Episode Transcript
[00:00:00] Speaker A: Foreign.
[00:00:11] Speaker B: Welcome to the Victory Show.
Hey, victors. Welcome to this episode of the Victory Show. If this is the first time you're joining us, I'm Travis Cody, bestselling author of 19 books and the creator of a bestseller by design, where I've had the privilege of helping hundreds of business consultants, founders and entrepreneurs write publishers their own bestselling books. And through that journey, I've discovered a fascinating pattern. Most businesses really struggle to break past that seven figure per year mark in revenue. So on this show, I sit down with some of the world's most successful founders, CEOs, leaders and business owners to uncover the strategies they use to scale way past that mark so that you can do the same. So get ready for some deep insights and actionable takeaways that you can implement in your life and your business.
Starting now. Today's guest is building the next generation of customer experience. Powered by AI. AI backed by data and built for speed. Ibi Syed is the founder of Kotera, a platform that helps companies like Bilt, opensea and Coterie. Am I saying that right? Automate insights, identify bugs before they escalate and unify customer data into intuitive dashboards that teams can actually use. Go figure. With a degree in applied mathematics from Harvard, IBB got his start at as Peloton's first data scientist where he scaled the global data team and rolled out production grade machine learning systems. And now through Kotera, he's helping brands use AI agents to close the gap between customer signals and action faster, smarter and with fewer blind spots. And he also hosts the podcast Numbers and Narratives where he explores how data connects marketing, product and customer experience in the real world. So whether he's advising seed stage startups or decoding predictive models, IB's mission is clear.
Turn raw data into clear decisions. IBI, thanks for being here.
[00:02:00] Speaker A: That's a, that's quite the intro. I, I didn't write that one. So whoever did, like, very, very good job.
[00:02:07] Speaker B: That was me. It's like I know my way around a keyboard or something.
[00:02:11] Speaker A: I am incredibly impressed.
Yeah, we, yeah, that was, that was, that was, that was, that was great.
[00:02:19] Speaker B: I gotta, before we jump in, I gotta ask, like, did you know you were gonna go into math from a little kid? Because applied mathematics sounds very intense to me.
[00:02:30] Speaker A: Applied mathematics is one of those things where it's exactly like the reason that people get that degree is here's a little secret. It is like, it is kind of like a nothing burger degree. Like there is like, there is like real mathematics. Like we called it Pure mathematics when I was at college, when I was in college.
And that's like, you know, you, you, you study proofs, you study proof based mathematics. Applied mathematics is basically like, I don't know what to get a degree in, but I want it to sound vaguely, I wanted to sound vaguely like stemmy, but I still want to like keep my options open. And that was like kind of what it was. I actually ended up, I actually ended up like concentrating in statistics. So that's like, like that's what my degree is in.
But yeah, like very much didn't know what I wanted to do. Know what I knew that I wanted to do something like vaguely stem oriented.
And that's funny.
[00:03:22] Speaker B: So for the, not the non, the non stem version, that is psychology.
I got a degree in psychology.
[00:03:29] Speaker A: Exactly, exactly. Which is what my wife got a degree in. But then she ended up becoming a doctor. So like, honestly, like, you know, it's fake degrees, fake degrees all day, fake degrees all day.
But yeah, like I, I grew up, grew up in Omaha, Nebraska. Wanted to make sure that I, you know, like a good, I don't know, whoever could see my, I don't know if you can see my face in this, but you know, like a good child of, child of Asian immigrants. You know, I wanted to impress my parents and make sure that I got something with a vague stem background. So that's how I ended up there.
[00:04:02] Speaker B: I was going to say, with a name like Ibisaed in Nebraska.
[00:04:07] Speaker A: Yeah, it's honestly. Okay, so this is a, this is a pretty. Where are you? I actually don't know where you're recording this from.
[00:04:12] Speaker B: I'm, I'm actually in Las Vegas.
[00:04:14] Speaker A: You're in Las Vegas? Okay, okay. Okay. So, so.
[00:04:16] Speaker B: But I grew up in Utah, so like, you know, it's probably feels like the same demographic.
