/ /

Glen Calvert: Harnessing the Power of AI to Revolutionize Client Services 🤖💼

In this captivating Maker Manager Money podcast episode, Kyle Knowles sits down with Glen Calvert, co-founder of Kaizan AI. This innovative startup leverages artificial intelligence to enhance client satisfaction and retention. Glen shares his entrepreneurial journey, from his early days in traditional publishing and online advertising to his groundbreaking work in the e-sports industry. 🚀

What Listeners Will Learn:

🎯  Passion and Persistence: Glenn’s entrepreneurial journey is a testament to the power of passion and persistence. From starting multiple startups to venturing into AI, Glenn’s story highlights the importance of staying committed to your vision and continuously learning and adapting along the way.
🎯 Embracing AI for Business Growth: Glenn’s journey with Kaizan AI showcases the potential of AI in enhancing client relationships and driving business growth. By leveraging AI models for client priorities and pain points, Glenn demonstrates how technology can revolutionize traditional business practices and improve customer satisfaction.
🎯 Startup vs. Corporate Mentality: Glenn’s insights on the differences between working in startups versus corporate environments shed light on the unique challenges and opportunities each setting presents. His advice to aspiring entrepreneurs emphasizes the need for resilience, adaptability, and a willingness to embrace chaos in the startup world.

Whether you’re an aspiring entrepreneur, a seasoned business owner, or fascinated by the transformative power of AI, this episode is a must-listen.  🎧

🔥 #ArtificialIntelligence
🌟 #Entrepreneurship
💡 #Innovation
🤝 #ClientService
💻 #Startups

In this episode of the Maker Manager Money podcast, I had the pleasure of interviewing Glenn Calvert, one of the founders of Kaizan AI. Glenn shared his entrepreneurial journey, starting with his background in traditional publishing and progressing to his ventures in online advertising and the e-sports industry. He discussed the founding of Kaizan AI, an AI client intelligence platform aimed at increasing client satisfaction and retention.

Glenn highlighted the importance of leveraging AI to augment people, particularly frontline teams, and shared insights into the potential of AI models for addressing client priorities and pain points. He emphasized the significance of building a strong brand, fostering customer love, and creating a community around the brand.

Additionally, Glenn provided valuable advice for aspiring entrepreneurs, encouraging them to start their entrepreneurial journey with passion and a willingness to handle the challenges and chaos of building a startup. He also shared an exciting initiative he is organizing – the Guinness World Record for the longest tug-of-war event, aimed at raising money for charity.

Overall, the episode provided valuable insights into the world of entrepreneurship, the impact of AI on client service, and the importance of building a strong brand and community around a product or service.

Book Recommendations:

Man’s Search for Meaning by Victor Frankel

The Hard Thing About Hard Things by Ben Horowitz

Good Strategy/Bad Strategy: The Difference and Why It Matters by Richard Rumelt

The Innovators Dilemma: When New Technologies Cause Great Firms to Fail by Clayton M. Christensen

The Mom Test: How to Talk to Customers & Learn If Your Business Is a Good Idea When Everyone Is Lying to You by Rob Fitzpatrick


00:00:00 – Introduction to the podcast

00:05:30 – Glenn’s entrepreneurial journey and founding of Kaizan AI

00:15:10 – Explanation of eSports

00:21:46 – Introduction to AI and natural language processing

00:26:58 – Impact of AI advancements on business operations

00:31:42 – How Kaizan can benefit a director of client services

00:36:23 – Utilizing historical data and repositories

00:43:28 – Contrasting corporate and entrepreneurial environments

00:45:56 – Advice for aspiring entrepreneurs

00:49:42 – Fun lightning round questions

Kyle Knowles: Hello there. Welcome to the Maker Manager Money podcast, a podcast about entrepreneurs, solopreneurs, founders, business owners, and business partnerships from startups to stay ups to inspire entrepreneurs to keep going and future entrepreneurs to just start. My name is Kyle Knowles, and on today’s episode, we have the pleasure of welcoming Glenn Calvert, one of the founders of Kaizan AI. Kaizen is an AI client intelligence platform that aims to increase client satisfaction and retention. Glenn is a commercially minded CEO with a wealth of experience in the corporate world and a passion for leveraging AI to augment people, particularly frontline teams, With a background in traditional publishing, online advertising, and the e-sports industry, Glenn brings a unique perspective to the world of AI startups. I look forward to diving into Glenn’s entrepreneurial journey and the potential of AI models for client priorities and pain points. To learn more about Glenn and his company, you can visit his Kaizen.com website. That’s K-A-I-Z-A-N dot A-I. Welcome to the Maker Manager Money podcast, Glenn.

Glen Calvert: Oh, absolute pleasure. Thank you very much for having me on.

Kyle Knowles: Thank you. Did I get that mostly right? Were there any inaccuracies in that introduction?

Glen Calvert: That’s spot on. Kaizan.ai. K-A-I-Z-A-N.ai is the domain name. But yeah, that’s perfect.

Kyle Knowles: Thank you. I think I said Z instead of Zed, but I’m American and so I probably got that wrong, but thank you. Thank you for the correction. So Glenn, what inspired the founding of Kaizan AI and how do you plan to leverage AI to augment frontline teams?

