot like we refounded the company. It's almost like we started their product base from scratch. If we could start over, what kind of product would we build in an AI world? And we went out and built that and then are transitioning our customer base over to this new AI product. And, you know, we can talk about the world of agentic AI and how that empowered us, but I don't want to go too far off, you know, into the conversation.

[00:12:04] But accelerating business, really happy to have crossed that chasm and that channel. And I have to give a shout-out to not just Lonnie from Insight but also John Chambers on our board because John has a particular history of seeing around corners and seeing around market transitions. And, John, quickly for the audience, John Chambers' background. I know John well from my really level three days, even more so than Zayo, and was fortunate enough to meet him a few times.

[00:12:33] But John is a legend in his industry. And tell us who John is. Yeah, John was CEO of Cisco. And I might get it wrong. He's either 23 or 24 years. A long time. CEO and chairman of Cisco. I think 26 total years at Cisco. And he took the company from $70 million in revenue to $40 billion. And he was known as just a great sales-oriented executive.

[00:12:57] He really knew how to run a professional sales force with creating the right systems, processes, incentives, motivations, culture. He really was a leader who thought about how do we sell as our first skill set. See, and that's what makes this conversation so interesting, Dan, is I think of him exactly what you just described. So you have this sales culture process.

[00:13:26] I mean, really, world leader. And I think if you go back through the history of the world, you might find him, I don't know, top five CEO. Could be top CEO of the world over time. How do you put that in the box of he's going to shape your product strategy, right? I mean, he's an incredible leader. He's not known for being this engineering direction guider. And here he is coming to us and saying, hey, what's your AI strategy?

[00:13:55] You're going to need an AI strategy. I was like, John, you know, I'm a product engineering person. And you're this culture leader and, you know, phenomenal CEO. I've got this, you know, product vision, product roadmap. But I think the funny part for me was he ended up calling my CMO, my CTO, my CPO. And I think what really hit me when he called my CFO. And he would ask the same question to all of them. He had already asked me the question. I said, John, I got it. But he asked, hey, what's your AI strategy?

[00:14:22] And when each one of them came back and told me what he had asked them. But I think it hit me strongly when, you know, why is John asking our CFO for the AI strategy? And that's when I realized that I think he's seeing around the corner and I'm not listening and he's sending a message. And luckily, the message was finally well received. And it was about two and a half years ago that we started, you know, developing and evolving our AI strategy. We had our first product launch in 2024 with Felix AI.

[00:14:51] It started to have, you know, a growth impact to the business. And then when we launched Agentic AI this year, that, you know, late last year, that's when we really saw that growth impact the numbers and financials of the business. And so I have to give a shout out to those two board members for helping push us to essentially re-found the company, re-redirect our product energy to something that the market needed from us. And I don't think it was obvious at the time.

[00:15:22] Yeah. And we'll come back to that in a second. One artifact of AI is a lot of decision making kind of gets frozen with customers and with investors in particular. Investors, I'll talk about that first because we've touched on that before. Investors often aren't sure what to fund or what to put more money into because things

[00:15:51] have been moving so quickly. You know, especially when it comes to their existing portfolio. Well, actually new investments too, but the existing portfolio is to what extent are these companies vulnerable to being displaced because of AI? And if you go back six months ago, nine months ago, the answer was, ah, we don't know. In fact, we're not even sure we understand the full gravity of what's happening. So I think we're starting to come out on the other side of that.

[00:16:19] And certainly for funding new companies, unless you're funding a person who you know is going to figure out how to leverage the should of AI, it's really hard to fund a new company too because funding a company is really hard right now because you're not sure what else is going on and how people are applying AI. But what you brought up is customers. And the same is true with customers.

[00:16:45] A lot of customers are like, well, we're not really sure what direction we should go right now, we have our existing go-to strategic suppliers, but we're not sure that we should be staying with them because AI is changing more quickly. Maybe there should be other platforms we consider. Maybe we should do more in-house using kind of self-built AI tools. So there's a long period of time that my guess is most SaaS companies are still dealing with

[00:17:13] this where their customers are in a very indecisive mode. And yeah, they'll continue to use your product, but they're not going to make longer-term commitments. They're going to be a bit slow to get deeper embedded into using while they're trying to get their heads around what their AI strategy should be. And do you think that's part of what you were experiencing? Yeah. I mean, I think there's eroding switching costs.

[00:17:35] And so it's easier than it's ever been for enterprises to place their bet on the real winner because sometimes it's like, well, it's a little bit better, but it's going to take us six months to move. And I don't think the six months of effort is worth that little bit better. So I'll move on and not make any decision. So I think there's always been that. And now that that has become a smaller cost to switch, I think people are looking around and saying, okay, who's the real winner in this space?

[00:18:04] So I think the best way I can describe it is I remember working with a retailer back in January, and I've been looking to win a relationship with this retailer for the last three or four years. So I've been trying pretty hard. And we got to the end and we did a POC and things were going super well. And they said, you know, but I'm not quite sure if your AI is the best.

[00:18:28] You know, and so we took a meeting with their AI leadership and we did a 30 minute demo. And I asked at the end of the demo, what do you think? And they said, hands down, without question. Now, I've seen every other vendor and their top best AI presented by their CEO. Hands down, without question, you are leaps and bounds ahead of everybody else. That's got to be a great point. They signed a three-year relationship with us.

[00:18:57] And it just feels really good when these moments are happening. Like you get so many signals. And I think for investors, it is difficult because I have a lot of faults, but probably one of my favorite faults is I love to do a product demo, right? And I realize when you're in conversations with investors, they don't really care about the product demo, but I get faulted for, hey, let me show you the demo all the time. Why is that? Why do you consider it a fault?

[00:19:23] Because of what you just said, because I think that investors are not the keenest at determining who's the best. You already said who's the best. Customers, right? When they put their money on the company, they are knighting it king. They are saying, this is the winner. They want to know our customers. This is the right product. Our customers react, not how you fish it. No offense, investors, and you might count me as amongst an investor, but what do investors know about the best? Customers do. They choose.

[00:19:50] And so I think we should all default to our customers buying. And we can go back to this conversation in a minute because I think we're seeing a different kind of adoption and acceleration in AI-driven companies where they're getting to $10 million in AR way faster than we have seen in the past. And so is that the right signal? Right? We can come back to that. But I think if you look at our customers placing the bets, customers know best. Customers can see that smoke test. You know, we've definitely seen lipstick on pigs coming out and, you know, hey, look, it's a gentic AI.

[00:20:19] Is it? Look under the hood. And so I think the real winners are going to be our customers betting and are they placing long relationships on this? And I think customers will always be the deciding factor of what's real, what's not, what has durability. So let's, before we get into which, you know, is really the story here, which is your product innovation, I want to first get into your tech stack and both from a, as you look at

[00:20:46] your tech stack and what you've done with it over the past year, year and a half, what has changed more from a, you know, how you go about doing business and how has that changed your cost structure, but also what has changed in terms of what tech stack are you using to develop your products and your solutions? Yeah. And as a reminder, we're in the world of analytics.

