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+**Daniel Whitenack:** Welcome to another episode of the Practical AI Podcast. My name is Daniel Whitenack, I am CEO at Prediction Guard, and I'm joined as always by my co-host, Chris Benson, who is a principal AI research engineer at Lockheed Martin. How are you doing, Chris?
+
+**Chris Benson:** I'm doing very well today, Daniel. It's happy holidays. We're in that season. I happen to be recording outside, kind of looking at the birds around me and stuff, and seeing them fluttering around the yard.
+
+**Daniel Whitenack:** That's amazing. Thinking about AI drones or something, probably...
+
+**Chris Benson:** There you go. I can't stop that.
+
+**Daniel Whitenack:** That's great. Well, I'm super-intrigued to have the conversation we're going to have today, because I think people will find this super-interesting and connecting on a variety of levels, both emotionally and technically, as is the topic that we'll be talking about. But we have with us Jeff Smith, who is founder and CEO at Chrp. How are you doing, Jeff?
+
+**Jeff Smith:** I'm great. Thank you. Thank you, Daniel and Chris, for having me on the show. I really appreciate being here.
+
+**Daniel Whitenack:** Yeah, it's great to make the connection. We have several mutual connections, so it was awesome to get connected through one of those. Shout-out to Greg Enos, if you're out there; awesome connector in the Indianapolis area, and good friend.
+
+Yeah, so Chrp, C-H-R-P. So no I. And I see, when I look at your profile, Jeff, one of the call-out categories that you have are AI, mental health, and music, which all come together in Chrp, and are all super-intriguing to me of how they come together. But I guess maybe start out by just helping us understand how these things intersected in your own life.
+
+**Jeff Smith:** No, I'd be glad to. As you mentioned those three things, it is the perfect storm, especially for topics today... And it was a journey to get here. And I can give you a little bit about my journey and how we ended up with AI and mental health.
+
+I would say in my background - I'm a corporate guy gone good. A classically-trained entrepreneur. I built six companies, three nonprofits... It's where I find the joy. Identify a problem in the world, create a unique solution, wrap a company around it and build it to scale.
+
+And this one's called Chrp, named from the story of the canary in the coal mine. When that bird stops chirping, you get the heck out. It can send signals that we cannot. Methane, carbon monoxide... And so it becomes an early indicator for health and wellness. And we've created a platform harnessing that, using music.
+
+And so to go even further back on myself and how I even got in this business - for years, I was the go-to guy for most ad agencies in New York to do all their social impact branding, corporate social responsibility... I became an expert on weaving purpose into the brand narrative, and bringing people alive at work, and through the products, and the brands, and these global campaigns... And along the way found a significant disconnect, I would say, between the leadership -- the leadership that cares. They care about purpose, they care about their employees, they're throwing millions at perks, rewards, telehealth, but their employees aren't feeling it. They're not feeling seen, they're not feeling heard. There's work-life enmeshment. They're depressed. They're anxious. They're looking for jobs. And so a few of us decided "Hey, let's take that on."
+
+We created a small company to come up with solutions to address workplace flourishing, and thought "That'd be kind of cool. Let's bring people alive in the workplace." And we started there, and said "Hey, what's this disconnect between leadership and employees? What's the problem? If there's intent and there's resource, but not results, where's the breakdown?" And we've found that it was an information problem. We've found it was the corporate survey, the quarterly polls. How are you feeling? Nobody answers it, they lie in their responses, fear of reprisal, and they're just making blind bets.
+
+The data they're getting back is at fault, mainly because people hate surveys. They want to fill them out. And so we said, "Let's start there. Let's come up with a better diagnostic tool, so people can feel seen and heard and where they're truly at. And how do we do that?" And we set out to improve the survey; the survey tool, different modalities, different lengths, maybe even the happy faces you see in the bathrooms at airports... You know, just make it super-simple. And none of those are really tracking after a few months, and I was training for a Spartan race. One of those crazy things that we do to keep ourselves alive... And I found my mood changing with my music. I switched to, I think it was Motley Crue, Kickstart Your Heart, and just found my energy level shifting, and started thinking, as I was running, "What's happening here? Am I being affected by the music? Am I making certain choices in my music selection that's a reflection of this?" And that was where I had the a-ha moment, to say "Hey, is music the signal that we've been looking for? Is that a reflection of how I feel?" And so we dug into that, looked at music science, listening behaviors, research AI, and found a direct link, as music is a mirror for your mood.