Glen Calvert: Yeah, the founding of Kaizan really has been But many points on the journey have led to this moment and led to this startup. It’s the third venture that I’ve had the pleasure of being involved in starting and scaling. And in the last 15 years, from the very first job all the way through to the last startup, which exited, had sort of seen and felt the same problem over and over again and spent so much time in building products, taking it to market, selling it and then you like have these cherished customers and clients and you need to scale them, you need to scale the revenue of the business and it’s this constant work in progress to nurture it and make sure that everything’s going in the right direction. it’s a lot of effort. There’s a lot of coaching of the teams, a lot of management of the teams, and I’ve always just been intrigued by this concept of the idea that if you were to leverage the collective intelligence of an organization, all of the knowledge, all of the qualities of every individual, and surface that to every individual throughout the day, you’d probably get a very good commercially minded client service manager, account manager, sales manager. And that played on my mind for many, many years, just thinking of all the coaching I was doing and all the management of teams I was doing and just thought, you know what, imagine if you could have me times 10 on every single person’s shoulder every single day. So that was at the back of my mind for a few years. And then language models came along and we realized, you know what, we could probably actually, we could probably realize the dream. So Kaizen went from an idea that had been in the back of our minds for a while to starting up. And then for the last 24, 30 months, we’ve been figuring things out, building the product, building the models, building the data sets, really understanding what the problem is. And we’re away. And so far, so good.

Kyle Knowles: Awesome. And so when did you launch?

Glen Calvert: We have launched, yes. We deliberately made a decision to, we knew this space would move really, really fast. And because we’ve done startups before, we realized that you really want to understand the size of the problem first, because an idea is good, but it’s all down to the execution. And what you don’t want to do is execute on a problem that’s actually quite a small problem. I mean, it sounds good. And, you know, potential buyers will be like, yeah, they’re interested. But startups are 5, 10, 15 year journey. So you really want to make sure you’re on the right path. And you want to sense check that and understand it. So we made a deliberate decision in back end of 21. We said, look, we can see what’s coming around the corner with lounge models. let’s build a little alpha prototype and let’s go to client service teams and let them play with it. But let’s also go to engineering managers and marketing managers and sales managers and product managers and operational and just basically sense check that how we think about this is the correct way. And then from there, we got some more insights. And then in 2022, we could really sort of piece together what this would look like. And at the same time, land models and AI was scaling exponentially. And in 2023, we’ve basically had the beta product in a good place, paying customers on the right path. And we’ll reveal Kaizan to the world this year and let anyone come in and use the product. It’s been a two or three year journey to make sure we’re on the right path. I think it’s been the right decision to make because the space moves so fast, it moves so quickly that you could spend six months building a particular product that could be completely replaced tomorrow by what a foundational model can do. So you’ve got to really deeply understand the use case, the problem space and how to approach it.

Kyle Knowles: That makes sense. And I’ve seen so many changes just since chat GPT was sort of launched to the world a little over a year ago. So have you built this platform to be sort of LLM agnostic or have you tried out different LLMs and found one to be better than the other?

Glen Calvert: So we have tried many of them, we are language agnostic and it’s the right thing to do. We’ve created a platform specifically to be able to plug and play different models. When it comes to anything to do with generative AI, context is everything. The more context you can provide to a model, the better the output is going to be. So there’s actually more work to be done on the infrastructure and the scaffolding required to get all the context you need from multiple data points, multiple data sets. have some understanding of what are the right elements to then send to a model, what are the right things to send to the prompt, and then prompt the different models to do different things. And again, we thought that was the best approach. And I think because the space is growing so fast, you can’t go all in on one particular API or only train your own open source model because tomorrow there could be a variety that comes out that you’ve not even thought about. So you need to be interoperable with different models and also not just have one model in general that you’re using, but different models for different tasks. I think you’re seeing this come to fruition now where you’ve got models for a particular task and other models to sense check that model’s output. And you have other models that are doing things around security and checking on that side. So yeah, many models doing multiple things, but critically, context is king. Context is everything. The better the information you can pass through, the more you can pass through, the better the output you’re going to get.

Kyle Knowles: All right. And it seems like you have client service experience in your background. So can you talk about your entrepreneurial journey? You know, you’ve talked about, I believe, did you say three other startups before Kaizan? Yeah. And how you sort of went from maybe university or back in the day? Did you have entrepreneurial spirit even in your youth and kind of just walk through your entrepreneurial journey, including your client services experience leading up to Kaizen?