[00:21:11] And I think there's a lot of people that think, you know, regardless of the foundational model that we choose, you can place AI on top of data and all of a sudden magic happens. And it kind of happens a little bit, but not to the extent that delivers on people's expectations. So I just want to state that fact because I think that's, as we discuss, you know, our product innovation and our core stack, I think you have to understand it just doesn't

[00:21:39] work with collect the right data and then have AI on top of it. And we can talk, we can go into more detail as we talk about product innovation. But as far as the question- But I would imagine like collecting the right data, getting your, you know, getting the data into, the data that you need into the right databases is kind of the foundation on which to build. And perhaps a lot of people aren't quite sure how to do that. They're not effective at doing that.

[00:22:05] So building tools on top of it when you don't have command and control of all the data that can matter, you know, is probably a mistake that is being made up there. It warms my heart, Dan, that you say that because I love when leaders understand that statement. It really does all start with data. We use the word context. We use data, maybe sometimes interchangeable. And sometimes that's true. It all starts with having the right data.

[00:22:30] And I think when I reflect on what we did correct 11 years ago when we founded the company is how do we make sure that we have the right data? Because as I look at our peers and where we have this advantage, it does boil down to collect the right data. And so once you have- And put it in the right spot. And that goes to your question of what was the stack. So we decided in 2017 that this relational database we used, MySQL, MariaDB, it wasn't going to work.

[00:22:59] That kind of transactional database did not have enough power as we collected an enormous amount of information to answer the questions our customers were having with their data sets. So we took a gamble. And I'll tell you, I still look at it. I remember how nervous I was about the gamble. But we switched to Google's BigQuery. So there's this petabyte scale analytics database that would allow us to answer really crazy questions. And if you're a database person without indexes, and I'm a database person.

[00:23:28] And I'm sitting here thinking, I'm like looking for all the right indexes. How do we get the right performance out of our database? And I'm like, okay, a database without indexes, this could be really powerful. We don't have to have a predetermined idea of what people are going to ask out of their data. You can ask anything and it will all be performant. So that's what a huge advantage over the last eight years has been for our business, has get a really strong database. So you collect the right data, you store it somewhere, and you make the accessibility of that data highly performant.

[00:23:58] And I think that's really separated us from our competition over the last eight years. I think the storage and queryability of data has been commoditized over the last two years. So you have competitors like Snowflake, Databricks, and more that have enabled companies to access their data faster than we have in the last decade. And so I think that's a bit commoditized. So I think how our business really runs is it's collect the right data, store and query it,

[00:24:28] and then do analysis on top of that data. And that comes into things in the last decade, which has been like dashboards, reporting, et cetera. And I think we're seeing a change of that analysis phase with AI, which I'm happy to talk about. Yeah. Well, let's stay on the tech stack. Have you changed in the last year or two because of AI? Kind of what? Yeah, of course. What the lower layers of your tech stack are? Of course our engagement with, and we use Gemini, and we've used Gemini since the beginning.

[00:24:55] And we did try, and we do try foundational models all the time. We're kind of rotating, whether it's at our development layer, our individual contributor users like myself, or what our product uses. We have tried all of the products. And at our core product level, we use Gemini. And then the rest of our stack. And why have you stayed with Gemini?

[00:25:18] I think at the end of the day, foundational models, there's a little bit of people getting a little bit better than their peers. And I think Gemini is a winner right now in that world. But consistently over that time, Gemini has been the most cost-effective foundational model. So I think it's been, you know, we've got to run a business. And so I want something that is highly effective and very cost-efficient. And Gemini has hit that mold over and over.

[00:25:45] And that it's going to stay one of the leading platforms over time as opposed to bump up into lead and then bump down. Yeah, I think so. And then I think there's a level of trust and transparency. You know, we work with businesses that have highly sensitive data from healthcare in the U.S. to global banking around the world. They care about their data not being mismanaged. They need sovereign ability of that data in the geography that they're in.

[00:26:13] So Google has a number of data centers around the world that allow us to meet the bill of our customers' needs. So I think like all of that combined is why we choose Gemini over and over. Gotcha. Gotcha. Okay, so you're a Gemini shop. So let's start. Were you able to use AI to take costs out of your infrastructure? We have used AI to take costs out of our business.

[00:26:42] Out of our infrastructure, I haven't been as sensitive at like, let's reduce our cloud costs specifically. I think there's about 7%, you know, percent of our overall costs that we could actually get out of our business. But I constantly fight mentally, just like a brain experiment. If I spend time saving that money, that's time I'm not innovating. And so I constantly push myself to make that decision. And every time I flip that coin, it lands on innovation versus cost savings. And I feel terrible for saying it.

[00:27:11] But if it's only 7%, you're making the right choice. Yeah. It's like we hear a lot of people like you who are taking big chunks of cost structure out by moving from what looked like permanent platforms that they were wedded to for a long, long time to, man, we gutted out of that and went to something and, you know, saved a lot of margin. And that positions us better for innovation. But it doesn't sound like that.

[00:27:38] I think it's on the horizon where we actually spend a lot of money of our costs. Like when people use our dashboards and they use them so heavily that there's a large amount of our costs that's spent in querying our data. And as we see a transition of people not using dashboards as much, I think that we're going to see people be more targeted about what information they're querying. And that will end up reducing our cloud costs. So I think that's kind of coming out later this year. Gotcha.

[00:28:08] All right. So let's get to the fun part. Yeah. Let's get to innovation. So walk us through the journey of the last several years. You mentioned things that happened along the way as you've begun to leverage kind of the new world of AI to bring new capabilities, new products, new features to your customers. Yeah. Just transitioning. I mentioned the word dashboard.

[00:28:31] And I think we're looking at the end of life, you know, maybe for 89% to 90% of our usage of dashboards. And think about it. Like what did you do in a dashboard? And as we as a company went through this exercise of how we are going to innovate, how we're going to adopt and engage in agentic AI, I started realizing that everything that we do for our customers is helping them answer a question. So we create a dashboard. We have a thought about, okay, I need to have this perspective.

[00:29:01] I have a question I need answered. And I'm going to pull these pieces of data to help me answer the question. And I guess maybe if I back up a step and I'll pick on a retailer that we can do this example in any of the industries that we serve. If we go look at the overarching question that people are asking about their data, how's my business? Am I going to hit for a retailer? They're thinking about, am I going to hit my sales plan today? And then imagine if it's yes. Great. Cheer. That's awesome. We're going to hit our sales plan today.

[00:29:30] But on some days, the answer is no, we're not. You know what the next question they ask? That. Why? Right? Like, why am I not going to hit my sales plan? And they want to know if it's something that they need to resolve and act on or it's just market conditions and maybe it warrants no action. Let's maybe take this example and be a little bit more tangible because you're saying today, which is interesting. Am I going to hit my sales plan today?

[00:29:58] If I'm not going to hit it today, why didn't I hit it today? But, you know, a lot of businesses think about that as, well, I won't know until the end of the month. Am I planning on it? A lot of happens at the last few days. But you're talking about like real time. You know, I imagine AI is giving you a lot more real time, not just understanding of, you know, how are you tracking, but on the question of why, what's really going on, what's happening,

[00:30:25] you know, like early detection, early feedback loops. And the ability to master that probably makes a huge difference in who the winners are going to be versus who are going to be left behind. Yeah, let me describe a positive scenario and a negative one. I was on a call with a retailer, a leader of e-com. And as I was jumping on the call, I noticed that their sales were up. They're doing well. And as I joined the conversation, I asked the head of e-com, do you know why sales are up? I actually didn't know the answer.