[00:04:22] Speaker A: It's, it's, it's pretty similar. Almost fairly decent because I think at least at the time that my parents moved, it was a low cost of living with like a high ish number of Fortune 500 companies. You could like get a job. I don't know how true that is yet anymore, but that was definitely true back then.
And that's how my parents ended up there. It's surprising. It's like Omaha, like the greater Omaha area is like a metro area of about a million people. So it's bigger than you think. But it is like very much like most of the suburbs are, are just kind of strip mall after strip mall.
[00:04:59] Speaker B: So were you, were your parents first generation?
[00:05:02] Speaker A: Yeah, so my parents Came here for college.
They, like, lived on the east coast for a while. And then. Yeah, they might. I think my dad. The story my dad likes to tell is that, like, the company is working for. Got acquired and they gave him the option to move to, like, just outside of la, so Irvine, California or Omaha. And the. Basically, like, the only decision, the only thing that him and my mom wanted was a house. And so they're like, oh, like, how much does it cost to get a house in both of those places? And I think Omaha was like a tenth the cost.
[00:05:29] Speaker B: Yeah.
[00:05:29] Speaker A: Of Los Angeles.
[00:05:31] Speaker B: But after about three winners there, your mom was like, what did we do?
[00:05:34] Speaker A: What did we do? Yeah, no.
Yeah. So that was. Yeah, they, they. They definitely. That's. That's where I ended up. Good place to grow up. Honestly, like, it's very similar to a lot of the other American, like the great American suburb.
Very much the great American suburb. Like, I.
[00:05:53] Speaker B: There's a reason the phrase American Pie exists.
[00:05:56] Speaker A: Yeah, yeah, yeah, for sure. For sure.
[00:05:59] Speaker B: Well, that's awesome. So. All right. So you wanted to impress the parents. So did you always know, like, something in stem, like some sort of technology? Were you just kind of that way when you were younger? Or was math just super easy to use? So they were like, go that way?
[00:06:13] Speaker A: It was vaguely stem. Like, I don't think I was, like, particularly good at math or anything. I just ended up.
I was always like a tinkerer. I think that it just kind of came a little bit more naturally to me.
I was, I like to say, like, very much like Jack of all trades, master of none. I did okay at most subjects. I don't think I had a favorite. I. I kind of was just like, pretty aggressively, I like to say aggressively mediocre.
But I. I did always love to tinker. I always liked building things. I always liked doing business. And then I took, like, my first day statistics class in high school.
Ended up really liking it. Thought it was really interesting. Took a handful more in college, and that was kind of like what started it. I. I ended up being. I ended up joining Peloton pretty early. I was. Yeah, I was their first data science hire. They had, like, analysts and a handful of other folks on the product side, but they didn't really have, like, a data science team. I got hired by one of the co founders, Graham Stanton, just very luckily while I was still in college and ended up just working there. And what was great about Peloton, and it still is, is it's a company that very much, at least from its ethos focused on just making a great brand and a great customer experience and using data to actually solve problems. So oftentimes what happens is there's this huge disconnect between business users.
So think like a marketer who wants to predict churn to be able to send an email to get somebody to come back onto the platform. Right. So like that's a business user's use case for a data model. Data scientist hears that, goes into a hole for six months, hacks up like some sort of like code thing and then presents the code thing to the marketer who goes, what the heck am I supposed to do with this? Like, what do you want me to do with this? Like, I want to send some emails. What am I going to do with this random like series of code blocks that you gave me?
And that was always like something that Peloton strived to, which was, you know, the you, your customer is like the business person.
And so we got to build a lot of really interesting things. There's that bridge, that gap between all of the data that the company had and actually solving problems for customers and all actually solving problems. And that was, that was kind of the impetus for Kotera.
[00:08:44] Speaker B: So that was. Well, and I was gonna say what's interesting is for Peloton, that's like almost the opposite of what not only just kind of the startup space is, but especially the high, high end fitness space.
[00:08:55] Speaker A: Yeah, right.