Glen Calvert: Yeah, absolutely. I probably was always going to be an entrepreneur without really understanding what that meant, or what that was, what that would entail. But I remember finishing university, heading off traveling for things like 18, 20 months, doing a variety of things in the US, in Asia, in Australia, and some of the Pacific islands, having a great time, just enjoying myself and realizing that at a certain point in the not-too-distant future, life’s gonna get real. I’m gonna go back to the UK, back to London, and have to figure out what I was gonna do with myself. And that happened during while I was away traveling. I was just playing with a bunch of ideas. What do I like? What are the areas I’m interested in? What are the areas that I like? And I remember distinctly thinking, and this is very, very basic in terms of my thought process, but I was sitting there reading a bunch of magazines on a beach somewhere. I like magazines. Magazines are fun. They come out every month. They’re interesting. They’re topical. And I’m interested in lots of things with sports and technology. And I thought, OK, let’s look at that. So when I came back, I just Google what it takes to work at a publishing company. And the very first thing that pops up, the easiest thing that you can do at a publishing company is obviously not be an editor and not write content, but actually be sales and go into brands and agencies and sell the space that you’ve got inside the magazine. this is to be mindful this is a you know this is 15 20 years ago so it was magazines the internet was just just coming about so yeah came back from traveling okay magazines that’s let’s let’s have a look at let’s look at that and then got a job fortunately at a publishing company called Dennis Publishing. Dennis Publishing is a quite a well-known UK publishing company it’s founded by Felix Dennis who’s very well known British entrepreneur. He was crazy. He was highly successful. He was in the top 100 sort of richest men in England for many, many, many years. And the culture at Dennis Publishing was just entrepreneurship. You know, they were the outsiders, they were super successful, but it was not corporate. And you just instantly was in this culture around you know, you were given responsibility and you was given an opportunity to to carve your own way with what you were doing. And that instantly impacted me. So I got that job and I realized and so and then basically the Internet’s coming along and you’ve got all these guys selling sort of iPod accessories and things on the Internet and you’re selling advertising space to them. I’m realizing these guys are younger than I am. OK, I need to make I need to do something about this. So I was probably about 24 at the time. And a friend of mine from school had an idea that he was playing with, and I was playing with some ideas. And we got together, and when we were 25 years old, we started an internet advertising business. And again, it was the right time to be doing it, because most brands were thinking, I’ve got to stop advertising online. Usage was, Facebook had just started, and things were scaling. And brands and advertisers had this crazy, epiphany that they could track things before you book ad space in newspapers and magazines and you know you’re told you would fingers crossed things would work. I mean the internet comes along and you could track the clicks and people’s minds were totally blown by this. So we launched a personalized retargeting business so it would go to retailers and e-commerce companies And then as people would leave those websites, we could then hit them up with adverts, personalize based on what they’d looked at, they’d click back through. The company scaled super fast from four of us. I mean, a little office in Soho, no internet. We had to sort of borrow internet from someone else’s office and have all the cables coming out, out, down the window, the side of the building, into the, through the window. We had desks made of old cupboards. It was a proper, proper startup and it was awesome. And scaled very quick, went from four of us to sort of 60, 70 people in 24 months and just done very, very well. And that company was sold to a US business. And in doing that and building that business at quite a young age, ton of knowledge, ton of experience. And I was still entrenched in media at that time. So the next startup was created, which was another advertising business. Again, quite successful. We had offices around the world, and we just took that concept and made it a bit bigger and a bit wider. At this point, most brands, most agencies were spending a lot of money and focusing on digital advertising and digital marketing, and in particular, programmatic, which was real-time. How do you, like a stock exchange, do you bid in real-time on particular ad units and places? and a whole bunch of knowledge around what you’re doing. So built that business which exited as well. So my knowledge on client service and account management, you’re building these technology companies and you’re figuring out where the opportunity is. And but at the end of the day, these clients, yes, they’re going to come back if you’re performing. But it’s also down to how good the service is and how good you are at with ideas and adding value to their business, not only just the ROI on the spend and the money that they’re providing to you. So I sort of viscerally felt and learned about building sales and account management and client service teams on that on that journey. So that that’s really helped inform Kaizan but during COVID I was taking some time to plan. what effectively has become Kaizen. And a friend of mine works in an e-sports team, and he just raised a series A round of money. He wanted to sort of take all of the hundreds of millions of fans they had and have you sort of commercialize them better. I’d just come off a decade of building commercial teams that were selling into brands and agencies. And he said, look, you’ve got all this audience here. You understand advertising, commercial sort of execution and scaling up beyond the UK. come and spend some time here. I had no clue about esports whatsoever, but I knew it was super interesting. I knew the space was growing really fast. And before I knew it, I was the COO of an esports team. And the timing obviously was impeccable because COVID came along, gaming took off and we had a lot of fun and a lot of success and worked with the biggest brands in the world, introducing them to esports and coming up with some amazing activations. And again, just reinforced the pain points I’d seen around client service and account management, but also just helped me learn and broaden my horizons outside of just pure play marketing. And then that brings us up to sort of 2021 when we thought, right, okay, the future is going to be AI. The future is going to be a lot of client interaction digitally. Let’s launch Kaizen and away we went.

Kyle Knowles: All right, thanks for talking about your journey. Just for the audience’s sake, and for my sake, too. Can you explain a little bit what eSports is? What is that eSports electronic sports? I assume the E stands for but?

Glen Calvert: Yeah, sure thing. So yeah, eSports is competitive gaming. So we all we’ve all games we’ve all played on consoles or PCs or mobiles. And certain games become so popular and so big, and the publisher or the developer of that game actually thinks, I could create a professional league around this. And that’s eSports, it’s professional gaming. And it started off with, as you can imagine, everyone sort of coming together with their PCs and these LAN events and connecting all their PCs and then playing games together. And then I prize money around that. And then it obviously just got bigger and bigger and bigger and bigger. And it’s got so big now where it’s different to traditional sport. Traditional sport, you’ve got a governing body that understands, sets the rules for the sport, but no one really owns the sport. And then you’ve got teams that compete in these structured leagues. But it’s a very similar analogy with eSports, but someone does own the actual sport, that’s the game publisher. So they can create the league, they can decide to say okay let’s take for example League of Legends, which is probably the biggest eSport in the world. and Riot Games will say okay we’ve got tens of millions of users, they’re all competing, let’s create an actual league around this. So Riot Games will create a regional league in the US and Europe and Asia and then all compete and they’ll play in the world which is basically the yearly tournament and Then you’ve got tournament organizers that help create, because it’s quite difficult to run an esports event. Then you’ve got the teams. And where I worked as COO was one of the teams called Fnatic. And each team would then set up these tournaments. They’d compete. There’s prize money. There’s sponsorship. There’s merchandise. There’s ticketing, et cetera, et cetera. And then you would compete, and you’d do very well. A few things that are super interesting around it is the audience. tens of millions of people are watching on Twitch or going direct to the publisher to watch it. So the audience numbers are absolutely huge. And there’s a second revenue stream that you don’t really get inside traditional sport, which is in-game. So all of the items inside the games teams can sell, like their branded jerseys and branded items. So not only are you getting money from sponsors and the usual ways you make money in sports, but you’ve also got this in-game economy that’s appearing as well. So yeah, super interesting. We had 100 professional gamers on the books competing in I think nine different teams, anything from FIFA, the football game, soccer game, all the way through to League of Legends. And yeah, super, super fun, super, super interesting.