[00:30:54] I was just being inquisitive. And I wanted to demonstrate the power of AI. And the individual shared that sales were up because of the election. So this is like November timeframe of last year. And I was like, okay, I have no idea. I mean, I don't know if your business is up and down related to the election. So I asked our, what we call Felix AI, our agentic AI solution. And- Tell us more about what you mean by Felix AI.

[00:31:23] So the fun part about Felix was, and so we have this agentic AI, we had AI, generative AI solution we called Felix. And then now we have Felix agentic. And- But before there was Felix, what, how did it happen? So what was the before? Yeah, we were- Felix. We were working on the, you know, honestly, it comes back to John Chambers. What is your AI strategy? And it began with tinkering.

[00:31:50] I don't think that leaders can say, hey, go use AI, come back to me, tell me what you find. So it was myself, my co-founder, David, and I, at first at his house. And then he came over here to Boulder. We set up in our house. And I think we hit this eureka moment. I'm like, oh my gosh, it's working. And we're kind of testing it. And I'm like, what should we call this? And we had this beautiful fox that had come and visited our house every day for like the last month. And I look over and Felix the fox is sitting outside of our door. I'm like, let's call this Felix.

[00:32:19] And so if you go look at our product, you'll notice kind of an icon that looks like fox ears. And that's Felix. And they've got some beautiful pictures of Felix. It's just a heartwarming story about how do you name a product? It's just Felix appeared. And now we call it Felix AI. So when you had the breakthrough, and this was the first phase of AI, what were you able to do that you weren't able to do? Yeah. So Genitive AI, you've used it. It's really good at summarizing.

[00:32:46] You ask Genitive AI to summarize a book and you have three, you know, hey, can you summarize in three sentences, three paragraphs, three pages? It does a phenomenal job with all experiences. And if you look at digital experiences, so when you're on that website trying to make that purchase from retail or maybe trying to change your flight or, you know, make a deposit at a bank, whatever that digital experience is, we can watch that experience and, you know, spend 10 minutes trying to understand what happened to Dan.

[00:33:12] Or we can have Genitive AI summarize Dan's experience into two or three sentences, depending on what we want out of it. It could be a product perspective, could be an IT level to support perspective. It could be a contact center perspective. You know, Dan's calling us right now because he's trying to change his flight and he's getting an error in the app. I mean, imagine that context pass that we could have by summarizing Dan's experience. Obviously, there are different audiences that need to understand Dan.

[00:33:37] And so that was what we developed that first Felix AI, we call it now Felix AI summaries. And it was phenomenal. It took what maybe took a human 10, 20 or 30 minutes to digest Dan's experience into two or three seconds. So that was a phenomenal leap for us at Quantum at being the first market. Were there any early moments where you shared that with customers and remember their reactions? Honestly, Dan, still today.

[00:34:04] Still today, we show that to a customer like their mouth drops. Like, oh my gosh, I can't believe we can summarize. Because people have been on this journey for 20 years trying to understand what's happening in digital experiences. It happened 25, 30 years ago looking through log files. Still today, we see organizations going through log files to understand Dan's experience. It just doesn't work. You see people spending five to 10 hours doing this.

[00:34:28] And so coming back to this experience of the retailer, they're saying that their sales are up because of the election. So I asked this Felix agentic AI, why are sales up today? So, and I know I keep interrupting you. No, it's okay. So you skipped from the first version to the agentic version. Walk through and kind of, you know, so people can follow what you're talking about.

[00:34:59] What did it evolve to when it became? Yeah. So we had this generative AI solution. We started seeing a sales lift. We started seeing our largest land account happen because of our Felix AI summaries. This generative AI solution was so innovative that, you know, none of our competitors had anything like it, that when people saw it, they said to themselves and us, you guys are the innovators in this space.

[00:35:24] And so, and it's great that they say it, but they started falling with contracts and relationships were expanding. So that felt good. The challenge with it was it felt good, but not so divisive that it accelerated our business by 50 or a hundred percent. And so in that 2024 period, we realized we had to do more. And I remember the moment that I heard of agentic AI, and I'll tell you, I kind of, I feel

[00:35:54] bad for saying it out loud. Oh my God, agentic AI, what is this? This is just another buzz term that people are putting. I didn't understand what agentic AI was at the time. So if I were to describe to my children what agentic AI is, I would say we've all had this experience with generative AI and it's really, really intelligent. I mean, it's like a PhD in everything. How can it not answer any of our questions? What we've come to find is it's really not good at thinking through a 50 stage problem.

[00:36:20] And so if we can break apart the problems into different stages and kind of gate check how it gets to the end of each step of an answer, we can get to a more refined answer. And so we kind of see this in thinking mode and checking itself. But then also if we could empower AI with tools. So maybe we can connect it to the internet and it could do a weather search, a news search.

[00:36:46] Maybe we can connect it to analytical tools that can query different data sets from different parts of our organization and so on and so forth. We can piece together kind of the operational effectiveness of an individual, an analyst that might be on your team that asking different questions. And so that's what agentic AI was for us. And we began, and I'll tell you, it started again with my co-founder and I in a room. It wasn't give this project to somebody else and hope and cross their fingers and they come back with success.

[00:37:11] It was, I think it starts with leadership and being an engineering product led, you know, two founders. I think it had to start with us. And so that's what we did. We built this agentic AI platform and here we are, you know, in November and we had been building it for about a year. And here I was with this retailer and I'm going to come back. I'm going to finish this story, Dan. And so we asked Felix agentic AI to help us figure out why our sales up. And it spends about two minutes to get to the answer.

[00:37:39] And it says, look, you, it seems like you ran a social media campaign. I'm seeing double the amount of traffic landing on women's outerwear. We had connected it to a tool that could query the weather. It got cold outside recently. It was November. This is why your sales are up. People are buying women's outerwear from this social media campaign. And the, the leader from a VECOM was like, I could see it on her face. It was, she didn't say the words, wow, but I saw it on her face. Wow.

[00:38:08] Now, after I got off the call, I wanted to just, I'm like, I don't know, maybe, maybe it is partially the election. So I said, are sales up because of the election? And this is the cool part about foundational models. It was able to translate the election to, I'm in the US, here's the election date. It queried the data before the election, after the election. And it's like, look, sales are up during the election period. But as per my previous response, you had more, you had a very successful social media campaign

[00:38:38] driving more traffic. They landed on women's outerwear. People bought a lot more women's outerwear. This is why your sales are up. And that individual, after cyber, you know, peak happened, emailed me and said, how do we get this? That was really cool. And now they're super engaged with us. So what do you, what all do you think the agentic I did in that example, what you mentioned looked at the weather, looked at the ad campaign, but what else do you think it looked at? Like what, what probing did it do?

[00:39:07] And this is the part that I think is so critical. Like if I gave you an answer and it sounds reasonable, do you believe me? I, I, you know, maybe because you know, we have a great relationship, but I don't know. I don't have a great relationship with AI. I don't trust it that much. Um, and so I think transparency and trust is built over. And if you remember back into like maybe fifth grade, remember when you had an answer in math and it could be the correct answer, but your teacher didn't give you full credit because you didn't show the work, right? Like I don't trust you. I don't know how you got that answer.