+
+In the simplest form, Chris, if you're driving in the car and you're listening to the radio, and you change, change, change until you find a song you like, that's just your mirror neurons lining up your emotion with that song. it's how you're feeling or how you want to feel. It's very hard to listen to music you're not feeling. It's that grind... And so we thought, "Okay, if we can bottle this up, we've got a rocket ship. If not, we'll sell the algorithm and move on to the next task."
+
+\[00:08:07.08\] And so fast-forward, raised a bunch of capital, surrounded myself with brilliant people, technologists, HR leaders, music... So a buddy of mine, Suman Debroy - we've built some amazing things together. As a doctor in machine learning, he jumped in to help figure out the models... HR leaders from enterprise companies, managing hundreds of thousands of employees, speaking into "What does that experience need to look like inside of the company?" The music industry, songwriters, musicians, former execs from the big music streaming companies saying "Hey, this is the data that's available to you, or even the intent from the musicians." And that was phenomenal, to understand what were they feeling when they wrote a song? What do they want to put out in the world?
+
+And so that was fascinating. And then even the attorneys, legal counsel. We've got the former privacy chief of Homeland Security to really look at "What are the privacy blockers? How do we hold integrity in this conversation?" Because music is so personal. And so we brought them together and said "Hey, let's solve this problem. Music is our answer." And like any, I guess now a new tech company, you're testing it across an alpha group, looking at everything: adoption, the science... You end up with a black box on the table, it works beautifully. And so that was like end of last year. Now you're shifting to product-market fit. Who is this best built for, right? A healthcare company at 2,500, a sports team, automotive company? So that's where I'd say the rubber hits the road, and where we're at today.
+
+And just incredible leaders - you mentioned Greg Enos and others - just looking at this, saying "Hey, here's a direction. Let's really look at that how we can apply it."
+
+**Chris Benson:** I've got a question or so for you... But for listeners, they can go to Jeff's LinkedIn profile - and I believe that's you in one of these Spartan races, based on what you said.
+
+**Jeff Smith:** That is. That might've been the one...
+
+**Chris Benson:** There you go.
+
+**Jeff Smith:** ...that it all goes back to. And that's a picture of my now nine-year-old that we're holding as we get the metal. But it's those fun races, you're bloody muddy, and your body hates you, but you love doing it.
+
+**Chris Benson:** As you came up with this hypothesis, and in those early stages you're socializing the notion around, and kind of explaining that, what kinds of different reactions did you get from people, and how were they different? And I'm curious if there were any reactions people gave you to your idea as you were just getting started, that surprised you, in a positive or negative way, either one. How did people take it in and process it themselves?
+
+**Jeff Smith:** I would say the initial reaction is the eyebrows go up, and they lean in. Music is ubiquitous. It's all around us. It's amazing. And it touches our lives. And so you have this emotional currency that we all get. So they lean in and say "That's amazing." And then they'll kind of sit back and say "Ooh, what does my music say about me? If I'm listening to Nine Inch Nails, does that mean I'm depressed? Or what's going on here in my heart?"
+
+So they go through that, and then they lean forward again and say "Oh, this is amazing. How do I incorporate this in my life, in my profession?" And I would say the surprise or the unique things that came out of those conversations is really these tributaries that were created.
+
+So we built the tech, you patent it, you apply it to a sector that you understand, have influenced in, that has a large enough addressable market, good liquidity, there's a budget item for engagement measurement... And you apply it in there. I didn't expect then where this would take us, right? We've got mental health professionals saying "Hey, we want it as a screening tool to get ahead of certain things in our clinics", as well as creating a profit center for them. We have athletes and sports psychologists looking at it for performance. We have the US military needing to address suicide rates and say "Hey, if we could just know more about how they're doing..." It's all about that early detection, early indicator on how they're doing. And again, I want to stress, it's a screening tool. It's not a diagnostic or an assessment tool. So inside of therapy clinics, it'll just get them to that BDI or GAD, the formal assessments quicker, which is kind of cool.
+
+\[00:12:18.09\] So I think that was the biggest surprise for me, is I've launched a lot of companies, and it's around the innovation or the relationships or the opportunity... This one was just -- I use the word 'ubiquitous'. It just -- it's emotional, it's primal, it's historic... It's just in people's lives.
+
+We come to learn that the average person listens to 22 hours of music a week. It's all around us. And so that's just really cool. So I would say that was a big surprise for me.
+
+**Chris Benson:** And by the way, I just want to say, Nine Inch Nails still rocks after all these years. I've just got to say that before -- I know Daniel has a question, but I had to say that.