Kyle Knowles: So you were organizing a lot of things, not only like as a general manager of a team, you were helping with recruiting and the branding and the advertising and selling jerseys and all that kind of stuff?

Glen Calvert: Yeah, so how we broke it was broken down by you’d have like individuals that would own particular areas. So Patrick, one of the co founders of Fanatic, or one of the early Actually, Guckel Patrick, he was one of the early players in one of the first teams for a team called Counter-Strike. He then became one of the managers and owners. So he’d been in charge of all of esports and all of the basically hiring players, firing players, selling them, and all of the issues that come with the talent. So exactly like American football, soccer, or any sport, there’s issues with talent. There’s the ones who are getting paid more than others and they don’t like it, the ones who don’t want to do anything to sponsors. So Patrick would look after basically the sports aspect. And then I would specifically look after more of the business side. So, okay, what are we doing with sponsors? What are we doing with content? How are we making money? And then just the back office operations. And then you had an e-commerce and a brand department, and their job was to go, right, how do we tell the narrative and the story through content? How do we monetize that content through merchandise and digital items? So we had multiple departments doing multiple, multiple things. So, yeah, and then you’ve got multiple teams as well. So there’s a lot of things that are going on, on, uh, on different time zones across the sporting side, the commercial sponsorship side, the e-commerce side, and then just the branding side and how you sort of activating the brand itself and your sponsors and trying to tile this all together across multiple teams, multiple talent and influencers. It was super fun.

Kyle Knowles: That sounds like a blast. And Twitch was the primary channel where people could watch the sport?

Glen Calvert: Yeah, Twitch was. Yes. So for the most part, Twitch would be where you did have other streaming platforms in China that have got a big audience. But the problem was that once you’ve got your similar to YouTube or Spotify, whichever you where you build your audience, difficult to move them away from that. So a lot of the players that we had, we get big offers to go to streaming platforms in China, but they wouldn’t want to do it because their audience was on Twitch. And then if a game developer is organizing a tournament, they would, for the most part, have an agreement with Twitch. Twitch would stream it and they would basically take a cut of the revenue. Riot, which runs League of Legends, which is the biggest esports and game in esports, they have their own stream platform. So you’d often get these multiple streams going on on their own platform, but also sort of co-stream with Twitch. And that’s the challenge of esports because there’s no media rights per se. In most sports, you’ve got the media rights and that money floods down to the leagues and floods into the teams. Doesn’t really exist in esports. It’s a video native product. Every minute you’re playing, you’re creating content, but it’s given away for free and it’s streamed for free. So you don’t really make much from the streaming side. It was more around get the eyeballs in for free. And how do you monetize them? Well, you can monetize them through winning tournaments, brands, and sponsors. And then if you can do anything on in game and the digital items and commerce and things like that.

Kyle Knowles: Okay, well, thanks for the explanation. That’s really fascinating. It sounds like it sounds like you had a blast doing that, especially during COVID. And then how did you get introduced to AI?

Glen Calvert: My co-founder of Kaizan Pravin, and also he was the co-founder of the last startup that we did together, he’s 20 years in natural language processing and 20 years in being a quite senior software engineer. So very well, very experienced, very knowledgeable with a particular passion for natural language processing. So we did this in our last startup and it was basically how to use the language that you see inside web pages to infer what the topics are and what the content is. And that was called natural language processing, which has evolved into what we now would refer to as large language models that obviously are a lot smarter and intelligent on what they can do. So he had a lot of knowledge in the space. And during COVID, we were going back and forth with our ideas and about what the next startup would entail, how we’d approach it. And he got early access to what OpenAI were doing with their models. And we were looking at the articles that were coming out about how GPT-3 was improving, for example. And he obviously knew a lot around the space. So it wasn’t my knowledge directly that was deep around AI. It was more of a passing interest. I’d read up a lot about it and then sort of have that validated by my co-founder who did understand more about it. And that’s where we got to, you could see where the puck was going and you could see it was kind of inevitable that these breakthroughs would happen and that you’d get to the exponential scale. But we did not envisage the veracity of the speed it’s appeared, especially since sort of ChatGPT came out and that sort of took the world by storm. It was still well known before then, I think that’s sort of a good, it’s a good marker for everyone to think of ChatGPT is like that’s when it sort of went mainstream, but there were still people doing a lot of interesting stuff before then, and we’ve been playing around for 12 months with the models and building the business, but no one expected I think what you’re seeing now in terms of that exponential curve of of capabilities. That is surprising. That is and if you even this week with what’s going on with Gemini from Google in terms of the size of the tokens you can send into the system and also with OpenAI and their new video, prompt to video model. So I think everyone that’s taken that’s surprised everyone just how fast things are moving. The question will be does it continue? Or do we have the usual plateau? Do we have a lot of everyone that’s super excited now, but then we realize the use cases there is on the journey to AGI. Is it going to plateau? Do we have to find new ways to get to unlock the next step? But, cat’s out of the bag, this is now the norm. I mean, you will fundamentally see a shift in how people use software, how people work because of it. So that’s inevitable. It’s just about how fast things are going to go, and then what it will actually entail inside the workplace, and what new products will be created that we couldn’t even envisage before it existed.