[00:39:36] And if you didn't show me how you got to the answer, I'm not going to give you full credit. And I think the same thing with AI. I think we need to, as responsible deliver, you know, software companies delivering agentic AI solutions, we need to show our homework. We need to show how we got to the answer because if I question a human analyst, how they got to an answer, they're going to give me the facts. They're going to show me how they got there. I think AI needs to do the same thing. And, and it's not that we were so smart and thought of this, uh, you know, early on is

[00:40:03] we were just giving people answers and you could see in people's faces, okay, that's cool. I think, but how we got that one of our breakthrough moments was we need to show you the work. So all of the queries of data, just the question that you're asking is what, how did you get to this answer? What information did you pull into to get to this conclusion? We show the entire time and we show the kind of summarize format of here are the pieces of information that went into our thought process, but you can expand it and really go into like,

[00:40:31] what were we thinking as we develop this strategy to get to this answer? And that is how we're getting buy-in from our customers. Oh, that's the same way one of our analyst team members would have thought of this. And I think another part to understanding of how to, to implement agentic AI successfully is we really don't know how to do this successfully at every organization. There's no way. How could we think like an analyst at every different retailer or bank or airline and telco, they themselves in the same industry think differently.

[00:40:59] So what we've come to learn is I was on a call with the CMO of a retailer in the U.S. the other day and, and he had asked me, how is our new checkout doing? And so I said, okay, let's, let's find out like, how is my new checkout doing? And it came to the wrong answer, the RAI. And it had said, okay, well, I found V2 checkout. So that is conclusion was the new checkout. And I asked the CMOs, your new checkout, V2 checkout? Oh no, that's the new and the old checkout.

[00:41:29] I'm like, well, how do you know if it's a new checkout? Oh, we have this one attribute called platform. And if the platform equals Vercel, that's new checkout. And if the platform equals SFCC, that's the old checkout. So what we learned was give the ability to teach AI, just like you would a level one analyst. Level one analyst doesn't know. I was a level one analyst in that room. I didn't know what new checkout meant. And so, you know, the CMO had to teach me. And so I taught Felix, new checkout means this event equals this attribute value.

[00:41:58] And then I saved it. And now anyone else that goes into this platform and says, how's my new checkout doing? Now Felix knows how to translate that into the right query. And I think, again, showing you the homework, showing you that whether it was V2 checkout or platform equals Vercel to figure out what new checkout means. I think that's how we build trust is just show our work. And so I can have believability in the conclusion that the AI is coming to. Yeah, you touched on this.

[00:42:25] I was in a conversation, dinner conversation last night that had a lot of, I'd say, bigger company executives were mostly around the table. And most of them were low tech. And they asked me a question toward the end of it. And I said, well, I said, first of all, like, I don't want to be disrespectful to anyone in here. But the first thing you guys have to realize is you guys are all low tech executives.

[00:42:52] And we're in a world, rapidly changing world right now. And the conversation was AI. I said, you know, the companies who are moving the most decisively about how to leverage AI are led by people who are very comfortable technically. More so now, that's more important now than it's ever been.

[00:43:19] And maybe that will change three years from now when AI becomes a little bit more of the norm. But if companies are led by non-technical leaders, they can talk about AI. They can direct people to do AI. But most of them don't really get it. They don't have intuition. They don't have the hands-on understanding. In fact, they can't even understand the language to have the right conversations with their people.

[00:43:46] And my guess is, you know, the best, you know, technical people with AI, you know, quickly find themselves into environments that are very tech in nature. You're probably getting more middle-of-the-road people who look like rock stars if they're in an environment where the leaders don't know the difference between a rock star AI person and a middle-of-the-road one. And I think that's going to create a lot of positive momentum for some companies and headwinds for others.

[00:44:16] And it's going to take most of the ones with headwind a while to figure that out, in part because it's personal to the leader. I don't know if there's enough time for them to figure it out. That's a big question mark I have in my head where, you know, we've – and I'll knock on, you know, non-wood, I guess. But, you know, we've crossed that chasm of making it with AI. I'm seeing in our numbers. Our financials, we're accelerating. We're having – you know, we're having our three best quarters in a row that we've ever had in the company's history.

[00:44:45] We definitely have the best Q1 in the company's history in sales, the best Q2 in history as we – we still have five weeks. We already have our best Q2 on the books right now. And I think there's a good chance that it's actually going to be the best quarter in the company's history. And this is all coming off of, as I mentioned as we started our conversation, three quarters of, yes, growth, but not the growth that I'm proud of, right? Like, just tremendous growth.

[00:45:12] And we're having that reacceleration into tremendous growth. And I'm really proud. And because I think you can have a quarter that hits in a business like ours. You just get one big new customer, and it could be so big that it just sets you up for, you know, a good quarter. But what I've seen historically, if you look at it, you get – in enterprise sales, you get these lumpy – oh, maybe you have that big whale count that lands in one quarter, and you have a great quarter because of it. But the quarters before and after aren't so great.

[00:45:40] To have three in a row, we just have never had this tremendous acceleration, this tremendous success. So I think it's telling a story that the product is really hitting a nerve that our customers – that they want a solution to, and we've delivered on it. So it's exciting to have that. And I wonder, the pace of change that AI is demanding, will companies have enough time to figure this out? To your point, I think it's – No. A lot will not. Maybe, but I think it's a tech leader. And I'd add one more part.

[00:46:09] I've had a lot of pride in what I realize is, like, I'm a – I like business. I like being an entrepreneur, and I really love tech as well. And I think that combination has given me a bit of an advantage in our space. And I see people that are really technical, you know, in our competition. And I see people that are very business savvy. And I think the combination of the two is what's required in this fast-paced movement being pushed by AI. So I think the leaders that can understand the technology and the business really well,

[00:46:38] I think those are the ones that are going to be able to make that crossing the chasm happen fast enough to be successful in AI. And I'm a bit worried for the other two. For people that are just techies or people that are just really business-oriented, how do they lead an organization through this change? When you think about your closest rivals and the profile of those leaders, how many of them feel like they're the appropriate balance between tech and business

[00:47:05] versus in what cases you have to say who they are out loud? Do you look at them and say, man, that leader is going to not understand how to use AI because they're just not technical enough? Or, yeah, they're very technical, but from a business standpoint, you know, they're just not ready. Yeah, I think it's both of those on either side of the coin. And I think my perspective doesn't matter as much. It's the customer's one. I'll go back to that big retailer that said,

[00:47:33] you're hands down, you know, leaps and bounds ahead of your competition. I think that's the message I need not, you know, I think founders have these. But when you look at them and you'll find, because you're always looking at your rivals and who the leaders are. Well, I would say, and you're not wrong on your statement. It's something I've taken to heart since 2015, 2016. Winners focus on winning. Losers focus on winners. Now, that's not to say, there's part of a winning strategy to understand

[00:48:02] where the competition is, to be fair. But I don't go into our all-hands meetings talking about, you know, our competition this, competition that. I try to focus on what are we doing well and what do we need to improve and get our teams rallying around those opportunities. At the same time, I do. And I actually maintain good relationships with these leaders. And I have a high respect for them. I mean, they're doing hundreds of millions of dollars of business. So this is nothing to sneeze at. But I do look at them and I put them into a box of,

[00:48:31] some of them are strong, you know, incredibly great marketers, business leaders. And some of them are really, really strong technology leaders that maybe don't understand the space that we're in, which is solving for the enterprise. And I just don't see anyone kind of in that middle. Like, you know, I would say the leadership at Quantum is. And I think that's giving us an advantage to really break away in this moment of AI with having the right product, delivering in the right fashion to our customers' needs.