+
+**Daniel Whitenack:** Yeah. Fair enough. And of course, we're going to get into kind of the AI intersection here, which I'm sure is sort of how some of these insights are developed. Maybe before that though, one of the things that I'm thinking about - and maybe you were starting to get into this as you were kind of mentioning the different types of scenarios, like in sports, or in various verticals, or therapy contexts, or whatever that is... One of the things on my mind is there's a whole variety of ways that music is adopted in in a person's daily life, in particular at work. I am imagining my wife's candle manufacturing company - there's safety issues, if everybody has noise canceling headphones on, right? But they have music playing in the environment, that everyone can listen to. And then I imagine the programmer, with his Bose headphones on just like grinding away, listening to whatever, almost all day, maybe in an environment where he's in a home office.
+
+So as you've kind of delved into this - and we will get to kind of the AI stuff, but how does that influence your approach, I guess, or your thought on the value that can be added here? Because I could imagine certain employers being like "Well, what music do I play in this common environment?" Or even in a retail setting? Like, what music makes people want to buy things? Or in that more intimate setting, the music that I'm listening to all day. What does that signal about kind of things that I need to be understanding, and need people to kind of know and see about me?
+
+**Jeff Smith:** Well, as you said, we can dive deeper into the mechanics... But more of a use case \[unintelligible 00:14:47.20\] technique that taps into your current music streaming. So it's not during the work that -- it could be if you're listening to it, but it's also in your commute, and when you're at home. So we wanted to design something that didn't change your behavior, but just kind of tapped into it. And then take that data and then analyze it and everything from there. So it's an opt-in technique based on their current music listening habits.
+
+So it's interesting you bring that up... I mean, working with architectural design firms, landscapers, I didn't realize they listen to music under the big headphones. So for them - great, all day long. Or manufacturing. But we can sense and analyze the data just on your relationship to music. And I can get into how that differs, and so on.
+
+But it's a lot of learning for us right now too, which has been exciting. People's approach to music, what it not only says about them, means about them, but just over time.
+
+Now, you bring up about retail... And there are some of those areas that we have intentionally said let's hold up on. Let's first just master the science, let's make sure that it's high-integrity, it is human-centric, and we take care of people, and it is for their wellbeing and flourishing.
+
+We have had a few folks saying "Yeah, but if we can help sell another sweater by changing up the music, maybe." But at the end of the day, that would be data that is completely stripped of any personal information, and all that. I think over time there are all sorts of use cases, and we just have that true North as far as how is this truly improving lives. And improving lives is improving businesses, and they are more profitable and sustainable and everything else... And so there may be a place for that. But yeah, I don't know if that helps.
+
+**Break**: \[00:16:40.26\]
+
+**Daniel Whitenack:** Well, Jeff, we are starting to get into this, around the kind of mechanics of this... And I don't think we have to drill into all of this, all the details in the implementation, but it would be interesting... Some of the things on my mind - and I'll be vulnerable in this context and reveal some of my music habits over even the past few days...
+
+**Jeff Smith:** Oh, Britney Spears?
+
+**Daniel Whitenack:** So... I think yesterday, when I was trying to get something done at work, I was streaming Gregorian chants, which is normally my go-to, like, "I'm not going to be distracted while I work with lyrics. I just want something in there." I think as I was driving in the evening, I was listening to Mastodon, Leviathan, and maybe -- I don't know what that reveals about my post-work feelings... Maybe you would have that interpretation. And then I think in other cases, I'm listening to... I love old-time Appalachian fiddle music. So that's kind of my general go-to. So there's a lot of variety there.
+
+So one of the things on my mind is, well, if I'm thinking about -- let's say you just gave me the songs that were played, in my playlist, or your playlist, over the past however long, and the behavior related to that, it does seem like a tall... I mean, there's a connection there to maybe well-being, how I'm feeling... But the connection is difficult for me to think about as a human, because there's such variety that you would experience.
+
+So I'm wondering, maybe even before what you've developed now, what were some of those challenges or surprises as you actually thought about the technical scope of this, and what was and wasn't possible?
+
+**Jeff Smith:** Well, I think music is very personal, and how you approach Gregorian chants is different than how I do. And what we designed was a system that's looking for how you approach music and creates a baseline. You're looking for deviation... And I can kind of go into all that. But it gets to know you over time. Creating that persona, is that - okay, you're making certain choices in your music selections that are unique to you.