Kyle Knowles: What’s the thing that surprised you the most about AI?

Glen Calvert: I would say, I think The capabilities of video is the speed at which that’s progressing is, I didn’t see coming. And the knock-on effects there are huge because there is huge industries that exist around content creation, whether it be marketing, know entertainment and the ability to create anything you want in video form now and it will and again if you’ve seen what’s in the last 18 months that improvement where it’s going to be 18 months from now and then five years from now so that to me really is is striking and I also think When new technology comes along, there’s normally like a five-year period for it to sort of bed in. I think we saw this with the internet, that you have this rapid growth from early adopters, sort of a bit with mobile as well, with social. And when it comes to AI, you’re seeing people moving a bit faster to go from prototype and understanding into generally deploy this and use this within our business. So I think the willingness to accept it inside corporations and enterprises is a good thing. And happening at a speed that we like, because obviously we want to sell to these guys. But definitely on the capability front, it’s video.

Kyle Knowles: It is surprising, and I haven’t been playing around with, I believe it’s called Sora. Is it Sora? Yeah. Open AI. But I look forward to playing with it more over the next few weeks. So I guess when you look at Kaizan, how have these advances changed the way that you’re either, I don’t know, deploying code, writing code, or running the business since you started in 2021? How have the advances in AI change the way you’re doing business or running your company?

Glen Calvert: Yeah, it’s a very good question. When we first started, we were doing a lot on fine tuning, you know, our own models and proprietary models like OpenAI. So we would put a lot of time and effort into fine tuning what you send, you know, what does a good action item look like after a conversation, for example. and a back-end infrastructure that would enable annotators to come in and basically create better prompts and better fine-tuning. And what we’ve seen is actually that’s now a bit redundant because the quality of the models is so good. You don’t need to, per se, fine-tune what’s the best action item from these particular conversations. the sweet spot is more what we call the scaffolding. It’s extracting different data sets from different areas that are disparate and bringing them together and sending them into a model. So the model themselves got so good that if you are now doing that, the smart thing to do is pull in the different data sets from different areas. So we started off with fine tuning and doing our own on our own models. And then we realized, look, actually, let’s be just model agnostic. And let’s get very good at what we call scaffolding. Let’s get very good at, if you’re going to build a very solid solution to go into a company and say, do you want to build an AI that enhances your client service team and eventually build your own autonomous account manager, your own autonomous client service manager? then that entails multiple integrations. We’ve got to think around security and privacy. We have to think around the different data sets to use to make recommendations or to do some generation for them. How to do the best answers to questions that come in from clients. It’s more to do then with the scaffolding of the technology and the data you’re using than it is like which model should we be choosing. So That has been a big lesson for us. If you get very good at building a very good user experience, and all of the scaffolding and the infrastructure, then you’ve got quite a big advantage. Because then, the data flows and the data models are all in place. And all you’ve got to do is plug and play with different and test different models and see, okay, is Anthropic better than OpenAI? Or if we use LLAMA versus Mistral, with these different data points, what do we get out of it? So we put so much more effort now into the data pipelines and the infrastructure that enables us to plug and play different models for different outputs. And then the other side of it is the users. You’ve got to speak to them, you’ve got to understand what they’re interested in, what’s valuable, what’s not valuable, what their pain points are. JAI doesn’t need to solve every pain point. There could be other things that don’t actually require any chat interface or language model. It could be just very basic prompts and notifications based on that helps them. So we turn a lot of our time with users, speaking to users, responding to users, really understanding what the user experience is that they want. Because the UX is a really important point when it comes to this next sort of 20 years of software. It’s going to be sort of AI first. is the user experience has got to be, is going to change fundamentally from what we’ve been so used to with project management boards and CRM and point and click software, that we’ll move into this more intentional user experience where it can do things, we don’t work for the software, it works for us and we’ll tell it what we need. And also offer things that we need to do based on what it knows about us and all our other, what it knows about us. So we’re going to get very good at the user experience. And you only do that by speaking to people. So yeah, in terms of, I can summarize what we’ve learned is you’ve got to just build the, you’ve got to move fast, get the feedback quickly, build the right infrastructure so you can move fast and test and learn, and then be in a position to trial and error, try different models, try different data sets and see what works best for users.

Kyle Knowles: All right, thanks for the explanation. So pretend I’m a director of client services for, let’s say, a marketing agency, and I’ve got a bunch of account managers managing clients. How does Kaizan then come in? Talk me through how a director of client services would engage with Kaizen and what you would do for that particular company.