[00:48:57] And it feels really good to have these three great quarters in a row. So look forward for your business, focus on quantum metric, move forward a year, two years, three years, so relatively near term. How do you see your product journey evolving with what you're envisioning AI to enable? Well, I think consolidation of our business and our adjacent businesses is accelerating.

[00:49:27] You know, case in point, when we... So what would be examples of adjacent businesses that, you know, you would... Yeah, I think it's just like, who, you know, who goes to market with us? And I just ask our customers, what products do you see adjacent to us that's doing something close enough to what we do that you're not happy with? And I think there's a very clear market message on one easy one,

[00:49:53] which is we've all followed Toma Bravo and this Medallia situation. And this voice of customer is something our customers have been asking us, like, could you solve for voice of customer for us? And I'll tell you, every time I've gone down that path over the last five or six years... What does that mean? Yeah, voice of customer. Some people say surveys, you know, right? Like, you know, but it's a little bit more broad than that. But I think for the everyday person, surveys come to mind. We understand, like, you can survey people.

[00:50:19] And I think if you look at what we do, we're looking at observing customers' behavior and understanding what's happening in their journey, how to improve the experience. That's indirect signals. Sometimes there are direct signals. And I don't know, I'm a big Seinfeld fan. Are you a Seinfeld fan? Oh, yeah, you know, enough. Well, there's this Seinfeld episode. I think you can find a Seinfeld episode that can describe anything that we want to talk about. But there's this one with 222 Film. Do you remember this one? No.

[00:50:47] Well, there's this beautiful one where Kramer gets like 222 Filk, I think, is a phone number. And people keep calling him. Do you remember this 222 film back in the 90s? Yep. Where you didn't call and look up where movies are showing. And so, you know, he's trying to figure out what people are pressing on the keypad, on the dial tone. And he's like, why don't you just tell me what movie you'd like to see? You know, getting... Why don't you... Like, just getting a direct question and a direct answer to help navigate how to solve for this caller.

[00:51:14] I think that's the same thing that sometimes surveys can do for you. Like, why don't you just tell me the problem that you have? Right? Because what we're doing with quantum is we're observing. And in many cases, we can observe and identify and quantify and prioritize the exact problem a customer's having. Sometimes it's just easier. Just tell me the problem that you have. And so I think a little bit about that Seinfeld episode. So, you know, what we saw with Toma Bravo medallias, they spent, I think it was about $6 billion. They got creditors to help fund that acquisition, taking Medallia private.

[00:51:44] And in the last month, Toma Bravo gave it back to the creditors and wrote off $5 billion of debt and said, you know, good luck, right? And so I think that was... Luckily, about a year ago, we decided... Why did they... I didn't follow that story. Yeah, well, yeah, why did it happen? I think the demand for voice of customer, the willingness to spend on something that I think the technology merits of that business aren't that strong, that there's just a competition coming from every direction.

[00:52:14] And I will chalk us into, you know, throw our name into that hat. We had the founder of online voice of customer. It was... The first company that did this was Opinion Lab. One of the co-founders of that business has been part of our business since 2017. And so, you know, we kind of looked, you know, as AI was empowering so much faster development, so much faster change, we're like, you know what, maybe, you know, over the last six years, we were like, well, it's kind of hard to do all the different pieces of voice.

[00:52:43] It's not just a survey, by the way, because that's really, really easy. It's way more complicated. And every time I looked at it, I'm like, well, that's going to be a big distraction. There's so much engineering effort to go deliver on that. Now with AI, and I'll give you just one example, text analytics. I don't know, it's kind of hard. Ten years ago, five years ago, you look back at how hard is it to analyze text analytics in every language possible. Foundational models do that for free. I mean, a couple pennies. They can do text analytics, and they can summarize across 10,000 responses in any language,

[00:53:12] and help you understand and put things in different topics. That's phenomenal. So, like, the ability to do that with almost zero effort is really easy with frontier models today. So, we decided about a year ago, we're going to invest in that space. We brought the co-founder of that space to lead that part of our organization.

[00:53:32] So, what's phenomenal about that opportunity and that consolidation is just kind of stars aligning where you have this struggling business that has a billion dollars of ARR solving for the voice of customer market. And now here we are launching a product at the time when folks are saying the duopoly. So, there's Qualtrics and Medallia. They aren't serving us, and the costs are too high. We want a new solution.

[00:53:59] And they're already doing business with us, looking at it from the indirect signals, that observability of the user behavior, and they're trying to coalesce the indirect signals and the direct signals. And it just makes sense to consolidate that into a platform like ours. So, you know, as I look at the future and looking at adjacencies, that was a massive win. I think that we're also seeing a consolidation in, you know, traditional marketing analytics platforms. We weren't going to market and saying, let's go solve for this.

[00:54:29] As we demonstrated our capabilities in Agentec AI, all of a sudden, we started getting our enterprise customers saying, hey, can you help us consolidate? We've got multiple analytics tools. We have a tool for our product audience, a tool for our marketing analytics, a tool for our IT audience. We really want that all consolidated into one platform. We think you're that platform. And so we're seeing that as a big go-to-market motion for us.

[00:54:51] So as I look at two to three years ahead, I think AI is forcing this consolidation both from a product capability but also from the other enterprise side of, like, consolidation. I don't want three vendors. I don't want three training sessions. I don't want three contracts to negotiate every year. And I think we're going to see more across the entire landscape outside of our business as well, just consolidation of enterprise SaaS vendors.

[00:55:15] Yeah, so I had a similar discussion in the podcast a couple weeks ago. So customers are going to pick their winners. And when they pick their winners, they're then going to want the scope of what that winner does for them to grow and displace other perhaps deeply embedded solutions. They're like, I spent a lot of money on that.

[00:55:43] And boy, if your platform just did a little bit more, I could probably get rid of that whole thing. And it's a lot easier because now I don't have two platforms who I have to manage and figure out how they work with one another. And I think it's because of how AI makes it very easy to expand and extend what you do.

[00:56:04] You can encroach on each other's territories a lot more quickly and a lot more easily and with a lot more powerful outcome. It sounds like that's really what you're talking about when you're looking at adjacent areas. I think there's two parts to that equation. One, I think there's been a moat in complexity. And this shows up in organizations where you have people whose entire career is servicing one software platform.

[00:56:32] And the names can start to pop in your head when you have someone in their title, they have the name of the vendor platform in their title. And I think that moat of complexity is disappearing. Like people building their career on one software platform is no longer necessary because I think we can solve things with a genetic AI in non-complex ways. Let me give you an example of ways that we're tearing down some of this complexity. I used to get questions over the last 10 years.

[00:57:00] Can you integrate with such and such platform? Can you pull data or push data into XYZ platform? The way that happens now with a genetic AI is either we've already built the connection into that platform, which is great. Or, and this is the part that I think is just profound, take their documentation, copy a link of their documentation and paste it in our platform saying, please build a connector into this.

[00:57:27] In 30 seconds, a connector of the data that we want to send into that platform has been auto-created in our platform. Of course, it's like not our coolness. It's the foundational models underneath it creating a connection into that platform. Obviously, we have some parameters around what data we have and how we can send that information. But it builds a connector into any platform. So when people ask me, can you integrate with this? I share with them, I think we're talking about the wrong question. We can integrate with anything nowadays. You just need to give us an API spec, a URL, a login and password.