+
+So you've got all this incredible data coming through. It's danceability, it's lyrics, chord progression, balance, happiness, beats per minute... I mean, there's all this raw data that's coming in. And if in a vacuum you're looking at that saying "Okay, well, this is my mirror neurons lining up. I get it. This is how I'm feeling. So I'm choosing that." Then you look at certain choices you're making. "I'm fast-forwarding through this song. I'm skipping this. I'm adding this to the playlist. I'm playing this again. My beats are going up in the afternoon, because I'm working out..." There's all of this usage data that then you're looking at in comparison to that, and then it looks at it over time. So what you're looking for is "Great. Here's your baseline, and you're trending, now, all of a sudden you're deviating in a certain direction... So what does that mean?"
+
+And so I'd say that one of the surprises - I think you asked that - in our discovery was, although music is incredibly personal and individualized, a majority of people still get hit the same way, with the same song. 60% of people listening to the song "Happy" will feel happy. I mean, it's just something like that. And then you'll get that extra 20%, 30% accuracy because of your individualizing it. But I thought that was really cool, that we could screen majority of population against a certain album, genre, songs... So that was one surprise.
+
+And the other was the flip it around and how the music industry starts supporting us saying "Ooh, this is interesting. Can we use your algorithm to write songs, to achieve the emotion that the brands want to accomplish in these commercials or these movies?" I thought "Okay, that's a whole different line of work." And so I think it's fun to explore that, the individualization, understanding how we're addressing that through AI and everything, but also surprised by these other opportunities for the masses.
+
+**Chris Benson:** I'm wondering if you can -- just to kind of make it tangible, because this is fascinating, what you're talking about here. If, extending what Daniel was talking about, one day I might be working and listening to Queen greatest hits, and doing that. And on another day - and God, I hope the audience doesn't beat me up on this, but I might be listening to musicals in the background. And there are things that I already know, I already have the lyrics down, so I can kind of ignore that and just appreciate it... What kind of insights, with those being two very different genres, but maybe for the same activity - I'm just curious, do you have any examples of...
+
+**Daniel Whitenack:** \[00:24:18.05\] What's like an outcome?
+
+**Chris Benson:** Yeah. Do you have any examples of some of the things that you've learned about that, or at least how you look at it? I'm just trying to kind of ground that into some kind of reality that you've discovered through this research. It's fascinating.
+
+**Jeff Smith:** So let me do two things. I'll take you through the use case. And so I'm now cherry-picking certain data points, and addressing it... But, I mean, the short answer on your question is it depends on your baseline, repeated listening, tilt your mood in a direction, based on certain behaviors you're doing. So I would say short answer is it depends. And I can kind of walk you through how it works, and then it'll be fun to get your response on it. And then of course - I mean, that can go into the AI, and what... Let's start high level.
+
+Music, like AI, artificial intelligence, it's limited inputs, exponential outputs. If you look at music, there are 12 notes. You've got seven letters, you've got major/minors, but in the end, you've got 12 notes, and that gives you everything from Mozart to Megadeth.
+
+Then you look at music behavior. People on average, like I said, listen to so much music. But then how they're doing it, when they're listening to it, their playlist, the repeat, and everything else. And so all that data is there, and we've built the engine to capture that, and then AI to analyze, interpret, decode for wellbeing.
+
+And if you look at -- I guess on the AI side, you've got the traditional to discern the mood of the user from the acoustic features (a lot of what I described from the songs) and then generative to customize messaging, and so what that output is. But if you look at an experience inside of a company, a use case - so you're an employee at a company, you get an email from HR "Hey, we've partner with these rock and rollers of corporate wellness. We care about your personal wellness journey. Opt in with your Spotify, Apple, YouTube, get free perks along the way. Learn more about yourself. All data's anonymized. We're looking for trends on how to better serve you, your job and your wellness."
+
+And so like I said before, we wanted to tap into their current music listening, not create another app they had to download, but it's just say "Hey, this is the behavior. This is how you're listening to -" I think say Gregorian chants, and what's going on in the background.
+
+And so what happens is when that user opts in, there are two paths. One's for the organization end. The data is anonymized, clustered... They're looking for trends. This way, an executive/leadership team can look at dashboards, report outs, company-level, department, division, down to team. You don't want to get to the individual to avoid any liability of selection. "Hey, Johnny \[unintelligible 00:26:48.13\] was listening to Kid Rock and he got fired." You want to avoid the one-to-one. But they want a better solution for insights that are one to many. And then you have bespoke recommendations for how to actually intervene or serve those teams.