Glen Calvert: Sure. We’d start off by asking them what’s the biggest issue or pain point or area that they’re looking at. And it would normally be three buckets that are mentioned. One is I have no understanding, true understanding of the health of our client relationships. Second will be around growth. How do I get more revenue from our clients. And third would be more around resource allocation and productivity. So if you can imagine you’ve got sort of a hundred account managers across the world, you don’t really know what they’re working on, on which clients, and is that the right thing or not. So once you understand those three buckets of the resource allocation and productivity, the revenue growth or client health and what they’re struggling with, then we will basically can address that through Kaizan. So what it is, is an AI assistant specifically for the account management team. They log in, they sign in, they bring their meeting assistant, they get a meeting assistant as part of that, their email gets gets plugged in and from there all communication with clients is run through different models, through different things. One’s doing sentiment classification, sentiment analysis, one’s understanding the objections, one’s understanding the really high sentiment moments and the low sentiment moments, we’re looking at the response times, we’re looking at the coverage around stakeholders and decision makers, we’re looking at the productivity they need to do throughout the day, post-call, pre-call, emails coming in, we’re generating the answers for them, we’re generating the copy for them. So it’s effectively an AI system that can tackle those three areas. And then the account manager, the CS leader, the client’s leader, will then lean in heavily and say, OK, for example, client health, I have no understanding. They’re currently using a spreadsheet somewhere. Once a week it’s updated with these clients that are in the red, or they’re amber, or they’re green. And then Kaizen comes along and gives them a real-time view of all client interaction, the quality of the interaction, the sentiment. And then through using it, they can say, well, now you’ve shone a light on what the issues are. And I can now fine-tune the team, and I can fine-tune how we engage those clients, and then ultimately increases the time spent with clients, because we’re taking care of the productivity side of things for them, and we’re recommending who to engage with and how to engage with them. So you increase the quality of the relationships that you’re building. So it’s, yeah, you plug in FieldNAI Assistant and all of a sudden there’s visibility on risks, opportunities, and it’s taking care of a bunch of admin for the team. So it ticks off a few of the pain points that they’ve got. And we’re just getting started. I think where all of our clients, I think this is super interesting, all of our clients have asked us to build this is They have got so much knowledge, all these service companies out there, these agencies, these interesting businesses, so much knowledge to provide to clients. And the problem is just bandwidth. They could bring more clients in, but they can’t just throw more account managers at managing the clients. But it can still service them because they’ve still got the internal smarts, the internal operations and processes to deliver it. So our clients have said to us, could you What does it take to build an autonomous account manager? So within our team of 50 account managers, you’re enhancing them. But is there an individual, like an AI that sits within all of them they can communicate with? And it takes care of a bunch of the Q&A and the reporting on the long tail clients. So that’s interesting because I think we’re so used to in high-touch relationship environments with clients, the client-vendor relation being person-to-person. But I think five years from now, AI-enhanced, AI-augmented will exist. Same way you go onto a retail website now and you engage with intercom and you’ll ask questions and it will sort of give you answers. Where’s my delivery? What’s the issue here with my with what I’ve bought. And then now that’s been taken care of with AI. So I think that customer support that’s being done with AI, we’ll see that move to client service as well. So enhance the relationship managers, but also take care of some of the mundane back and forth that happens.

Kyle Knowles: All right. And I think it was McKinsey had sort of an internal AI thing going. I think they called it Lilly, but they had like, I don’t know, 10,000 documents. And now everyone can draw upon the knowledge that’s in those 10,000 documents. Can you do something, what I was hearing is that you engage and then going forward you have all the help from the Kaizan platform. Can you do anything where it’s looking at historical data? There is a repository of, I don’t know, different meetings that have taken place, different memos that have been sent, those kinds of things, and then build kind of that knowledge base around that?

Glen Calvert: Yeah, exactly. So we’ve started with the area we find the hardest, which is the qualitative communication. So it’s all the back and forth on calls, the back and forth on email and chat. So all of that language, it doesn’t exist inside language models. So it’s super interesting, super valuable. It’s where the most interesting knowledge sits. Yes, there’s good knowledge that sits on site, the websites and all the internal documents. But because it’s quite easy to do, you could very easily spin up a chat GPT that sort of responds and asks questions around your own internal documents. That exists now and people are doing that quite easily. It’s harder to take all of the communication that’s happening within a company and with clients. And there’s a multitude of reasons why it’s difficult. Language, accents, different individuals on different lines of communication, different email threads, different people on them. So how do you start to sort of extrapolate who belongs to which clients or which project, and then really get through all of the gnarly bits around transcription and audio speech recognition? It’s a big, hard problem to have a qualitative data set and bring it together in a way that you can make it useful for a company. Now, in the future, not too far from now, we’re actually layering all the other systems, their CRM, project management tools, HR systems, and anything that’s to do with the internal reporting and documents they’ve got. But that stuff’s a bit easier. That takes, you know, that’ll take a few engineers, four weeks. We’re focused on the qualitative client-vendor interaction data to start with. But it’s a very interesting point because I think everyone’s going to start to look at how you approach throw models at all these data sets we’ve got in these companies, what does it end up looking like? I think there’ll be multiple AIs within a business doing certain things. I think that’s obvious. Then the AI starts to speak to each other, engage with each other. Then you have these AIs doing stuff externally when it comes to sales and lead generation. People are going to be on the other end, probably AIs blocking lead generation and sales AIs approaching them. So it’s going to be interesting how this all plays out.

Kyle Knowles: Yeah, it’s fascinating. So the moat of differentiation for Kaizan, can you explain what that is?

Glen Calvert: Yeah, definitely. So number one is, if customers and clients love what you’re doing, that’s the beast moat. They can come in and they can use the product and they can go if they don’t like it. But if they’re truly enjoying what you built, that customer love translates into dollars, and it translates into referrals, and you build that. So the number one thing, I think it’s wrong for them to go, how do I build a moat here? VCs love to say, what’s the most defensibility around this? They want you to say, I’ve got this unique data set that no one has access to. But the reality, number one, is do users love what we’re doing? Number one. Two is in the brand. It’s going to be so, What’s the user experience? What’s the brand? What do you stand for? How do you articulate that? What’s unique about your brand? And critical within that is your community. So if you’re going to build a brand now, it should be how do you build a community around that brand? How do you engage people into that? And then reinforce and sell yourself. So these are unquantifiable, but really, really important. And they can’t be easily replaced. If you’ve got great customer love through product, and you’ve got a great brand, and a community aspect to it, and great content around it, that’s not easy to to replace. So that’s really, really important. And then outside of that, it’s going to be starting to layer in things to do with what data points you use, how you’ve understood which data points to use and feed to which model. And then that’s a creative performance that you can provide. Network effects will be part of that as well. I think we build an AI for companies to help them engage with their clients. So the more clients they engage with, the more Kaizan is understood by those clients, again, spread by their clients to their clients. And we’re seeing that network effect be pulled in now. But for me, I think that what I really want to get right with Kaizen is the brand. And they understand what it’s, you know, what we stand for in our mission, they want to be part of it. And we know if you’re building an AI for client service, you’re going to be very good at client service. If you don’t get that right, then you’re going to fail. So our brand is going to be, you know, we’re selling quality client service and increasing your client service revenue. We’ve got to be very, very good at that. So that’s what if we get that right, then that that is very difficult to to be by anyone who wants to spin up and try and do something in our space.