[00:57:56] And the answer is always yes. And when you start to say, well, I like to work with this platform because they have 100 integrations, that's been built into our mindset for the last decade about, oh, let's make decisions based on how they integrate with our stack. I don't think that's a problem anymore. And so when you tear down some of these moats that people have built their success on because, oh, well, we have 100 integrations, that's no longer the deciding factor. It's no longer a contributing factor to how we make decisions on which platform we go with.

[00:58:26] And as we do that, like how are you going to – I think what SaaS companies have to really focus on is what's our differentiation? And as you pointed out, I think for quantum, it starts with do we have the right data? And it turns out getting the right data is really, really hard to do in our business. I think, as I mentioned earlier, the commodity of storing and querying it has kind of been commoditized. And then I think there's this layer of differentiation on the analysis. We didn't really talk about it yet, but how do we do that analysis?

[00:58:54] We did talk about some of the really proof points of that analysis. Like you have to think about the – for us, Felix Egentic as the analyst kind of replacing the dashboard. We didn't talk about the agent, which is once you really see these amazing outcomes of questions and answers, you don't really want to be in our UI having a chat. You want these questions and answers happening 24-7. So you kind of want these agents in the background asking questions of your data set and then elevating answers that are concerning to the right teams.

[00:59:22] And then you want this partner where you can have this teachability to it, where you think of this platform, Felix, as a level one analyst and teach it over time. But I think that analysis layer that I just described is going to be another differentiator because I think all systems today haven't been equal. And I think when I reflect on that retailer saying that we have gone leaps and bounds above the competition, I think in part they're describing that having the right data is really important. But I think what they're really looking for is are you able to answer really complex questions that we have? And we've demonstrated that.

[00:59:50] Wow. That's crazy. And it feels really good. And again, I will say it's the numbers. The numbers show those proof points. Don't just get excited that a customer says this looks nice. If they put money behind it, if they sign on the dotted line, that's the proof point. Especially now because of the hesitation a lot of customers are having. If you're breaking them loose to that and they're starting to view you as kind of one of their stacks to build around, that's huge. Yeah.

[01:00:18] It's a powerful signal that they're asking us to consolidate the peers around us. Yep. So talk more about the analytics. And you mentioned that a few times, but we didn't dive completely into that. Yeah. I mean, I gave an example of a positive scenario where sales were up. I was talking to a customer, you know, in, again, I pick retail because I think it's easier for our listeners to relate to because everyone buys things online today. Okay. What they, they got an alert.

[01:00:47] And you mentioned this world, this word around real time and how we can have AI help people get insights. Because, you know, getting insights a week or a month or a day later is just too slow for the world that we live in. And so this, this head of e-com was sharing the story that they had gotten an alert and sales were down. And it was a Saturday. So they were out with their family and they ignored it. They got the same alert on Sunday and they said, you know what? I have to address this, you know, with two alerts, two days.

[01:01:16] I don't want to start my Monday behind trying to track this down. And she had shared with me that she really dislikes asking her team to work on a Sunday. That's family time. But two alerts, two days in a row, she needs to address it because her boss on Monday is going to say what's going on. So she activated, I would call what we all, you know, maybe connect with a mini war room.

[01:01:39] And there was eight people in the war room for five hours tracking down that simple question answer is, why are our sales not hitting our plan today? And they went out. And so we had, she had to ask her product team, her marketing team, her tech team, her merchandising team. She, there was people in there from voice of customer. So maybe someone just told them in a survey and the contact center, people calling the call center saying, I have a problem.

[01:02:06] So she asked all those teams to go through all their data sets, took them five hours. And here's the part that hurts the most. Sometimes you have, there's a real issue that you need to address. At the end of these five hours, the answer was her merchandising team realized that they had sold out of their clearance items. So prior to this moment, people were landing on their website and they would buy items that were discounted. When all the discounted items were sold out, people bought less. As you can imagine, their conversion rate went down.

[01:02:35] That alert that said your conversion rate's down was actually business as expected. But the problem was they worked on a Sunday, five hours, eight people who should have been with their families. And when I think about how can we use AI to solve for that, AI and what is actually the product platform that we built. We thought about this idea of, well, who do you call into that room to answer the question, why are we not going to hit sales plan? I just described those different personas. We have to look at our marketing data.

[01:03:03] How is our traffic mix? Is it normal? Is it was as we expected a week ago or yesterday or the last three weeks or year over year, depending on our comparison segments? How is our merchandising? Are people landing on out of stock? Are people buying a different product assortment? Why? What do we have in our data? As I described earlier, is the add to cart button not working? Is the payment type not working? Is the shipping address validation? There's all these different pieces.

[01:03:33] But with AI, we can have it go and sort through that data in seconds. I was in London last week, and I was on stage with a customer conference of ours. And behind me, I started off our conference with this sentence, the answers are already there. It's probably the most insulting sentence I could have on that stage because it's true and painful at the same time. The answers are in the data set. And exciting at the same time. It is. It truly is. It's exciting that they're there.

[01:04:03] You just got to figure out how to use the latest and greatest tools to bring them forward in real time. It's true. But the five hours, the eight people, it's like a smack in the face. I'd rather be with my family, and here I am combing through data to get to the answer that is already there. The AI agents can do that now. Exactly. And that's the beauty of this analyst, and that's the beauty of the analytics. And in the past, and what that eight-person team is, let's go through dashboards. Let's go through log files, depending on what data set we're looking for or looking through.

[01:04:30] And so what we can do now with analytics and analysis is that we can ask AI agents to assume the persona. Like, how does a merchandiser, how does a marketer think about this data set? And give us the answer in a quantified way. And then we have this orchestration engine on top of it, thinking about, okay, what did every one of my team members come back? Or, you know, we call them sub-agents. What did they come back and tell us? And then which one is the most meaningful, the most quantified impact? That's what I'm going to elevate to the user as the answer to this question.

[01:05:01] Wow, that's cool. So you must be more charged up about business now than you've been in a long time. I, you know, I could hear that in your voice. I could see it in your body language. You know, AI is really scary to a lot of folks, and it's bringing a lot of people down and, you know, overwhelming, you know, stressful. But for others, it's like a whole new world just opened up in front of them. I know I feel that way, and it's caused me to lose sleep because I'm, like this morning, I was up at 4, 4.30.

[01:05:31] I'm pulling out my claw and starting to do work. Not because I absolutely had to do the work, but because it, you know, it's like this whole new world has kind of opened up. I've spent a long time, you know, I think I've got to look back to really, for me, when I built really complex, interesting spreadsheets and just did it because it was fun doing it, you know, and that was a lot of years ago.

[01:05:56] That's the last time I felt this hypercharged about hands-on doing stuff, you know, on a computer. Yeah, I think it's both exhilarating and frightening at the same time. I think all of us are having these different emotions. I guess I give two examples. I forget the exact words of the quote, but I came across over the last two months a Steve Jobs quote around, you know,

[01:06:19] the evil that happened at Apple when he left was that people believed a great idea was good enough. Like, that's how you create success, and it was way more than just a great idea. It's really about execution. So I think part of the frightening thing is the pace of change is moving so quickly that we must execute. Like, it's not a great idea that's going to win this market. It's a great idea with great execution.

[01:06:46] And so that has been a bit of an exhausting moment. So, yes, I'm excited without question. And then I remember talking to a team member who was helping us lead this AI project, and I was just like, look, I said, I'm sorry. I'm sorry. I know his wife for 20-some years. I'm like, can you please tell Julie I'm really sorry about the demand this business is having on your personal life? And he said, Mario, don't apologize.