+
+On the individual side, they're offering more intel about their emotional buoyancy. After a few weeks, they get a weekly email, encouraging them to check out their E-score. So think of a WHOOP band for mental health. Your sleep score. What does that data point on you? What does it say about you? Because your interaction with music is unique. And they love that, because it's a data point in their life and wellbeing.
+
+And then we throw in that little added perk, because we do want them to feel seen and heard. "Hey, it looks like you're feeling a little melancholic this week. Here's \[unintelligible 00:27:31.27\] your favorite coffee spot." How do you actually take that data, not only to be self-aware, but given tools for self-regulation, and everything.
+
+And so when you look at that model, it's very personal, yet anonymized on the organization side. And so we've always had that tension to make sure we have a strong firewall. But then as we work with them and as they listen more and more, it just get smarter, smarter, smarter. It's fascinating.
+
+\[00:27:59.19\] So to answer your question, I personally don't know, if you were listening to Taylor Swift this afternoon and then the Wiggles in the morning, how you're feeling. But I can tell you by running through Chrp for a few weeks, you will see a mirror of your emotion, which is fascinating.
+
+**Daniel Whitenack:** How long does it take to kind of form that baseline? You mentioned a few weeks... How much is needed for that kind of cold start of problem? Or maybe is there more as more people use the system, there's less of that cold start problem? Or I don't know how that works.
+
+**Jeff Smith:** Well, again, as more people listen, we get more data, and you're seeing the trends that are part of that natural grouping of the song data. But you still want to refine it, refine it for that personal answer. And so we say three to four weeks... It's closer to two, but everybody listens to a different amount of music. So we can say the average is 20-some hours, but this week you might be four hours, or eight hours. And so how much data is being ingested is important.
+
+And then also, it pulls in podcasts and audio books. And so that helps with contextual markers. You might be driving more melancholy, but you're reading a lot about grief, you know? And so what are the things along the way that can just make the engine even smarter?
+
+**Chris Benson:** I'm curious - and you started to answer that a little bit, but when you mentioned podcasts and books on audio, and that kind of thing... Which I do a lot of, both of those, in addition to music. Is there any kind of contextual difference with people in that? And how do you account for natural mixes that people have? My mix is probably somewhat different in some ways from Daniel's mix, from your mix. How do those different types of things that are coming into your experience change how you evaluate someone in that way?
+
+**Jeff Smith:** You know, the podcast and audiobooks - I use them as contextual markers. I'd say they're additive. They're not nearly as accurate as music and music choices, but they tell a lot. And there are so many attributes that we pull in these acoustic features that give tons of data. And I can talk about how that's churned.
+
+On the podcast/audiobook side, you have transcriptions, you can listen, but they deviate a lot. And so you can look at certain choices that people are making. "Hey, I'm going to listen to this. I'm going to read this book. Okay, I'm halfway done with this, but I switched to this other book." So behaviors are not as, I'd say, accurate to really know at a point in time how somebody is doing. But you put that into the mix with their music, and now you start to see a really colorful picture.
+
+**Daniel Whitenack:** Maybe you could touch briefly on this... You alluded to it earlier in terms of the way in which you're going about this technology towards kind of human flourishing and wellness, because this is one of those things - and I'm sure listeners out there are thinking about this... You can think about really good uses of facial recognition. Or even the technology that is used in deep fakes. You can also think about really harmful usage of that, or manipulative uses of that technology... And I think this scenario - I love how you've talked about it with that kind of standpoint from the very beginning. And you've even kind of mentioned some of these things, "Hey, let's hold off in this area for a while", or other things. How have you kind of come to grips with that, and how have you thought about that kind of trustworthiness and care for the users within what you're building?
+
+**Jeff Smith:** \[00:31:35.05\] Well, I think it's incredibly important to keep the human at the center of it, and just look at the integrity of the data, and look at the integrity of the communications, how we're really working with them, treating with them... What are they seeing -- even in the E-score, we couldn't say that "Hey, you're a 85 and I'm a 75", because that might make me feel like "I'm not optimized", you know? And so what are all the little things? And we even have some of the team that developed the Oura ring and the visuals there to to look at it... I would just say -- I mean, we are a purpose-built company. We are all capitalists, but our true North goes a lot deeper in our purpose, our faith, and how we're driven.
+
+And so from our standpoint, seeing this as a tool for human flourishing I think is just super-important and sacred, matching with how sacred and epic music is. It's just historic; it speaks to our soul, it goes so deep. And so we want to make sure we steward that well.