Kyle Knowles: That’s excellent. What other ways are you using AI, you know, you’re building AI into your platform, but what other ways are you using AI to help run your company?

Glen Calvert: Yeah. So when we, when we, when we go live, um, sort of come out of closed beta into a, into a proper sales and marketing run, you know, organization, we start to sort of, you know, take, take Kaizan to the world. We’ll use it heavily for. for marketing. We use it heavily for content creation, content generation, and obviously sales and lead generation and filtering of leads and lead creation. That’s all to come. There’s many, many people doing some interesting stuff in that space. How we currently use it is internally, it’s a couple of ways. It’s mostly productivity. So reporting, so we have a ton of information around how people use our products, and we can then send that through into different models to basically get insights from what we see within our own data. And then also easy triaging of information. So when X, Y, Z happens, please then create this and send it to that particular system. So quite, you know, not anything super sexy or interesting, but just helps us make sure we’re not doing anything manual. In the future, I think, yeah, it’d be on the marketing side. How you build that brand and community and you basically leverage a small team of really smart marketeers and sales team to leverage all of these tools. That will be a big part of what we do.

Kyle Knowles: That’s awesome. So it sounds like you’ve worked in maybe not corporate environments as much as entrepreneurial environments. Can you, but maybe you could still answer this question. What’s the difference between being an entrepreneur and working corporate?

Glen Calvert: You’re right to say that I’ve not worked in large corporates. So my comparison will be like a good educated understanding without deep entrenched knowledge. If you work in a startup or an SMB or a scaler, you and your team have to be self-starters. that you have to be able to take on. You’re going to be positive, you’re going to be proactive, and you’re going to take on lots and lots of things. There’ll be many, many plates spinning, and you’ve got to manage and spin those plates, and when stuff breaks, not let it affect you. in a corporate is a bit different. It’s a bit slower. And you’ve got a particular swim lane that you’ve got to be a specialist in and go through and follow the process and make sure that you do it. So I think that’s the biggest, it’s the nature of the And again, I’ve rarely hired people from large corporates into startups. I know that culturally it’s very, very different, but my friends who run businesses have told me that they brought in someone from a big logo and they walk in and they expect process to be there, people to be doing stuff for them because they’ve been used to swimming in their lane, doing their thing. That’s not what it is with a startup. You’ve got to go from zero to one. You need people who can go from zero to one. You can create something from nothing. You can go through walls to make something come to life, which is you just can’t do that if you’re used to just swimming, doing your particular thing. I’m not saying something’s against it, because in large corporates, that’s what you need. You don’t want just total chaos. You need people with deep expertise in particular areas to do what’s required to make the well-oiled machine run at scale. But in a startup, it’s different. You’ve got to find a way forward. So yeah, that’s the That’s the biggest difference in the nature of the personality. Those people can’t easily sit inside a corporate and stay in their swim lane. It’s not enough for them.

Kyle Knowles: That makes sense. Thanks. Thanks for answering that question. And I think you hit it right on the head. I’m sure you have friends that work in corporate as well, or I don’t know, maybe all your friends are at startups, but I’m sure, you know, family members or someone that’s worked in corporate. So thank you for answering that. So, you know, make or manage your money is about inspiring, you know, entrepreneurs to keep going, but also just future entrepreneurs just to start. So what’s some advice that you would give to someone who wanted to start a side hustle or start a first business?

Glen Calvert: loads of advice to provide here and take some of it or little of it. I think the first thing is do it. So one thing we don’t get is time. So if you’ve got an idea and you’ve got to say it needs to be scratched, I think you should, for the most part, do it. I think that I would caveat that and say it depends on what you’re doing. Side hustle is brilliant. If someone’s got an idea around actually going into a startup, deeply difficult things to do. Very, very stressful. It’s either total euphoria or total depression. There’s lots of total chaos. And you’ve got to be ready to take a hit on the hardship you’re going to be taking on for many, many years. So to do that, you’ve got to be deeply passionate. and want to do it. It can’t be a, I want to be an entrepreneur, I want to be, it’s like I’ve got a loose idea and you do it for, you know, you try it and it’s like, it doesn’t work. You’ve got to really, really care about what you want to do and the impact you want to have. But I think start, I think most of the, I’ve got a particular passion for them because I think if you look at the the growth of the human race and the productivity we have, it comes to people just starting, coming up with ideas, trying it, and then delivering it. And that moves us all forward. And so I’m all for more entrepreneurs, trying more things, building more products, using more technology. The more tech that we build, the more product we build, the more value that we can create. So I’m deeply for people doing it. If you are going to do it and you’ve got to then understand what you’re getting yourself in for and be ready for the you’ll have thick skin and you’re going to be get ready to take the to handle the chaos and but ultimately it’s the ultimate journey because you’ll look back and go on the lessons you learn, the knowledge you can create, the value you provide. I mean, the most fun I have is not only working on the product and seeing customers happy, but when you see a team around you that you’ve created and you’ve sort of pulled together, and they’re working on hard problems, they’re solving it, they’re enjoying it, nothing more rewarding than that. Users and customers saying they love what you’re doing, throwing money at you because they love what it is. And then teams working on hard problems, Brilliant, brilliant to see. So yeah, I would, I’d say go for it with some caveats. You’ve got to be really ready for it.