[01:07:11] I'm up at 5 a.m., and I'm working, but part of the reason I'm working at 5 a.m. is I'm so excited about what we're doing. I'm moving at a pace I've never been able to move at because of the speed at which AI is allowing me to develop, allowing me to create, allowing me to take product ideas and actually turn them into a real product. So I just wanted to share, you know, it's exciting and exhilarating and frightening all packaged up in one piece.

[01:07:41] That's the world that we live in. There's really no choice, but it really is not just about a great idea. It's really about great execution. Yep. So let's maybe change the conversation a little bit and get away from quantum metric and look into the future around AI in a more general, more community, more society,

[01:08:03] you know, whether it has to do with kind of, you know, kind of the experience young kids, you know, are going through, whether it's older adults whose career journey is permanently being disrupted, or it's just how society works as a whole.

[01:08:27] Like when you opine about this rapidly changing world that we're staring down with AI, where does your head drift toward? I think my process of trying to consume what you just said started about a year ago. My 16-year-old son said to me, Dad, what job is safe from AI? And every time I tried to answer the question, I'm like, well, I could see that one being challenged as well.

[01:08:57] It was a frightening moment. And to make this more human, because I hope I'm human, I remember going to my board the next week, at a board meeting the next week, and I said, guys, am I going to be replaced by AI? And they laughed at me, and we had a great discussion. And it definitely instilled some confidence, and I shared that story with my team. Like just in all transparency, like I'm in the same boat. You know, who is Mario, right? And can AI do as good a job as Mario, maybe, in some scenarios? I think I've had an honor of a lifetime.

[01:09:26] I didn't get to catch you up, Dan. But last weekend, I went back to my alma mater at Penn State and gave the commencement speech to the Eberly College of Science, the college I graduated from. I'm giving the commencement speech at University of Chicago in a couple weeks. Congratulations. It is such a lifetime honor, I'll tell you, to be in front of the students and their families and share a little bit of your story and perspective. And of course, my opening content was about AI. Now, I don't want to spoil it. I want you to go back and listen to it.

[01:09:54] But I'll tell you the ending part of how that message was sent. I told three stories, but the first story ended with, you know, this vote of confidence that chaos that we're experiencing does not kill opportunity. Chaos is what opportunity is made of. And think about all the shifts that you've had in your lifetime, the ones that you worried about, the ones you were frightened about.

[01:10:22] And think about what came out of those moments. Massive opportunity, massive kinds of companies that came in, saw a problem to solve, and solved it. And part of it because of some kind of market shift, market transition that was happening in that moment that we were all scared of. That's what the fear was from. And during that market transition, there was opportunity. And so is there fear? And is it justified? Absolutely.

[01:10:50] You know, we're seeing transitions over time that have happened. But I think in my lifetime, and as we get older, we have these pattern recognitions, these parts, a part of what we call wisdom is seeing things happen over and over in our lifetime that just happen over and over. And I think that's right now. I think there's a bit of chaos that's occurring here because of AI. I think the pace of change is uncomfortable because I've never experienced it in my life. But every one of those pieces of chaos were moments I had never experienced in my life. Something was different.

[01:11:19] It was always different. That's the part of chaos. But in those moments, there's opportunity. And I think quantum metric success is a reflection of harness that chaos and convert it into opportunity. And I'm so proud that we are doing that and we have done that here at Quantum Metric. And so when I think about what's – and I know you said don't talk about quantum in that statement, but that's what I relate to. I'm seeing it happen in our business.

[01:11:45] I think there's a moment in every human's lifetime right now – or a moment in our journey that's happening in our lifetime right now that take advantage. There's something happening. There's change happening. And I think we can all find that side of that coin to drive to success. Is it uncomfortable? Is it something that we're going to have to change in our lives? Absolutely. But the pattern of humankind has we find a way. Yeah. Yeah. Yeah, I'm sure humankind is going to find a way.

[01:12:14] But some people, you're probably definitely in this category, are going to adapt and adjust and run into the opportunity that AI presents. And there's going to be people who hesitate. And this is the kind of thing where hesitating is a massive problem. And there's others who just are going to be ill-equipped.

[01:12:40] And we're going to have to, as society, we're going to have to realize that this is an enormous opportunity for some. But it is a real, significant, endearing threat for the well-being of others. And we're going to have to figure a way to bridge those two together. It is. Without a question, it's a frightening moment. Again, I will relate to my own concern about my own position.

[01:13:06] But I think there are only a few, if you look at the history of transitions, only a few people have to figure out this innovation. Only a few people have to create jobs for us in this new world. Not everyone. It's not on the, the weight is not on everyone's shoulder to figure out what's my role in this transition. I just don't, just history has shown that not everyone has to figure it out.

[01:13:29] We just have to have a few bright moments in humanity to figure out, like, how do we navigate this transition and pull the rest of society with them? So I don't, I don't feel that weight on my shoulders particularly. Like, I have to go figure out how this is going to work for everyone. I think I'm working through my own domain. But I think there are going to be some breakthroughs in the next few years that will figure out what does the world look like in this post-AI, you know, adoption.

[01:13:58] And I think it will happen. I just, I just see history consistently having that change. And I'm excited about it. And I'm fearful for it at the same time. So I share in the concern, but I trust in humanity that we've always navigated these great moments of transition. Yeah, yeah. Certainly everything looking backwards in time is consistent with that.

[01:14:21] And, you know, there's been, even in our lifetime and certainly in our parents' lifetime, there's been multiple of those that were going to ruin the world. And guess what? They didn't ruin the world. The world kept moving forward. And, you know, I think there's a reason to be optimistic.

[01:14:36] But I also think this is different than what we've encountered in the past because I think nothing in the past has led to a redefinition of what it means to be human.

[01:14:53] And the combination of not just AI but what's happening in genetic engineering, you know, could mean that there's a post-human, you know, situation that is going to be soon upon us.

[01:15:10] Where some people have different genes that they pass on or they, you know, they are able to do themselves that creates a separation that can have societal impacts that are, you know, you have to read science fiction. What then happens? Because that has never happened before.

[01:15:32] And the ability to leverage AI, I see AI as more like splitting the atom where, you know, certainly a lot of good came of that. But there's also been and continues to be this threat from, you know, nuclear weapons that could, you know, disrupt and destroy the world.

[01:15:51] And I think AI is going to be somewhat similar to that in that there's always going to be this other side to AI that we're going to be staring down and wondering if it gets in the wrong hands and be used in ways that can be very damaging to broadly to society. And, you know, I think these are – this is a path that has not yet been traveled. And we're going to have to, you know, view it that way.

[01:16:20] That isn't to take away from the excitement, the opportunity. I do believe that the net effect of this isn't going to be lost jobs. It's going to be enormous new opportunities that open up broadly, you know, read, you know, new types of jobs. I also think it's going to be kind of like the internet was where those who can harness the power of AI are going to be able to do a bunch of stuff regardless of what their background was.

[01:16:48] It's going to be people who didn't come from wealth, who didn't get the privileges of, you know, access to this or access to that. But they are able to take, you know, this new thing called AI and do a whole bunch with it. And all of a sudden, you know, their life journey looks very different than what could have been possible, you know, 50 years ago without this kind of technology.