+
+Now, in this process you've got all the baggage, all the red flags, the landmines you have to look for. Inside of companies, it is privacy, it is data integrity and security, and you look at the user experience and the rate of adoption, and then the whole way -- and if we stick to that true North, you're going to get there. You're going to get the numbers, you're going to get people participating... The moment that you violate that, you've lost them. And that's just not something we're willing to do as a company, as a team, and where we're headed.
+
+So it's just been fascinating to see that for us that's normal. Even how we've built the company, we want to do life with people we love. And at the investor level, and the clients, and everything. And what we're finding inside of companies is trust is so important. We've turned away some C-suite that have come to us saying "Hey, we want you to bring this in and fix our culture, because our culture sucks." And then you realize it's because they do. It's just, they just want to get more out of the apple. And so we want to work with companies that care about their people... Yes, retention is important, and productivity is important, and if you have flourishing companies, you have flourishing cultures... But who's at the helm?
+
+So I think we've just used that language and vernacular, and never knowing these environments, if that's foreign or regular. But I know even from your own leadership and your direction, and our relationships that feel comfortable sharing, that purpose is at the core.
+
+**Break**: \[00:34:00.07\]
+
+**Chris Benson:** So Jeff, as we kind of jump fully into the AI aspects here, could you describe - without giving away any secret sauce, obviously, anything like that, but kind of describe which AI technologies have been important to help get you go, and some of the things that you're most interested in. Because we've been talking so much about generative AI, and large language models in recent years, and I'm sure that's part of the mix... But one of the things that we're starting to do at this point is kind of understand how people are using those, and also what other things they might be interested in that are not necessarily straight \[unintelligible 00:37:46.03\] another LLM. I'm curious if you can kind of share how you've built the business around different types of technologies a bit.
+
+**Jeff Smith:** Sure. And I can continue on the user journey, and talk about the case in which it's being applied. So if we look at -- as we said, when a user signs up, we spend three to four weeks analyzing their data before we create that baseline profile for the user. Then based on their listening, we estimate that delta change of the user from their natural baseline.
+
+So when Chrp looks at the music or song, it uses the acoustic features from a song to map it to -- I guess the term multidimensional vector. So the vector sits in one or multiple clusters where at the center it represents the first level of emotions. So we inside, we call them L1s, level one emotions; it's energetic, melancholy, aggravated... So what are those core emotions that we're uncovering. And the distance of the vector from the center indicates its closeness in the membership of that L1 group.
+
+Now, once these are defined, so the relationships are defined, a combination of them with the specific weightages, you end up with L2s. And the L2s - again, it might just be an internal term - they're second level attributes that are relevant inside of the workplace.
+
+So executives - they were less interested in baseline emotion; kind of "Are you happy, sad or sideways?" but really, how does it affect a person's balance, their motivation, their stress? ...and knowing the correlation between those to get ahead of burnout. So we had to really define those organizational attributes. And I'd say -- to your point on secret sauce, it's really knowing the L1 group memberships for a song, and then knowing the weight is necessary for each combo to generate those L2s. So to get an accurate level there.
+
+So then based on further listening, estimate the change from the baseline. Since the baseline is unique to each user, as you said, Daniel, we can do a better job knowing who they are, I'd say fingerprinting it to them week on week. And so measuring that data every week. And then from there, pulling in the podcast audio books for context. But what they end up with is the organization ends up with actionable insights to serve their people. People are equipped with their emotional data point. You've got the extrinsic motivational rewards and perks, getting them in, the intrinsic motivation and data point, understanding how they're feeling... And so you've got self-awareness, self-regulation, and it leads to reward.
+
+\[00:40:18.05\] But as far as the generative versus traditional, the traditional side is what's analyzing it. The generative will show up in the messaging back to the individual. So if you look at -- you've got an E-score, and "Hey, this is what's changing in your life, and your motivation's going up, and your balance..." And so you're getting an idea for the fluidity of how you're feeling and your emotional wellbeing. And then you'll have a little copy area that speaks to that, you know? And that is unique to their situation. And then we'll do the same for the organizations on their tips, trends, triggers, and how to understand it. But I would say that's really where the generative comes in, and it becomes unique to them.
+
+And then the third element being that it individualizes the perks, again, like I said, about dark roast coffee, or whatever that is, getting to know you and how does it spit that out. That's really diving under the hood. So without giving out too much, but understanding the science behind it.