Kyle Knowles: Thank you. I love that answer. I know we only have a few minutes left. So I wanted to get to a lightning round of questions before we close off here. And first question for you is, are you a musician? I used to be. Describe that. What does that mean? Do you play an instrument? Were you in a band?

Glen Calvert: No, I’ve played a bit of guitar in the past, but children and startups have hampered my guitar playing.

Kyle Knowles: Okay. How many children do you have and what are their ages?

Glen Calvert: Yeah, two small boys and one of them is quite into guitar, which is nice to see.

Kyle Knowles: Okay, very nice. So maybe you’ll pick it up again. Definitely. Awesome. Okay. Favorite candy bar?

Glen Calvert: Oh, I’m going to say, do you have cream eggs?

Kyle Knowles: In the US? In the US, we don’t. I’m not familiar.

Glen Calvert: Cadbury’s do cream eggs.

Kyle Knowles: Oh, cream eggs, cream eggs. Okay. Yes, yes, we do. Around Easter. For Easter, the best candy bar. So is it the one that’s yellow cream inside or white cream inside? White with a touch of yellow. So technically, I’m betting. Right, it’s like an egg. It’s when you break it.

Glen Calvert: Exactly. I’m bending the rules on candy bar, but cream egg.

Kyle Knowles: That will work. That will work. Candy. Yeah, any candy is great. Favorite music artist?

Glen Calvert: So, alive, I’m going to go with the Arctic Monkeys. And dead, I’m going to go with Jimi Hendrix.

Kyle Knowles: Very nice. Favorite cereal?

Glen Calvert: Not a big serial fan. I’ll go with Frosties.

Kyle Knowles: Okay. Mac or PC? Mac. Google or Microsoft? Google. Dogs or cats?

Glen Calvert: Dogs.

Kyle Knowles: Phantom or Les Mis?

Glen Calvert: Les Mis.

Kyle Knowles: Okay. What’s something that most people don’t know about you?

Glen Calvert: This year I am organizing the Guinness World Record for the longest ever tug-of-war event. So my boys and some of their school friends wanted to raise charity, raise money for charity and do it around events and had the idea around the biggest tug-of-war event. And I approached the Guinness Book of Records, they’ve approved the application and we are now going to be doing that in the summer. So I haven’t spread the word yet because I need to, because I’ve been busy. But yeah, people don’t know that, but soon I’ll be announcing that we’re running the biggest kids tug-of-war competition that’s ever taken place.

Kyle Knowles: And it’s going to take place where? In London?

Glen Calvert: In Kent, just outside London, yes. Okay. It’s got to be, it’s going to be just shy of a kilometer in length. So it’s going to be a lot of kids.

Kyle Knowles: That is a lot of kids. That’s really exciting. What’s the book you recommend the most to people?

Glen Calvert: Oh, okay. So if it’s, I’ve got a few. It can’t be just one. So if it’s especially starting to do startups and entrepreneurship as your audiences is for, I would say if you’re just starting out and you’re on the journey to entrepreneurship, then Man’s Search for Meaning by Viktor Frankl is the one that is like, how to control your mind and control the hardship that approaches you. Specifically around startups, Ben Horowitz from Andreessen Horowitz, the venture fund, the hard thing about hard things is like the most visceral example of the startup journey and the hardship that you go on. Then more tactical, good strategy, bad strategy, how to think about your business and what to be doing or not doing. the innovators dilemma, like very present at the minute because of AI and how it’s going to disrupt the incumbents. So that’s a spot on book. And if you’re just starting out, a must have is the mum test, which is how to interview customers and how to interview who your potential customers are, to understand have I got a real, I’ve got a real problem here, or is it a bad idea?

Kyle Knowles: And that’s called the mum test? The mum test. Yes. Okay. MUM. MOM test. MOM and does MOM stand for something?

Glen Calvert: No, just the mum. Okay. As in the mum, the mum test is basically, yeah, if you are going to uh how you interview people and you people if they would normally come up with an idea for a starter to go to their mum and say i’m going to do this and she say oh yes darling that would be great that’s not the way to to do it it’s not the way to do it there’s a particular way to extract knowledge and insight from potential customers and the mum test is a quick book that says do these things ask these questions and you’ll be a lot further down the line from um if you if you follow that

Kyle Knowles: These are excellent book suggestions. Well, thank you, Glenn. I’ve been looking forward to our discussion since you are a co-founder of an AI First company. I want to thank and give a shout out to Stephen Caron. Episode, I believe, number 25. He’s the one that referred me to you and we were friends and there’s a connection there with Kaizan and Skel. Yeah. So I’m so, it’s just been my pleasure and I’m so happy that Stephen introduced us to each other. And I just want to thank you for being so generous with your time today and all the things that you’ve shared. This is going to be, I can’t wait to publish this episode because there’s so much knowledge that you’ve shared for people who are either entrepreneurs or future entrepreneurs. So thank you, Glenn.

Glen Calvert: Awesome, Garth. Absolute pleasure. Thank you for your time. Enjoyed it.