[01:17:14] So to some degree, it will be an equalizer for those who are able to harness the power of it. But it's going to be, you know, another separation between, you know, I'll say the haves and the haves-nots, but the haves being those who are able to harness the full potential of AI and those who struggle. There's going to create more separation there.

[01:17:33] And that's just part of what's going to make it so innovative and so, I think, so much of a positive impact on the world as a whole if you measure it by our people living in better circumstances across the globe. Are more people who have, you know, food to eat? Are they in better health? Are they living longer? Are they sheltered? You know, the abundance that's going to come from AI is going to quickly spread across the world. But there are going to be some real challenges that come along with that.

[01:18:03] And we're going to have to navigate our way through them. Well, I think you hit three topics, you know, in that. And I'd love to address them in the order you had them. When you came in and we greeted each other, you know I only have one answer to the question, how are you doing? I say every day is a great day. I really believe in positivity can transform. And so my answers to your statements are going to stay on that strong positive note.

[01:18:28] So we're 80 years past, you know, nuclear here in humanity. And we have found a way to manage that responsibility of the power of nuclear energy. So I'm proud of humanity. Just going to say that right now. I'll add a soul far to that. And so, yeah, exactly, right? But that's the same thing how I feel about AI. I think we will harness the power of AI in responsible ways.

[01:18:58] I do think it's a very powerful technology, just as nuclear was. And we've done great things with nuclear energy. You know, we brought it to power homes and society. So I do think that it can be used in great ways. I think, too, you know, how we understand our humanity, and I'll just pick on genes. My college roommate is a professor at Stanford, and he uses CRISPR to help solve ALS.

[01:19:28] Now, I might say these words slightly incorrect, and he'll come talk to me later. But he had found a gene edit and did it to mice. And normally, within a few days of contracting ALS, they pass. The mice pass. And the mice, after this gene edit, live their entire lifetime. Like, how incredible that we could use AI to advance his research to bring ALS solution to humanity.

[01:19:58] And this is not just ALS. This is every disease that we have. I mean, can you think about that? Forget about the gene edit and give me a third arm. I don't know what I'd do with it today. Or wings. Whatever thoughts that you had in science fiction. But I think about solving disease and the afflictions that we have. How amazing, right? AI could do. And the third one, you know, the access and empowering of AI. Here's what I've seen in the workplace of the power of AI.

[01:20:25] I have folks on my team that are incredible designers. But they're not good developers. And I've seen designers harness the power of AI that were good designers. And now they can create code to make their designs come to life. And vice versa. Incredible developers that aren't good at design. And they were able to augment their lack of skill or experience in design with AI. And so I think, I don't think it's going to be the haves and have-nots with AI.

[01:20:53] I think there will be a transition period where some people, like, haven't adopted it, haven't engaged in it, and don't get some of the early benefits of using it. That's fine. I don't think it's going to last long. I think humanity is going to realize that this is, like, the day that we developed knife or fire or whatever new tool that we have. There are some people that didn't use fire right away. But they're like, wait a minute. Like, fire does so many great things for me. Why would I use fire in cooking or staying warm or whatever we use fire for?

[01:21:20] So I think it's a tool that humanity will adopt and use. And I don't think it will be the haves and have-nots because they won't figure out how to use fire or how to use a knife. I think we're all going to have to figure out how to use AI in our lives. And I think it will offset, you know, where we have strengths and where we have weaknesses and bring those two together. That's certainly true. So let's talk a little bit about, on a positive note, Endeavor, Endeavor Colorado and Endeavor Global.

[01:21:43] You've been one of the earliest Endeavor entrepreneurs here in Colorado and one of the most successful at harnessing the full power of the Endeavor Global Network. Talk a little bit about what Endeavor is and what it's meant to you in your journey. And, you know, just tell us a little bit about Endeavor. Yeah, I'll kind of translate it into, as a founder, we have a lot of folks that we can bet on.

[01:22:08] And part of the early journey a lot of founders go on and, you know, fall into this decision of which venture capital am I going to engage with and maybe sign a relationship with and take some funding. And I think part of the process that founders go through in that early stage is, you know, not just the money. I think sometimes money, you know, kind of blinds people in the decision process. Oh, you'll give me more?

[01:22:32] But I think it's, you know, money, if you have a great idea and you have great execution happening, I think you can find easy access to cash over the years. We've seen that kind of, you know, lull and grow over time. But I think when we made our decision at Quantum, we chose Insight Partners because they have 170 people that across like different centers of excellence from sales to product, to go to market, you know, marketing, et cetera.

[01:23:00] I think the relationship with Endeavor was one of those same processes. I remember hearing about Endeavor from one of the board members here in Colorado. I'm like, all right, well, you know, I'll give it a chance. I'll meet some of the people. When I got on a call with the Endeavor leadership, I was like, oh, my gosh, these are incredibly successful people in their domain and they're willing to spend time with me? Wow. Right. And kind of early on, I thought maybe this is just one phone call to spend time with me. That's nice.

[01:23:27] I get to meet some really cool people, really successful people, people that can impart nice wisdom with me. But maybe that's it. But it turns out over the last few years that those people are actually willing to spend time with me, coach me, help and make our business more successful. So I actually have a meeting tomorrow with an incredible founder of a global success. I'm really excited to have that conversation. I've been able to connect with the executive leadership of one of the of a Fortune 20 company here in the U.S.

[01:23:56] both last week. I'm connecting with them next week again because of folks on the Endeavor board. So helping go to market, helping connect, driving some of my decision making process, the board and the team, not just here in Colorado, but around the world has really been willing to spend time with me and my team to help us advance and create greater success. So I think there's some people that get involved in organizations that, oh, it's nice. It's just like a footnote on their LinkedIn.

[01:24:25] And there's other organizations that are active at helping create success. I've been really fortunate that Endeavor is that second one and has been able to engage and really make a difference in our success and our journey. So I'm really thankful for what Endeavor has done in our partnership. Great. No, and I couldn't be more pleased with the traction it currently has under Zeb's leadership. And we've got four, soon to be five, brand new Endeavor entrepreneurs from Colorado.

[01:24:56] I think it's a really strong organization and one that's doing a lot of good. And that's what we all owe each other right now is to find ways to help each other out. Anything we haven't touched on that you'd like to bring up? No, Dan, just your leadership. I mean, I'd just like to say thank you again for having me in your house a second time. But that Endeavor conversation, at some point you said, I'm going to take the reins of the organization and have impact.

[01:25:24] And I try to measure that impact by, let me show you the percent of our business that Endeavor has touched, that has directly driven. So really, I think I'd just say thank you to you and obviously the Endeavor board, the Endeavor global team for having created this success. I'm just very thankful. So I just want to extend a real big thank you to you. Well, I appreciate it. All right. Well, it's been great getting to know you. I'm so glad you moved up here to Boulder, making our Boulder tech scene. I'm sure you're seeing that much more lively.

[01:25:55] And glad to see you guys are doing so well. Well, thank you, Dan. And I have to leave it with, every day is a great day. Thank you. All right. Good deal. Thanks. Thank you. Thank you for listening to this episode of The Bear Roars. Check out Stretch, the new song from Dan Caruso, with music by Jason Mendelsohn, available now on all streaming services. This podcast was produced by Loud Bear Productions and edited by Natanya Chatfield, with support from Kendall Weinberg, Gibson Siegert and Alex Kim.