+
+**Daniel Whitenack:** On the actual business user side, I could imagine -- you even mentioned a little bit of this, of getting the right people in the room that could think about how to visualize this for the business user... Because I can imagine if you have an organization with 5,000 people, 10,000 people, a hundred thousand people, some of this can be broken down by team, or group, or project, or other things like that even, in addition to kind of overall, or at the kind of interactions with the individual level. So yeah, how do you think about that at a more consuming side of this, and the utility of that, and how have you kind of found the right level of detail that kind of balances insight, privacy, utility for the individual, kind of opted in user? Yeah, any thoughts there?
+
+**Jeff Smith:** To speak to the original business case as we said it, so carving out the military, pro sport team therapists and so on, but looking in that HR tech, it was fascinating, as we're working with CEOs, CHROs, head of people, chief administrative officer, to really understand their charge inside of these organizations or agencies. And you're right, the size of the company matters. We were going with enterprise companies, CHROs are saying "Thank God you didn't come to me with solutions.
+
+I have an entire library of solutions. I just need better insights. How do I deploy them better? Who do I serve \[unintelligible 00:42:53.21\] development, with perks, with executive coaching and so on?"
+
+So you kind of have that, where they're saying "We just want better diagnostic tools, better insights to work with." One said "Hey, I spent last year $250,000 on a single survey that took 90 days to get the results back in nine months and talk about it, for a moment in time that we weren't in anymore. The company had changed. And so this gives me regular insights that I can really look at."
+
+And then I can call up that manager, "Hey, this team is looking on the verge of burnout. What's going on down there?"
+
+Or "Your team's crushing it. Hey, how can I provide you with more resources?" And they all segment differently. You're right, it's regional, it's by business unit, different teams... So you figure out that segmentation, and then they run with it.
+
+The smaller companies I was finding are saying "Great! Thank you for this information. But what do I do with it? Can you help us better understand what this means inside of our organization?" So what we did was we ended up with our data sciences, behavioral sciences, organizational health, sitting down with these companies saying "Hey, this is what I see." I mean, already they're looking at the data. Everything's coming back, they're looking at it, great, let me tweak this, let me change this... And before the reporting goes out, because they have a dashboard and \[unintelligible 00:44:10.26\] on teams... And then at 90 days we sit down with the client and with the behavioral scientist and say "Okay, this team is showing these signs of this. I would provide more fuel on the fire. Rewards. Perks. That's really -- honing on this. This is an area you might want to check on."
+
+\[00:44:27.18\] And so we're in that phase right now to understand "Did the large enterprise companies say they had the tools, and just projecting that they had everything, but they really need help, or not?" And we don't know yet. So I think it's been fascinating to walk alongside them. We said "Hey, what is our role in remediation, intervention, solution?" Let's first be the best in the world diagnostic tool, using music as a signal for emotional health and wellbeing. Great. Then, where do we step into the solutions and recommendations? And so we have a full report out, say "Hey, this team - motivation's up, stress is up, balance is okay. Collaboration's good. It means they're crushing it. Let's provide these." "It looks like collaboration's going down, stress is remaining high, balance is a little bit off... Okay, they're on the verge of burnout."
+
+So to you understand behavior and what's truly happening there, and how it influences and impacts companies is important. And so that's why we have the team look at that, and we're providing this HR intelligence center to these clients to say "Hey, do you want to tap into this? Can we help?" And I'm finding that they like that. They like being led to water. It's not a reflection of whether they're doing a good job or not, it's just to say "Hey, here's some resources that you might want to consider", because we created it for that reason. They do care, and they're throwing resources at employees... Let's just help them better deploy those resources so they land. So people stay in their jobs, and companies are growing, and people feel excited to come to work every day.
+
+**Chris Benson:** As you kind of addressed that HR leader profile on your website there, and you have these others, and you've alluded to them earlier in the conversation, therapy practices, sports, military, university... I'm curious, when you're positioning this capability to these different groups, is it all more or less the same, or are there very distinct ways that they perceive the value that is -- for instance, the HR that you've talked about, versus military, or versus university? Is it distinctly different in those groups? And if so, could you kind of describe what that is?
+
+**Jeff Smith:** Sure. I was with a friend at an event and he was explaining to his wife what Chrp is. And she sharply elbows him in the ribs and says "See? My music knows where I'm at." And it just was the funniest moment. I'd say that is the consistency across those verticals. So how they apply it, the value they're getting out of it varies. But at its core, it's all the same, to really understand people's emotional state there.
+
+Now, like I said, built it for HR. Great. There's a need. As we start to go into others, it's been interesting, because you're getting more into mental health and performance. We start with organizational health by addressing emotional wellbeing... And then on the mental health side, we have several hundred therapists that are starting to sign up and use it, and everything. We're figuring out "Okay, how do you craft this as a tool inside of their agency, inside of their clinics?" And what they want to use it for is better patient care, they're more connected to their clients... And then also, it's a revenue model. It's an added assessment tool that is helping them build their practices.
+
+\[00:47:41.12\] Now, they're coming out of COVID, being overtaxed, at capacity... How do you build \[unintelligible 00:47:44.23\] We said "Great. Let's help you build your business by caring for your people better." And so that's a unique tool. So you think about their mindset - that's different than the HR leader, which is different on the military, where the military came in -- we've got one base, there's 4,200 troops under their command. 120 of them we're looking at piloting with, and that 120 has had three suicides and three suicidal ideations in the last year. I mean, that's insanely high. And so the colonel's saying "Hey, I want to use anything, whatever it takes, to really know where my troops are at. I want to keep them alive, keep them healthy." And so that's a different use case.
+
+And then on the sports side, where that started was the universities are saying "Hey, we want to use this for our college." And I challenged the president and said "Why?" They said "Well, student retention." If students are socially, emotionally healthy, then they stay in school, they pay their tuition. I mean, that makes a lot of sense to me. Great. That's student retention, that's revenues, that's a business issue.
+
+And so as we start to look at that, you've got organizational health inside, then you start to look at campus-wide, but then the athletics directors started to call, saying "Hey, this is interesting. Can we do it in sports? This is something with student athletes... We haven't dealt with this level of anxiety and stress and depression that we have before. So could this be a resource so I can better help them?" And then one athletic director said "Can I also optimize behavior, help us win some championships?"
+
+So you start to look at -- okay, now we're getting into behavioral health and performance, and that gets exciting, but it's just... It's figuring out that balancing act without feeling like the entrepreneur that's trying to boil the ocean. So each one of them, I'd say, has that underlying belief that music knows where I'm at. And so by better understanding people's emotional well-being, I can serve them, I can heal them, I can improve them, I can optimize them, but all kind of on that core tool, using this engine.
+
+**Daniel Whitenack:** As we kind of get close to the close here, I'm just kind of struck with the kind of nature of what you're building here, which is using AI in a way that's driving more human connection, and human connection to resources, hopefully that improve their wellness, help them flourish. That's super-encouraging to me as I think about the future, rather than AI solutions that kind of increase isolation, and have us interacting more just with AI agents. But I'm wondering, as you look towards the future, maybe in terms of what Chrp is going to do, but maybe more broadly as kind of what's possible with this technology, in these ways that are actually positive and restorative... What are you excited about? What's encouraging to you? What are you thinking about as you're going into this next phase of your journey?
+
+**Jeff Smith:** Well, I'm very excited about some of these tributaries that I mentioned. Going upstream on the mental health side, saving lives, downstream on sports and performance, still with music at the core... I would say you mentioned it - it's not taking the human out of the picture. We are relational beings, and so first, let's heal the soul, let's use music, let's speak to them, let's bring them alive. Great. Then, is there a way to connect people around music? Is there a way that this then takes you a step further, that is greater connectivity with others around the music you listen to, around how you're feeling?
+
+And so I think that would be great to explore. Again, not to put more directions on this company, but I would say let's master the organizational health, the mental health, and the performance, and then look at community formation. Music is a connector. It always has been. And so how do we make people feel part of something greater, keeping the human at the center? And AI is a complement to that.
+
+**Daniel Whitenack:** Great. Yeah, thank you for that perspective. I think that's an amazing way to close out here. Thank you so much for joining us, Jeff. This has been a real pleasure. So happy we got connected, and I look forward to analyzing some of my own music. I believe that you mentioned kind of getting some people into the system, and having them understand a little bit from their own music playlist... I believe there's a link that you can share, that if people are interested, they can take a look and understand some of these insights. Do you want to share that?
+
+**Jeff Smith:** Oh, we'd love to. So we have, I would say, an alpha group: friends, families, practitioners, experts that are joining in this movement with us. And so that's a separate link at mychrp.ai. We can put it in the show notes, but it's really just if you want to know what it says about you. It's free, it's fun, and it provides a feedback loop for us, and I would honor the technical savviness of your audience to really teach me something. So play with it and test drive it.
+
+**Daniel Whitenack:** Awesome. Thanks for sharing. We'll link that in the show notes. And yeah, thanks again for joining, Jeff. We really appreciate it.
+
+**Jeff Smith:** I appreciate you both.