[00:08:56] Speaker B: It's like we're doing the thing and you're gonna like it. So that I really find that fascinating. I had a conversation, I'm sure you're familiar with this. I had this conversation a couple of months ago. But just like the idea of the founder of Doordash made it so that the programmers and engineers had to spend an eight hour shift doing customer service every month. Oh yeah, and they were like, had all a bunch of engineers quit and everybody's mad. But then the engineers, when they got there and we're getting all customer support tickets, they're like, what do you mean? And then he's like, this is what the thing you're building's doing.
And it wasn't until they were like, they didn't get it from the user perspective because they were also enamored with like the tools and the bells and the whistles and what it could do. But then nobody knew what to do.
[00:09:38] Speaker A: So I think a high degree of customer empathy is like, I think it's like one of the most important things that you could have.
We actually work, we not only work with customer Experience teams. But we've actually kind of expanded beyond that into using these AI agents to, you know, help product teams, help sales teams, help a bunch of other types of teams within business, within businesses really like listen to their customers, use these agents, use their data in a smarter way. And it is just really, really interesting how much information is present within an organization's verbatim data, right? So think customer support tickets, think product reviews, think transcripts from sales calls, right? You can really, you know, you can really take that information and really utilize it to make faster decisions, make better decisions.
Really listen to your customers.
I always like to say that engineering teams are a very, you know, finite resource that is quite expensive. And so if you're going to use, you know, if you pay, I mean, let's run some, let's run some math here. If you're paying an Engineer, you know, $250,000 a year, that person works, you know, 52 weeks for 40 hours, you're paying basically $120 an hour per engineer. And so, you know, what, if you're going to solve a customer, if you're going to solve a problem, if you're going to set a roadmap for your business for what to build or what to fix, what is going to get you the maximum ROI on an engineer's time? And most businesses, you know, they have a really, really high level approach to setting a road map that's very, very long winded. And what we find more and more people are doing is they're looking at what customers are saying, what customers are complaining about, what customers are, you know, putting out there on Reddit as like a value prop is, is what these teams are taking in, analyzing with AI and then using that to actually like make make decisions on, on what to build and what to.
[00:11:45] Speaker B: So how long were you working with Peloton before the idea for Katera started to.
[00:11:49] Speaker A: Kind of about three years.
It was like about three years we started and we, we did YC and we kind of pivoted around a little bit.
But yeah, we, we basically did like data consulting for like the first, honestly like the first year of Kotaro's life until we really realized, hey, like, there's actually a thing in this, like turning. And it was around the time that ChatGPT was becoming a thing, a lot of people were trying to make sense of their qualitative data and, and that was kind of one of the only ways of doing it. And so that's where we started.
[00:12:22] Speaker B: Wow. So walk me through then. This just did the first year of Kotera. So you were with Peloton. Did. Were, were you building Kotera kind of on the side while you're still there and then jumped off or would you. And when did you get to the point where you're like, oh man, I've got to like, you got to make the choice of which one you're going to continue down with?
[00:12:40] Speaker A: That's.
Yeah, the story is a little bit interesting. It was, this was like definitely just the Zerp era to some extent. Like I was at Peloton, I knew kind of what we.
I knew kind of what we, like what was. I knew how to do my job pretty well. But I don't know. You, you know, you get good at a thing and you can kind of continue it. I would use my off hours because it was still Covid time. Right. I was still working from home. There was not really a ton going on.
And so in the evenings, on the weekends, I would be hacking on different ideas, different things.
I also was actually a part time investor at a venture fund and so I would need a ton of startups doing that.
And between those two, there was really this key understanding that Peloton had this issue where people were trying to use data to actually make decisions but couldn't. And there are a ton of other people that were trying to do the same thing in this space where it's. All of my data's in a data warehouse, some sort of system to make sense of that data. And then I want to pass that data into an external system where a business user can use it. So hark back to my like, example for the marketing team. You know, all of our user data was in the data warehouse. The marketer wanted to, the marketer wanted to figure out like, who do I email?
And the, the key there was we had to develop some sort of model that, to take in that user data and say, hey, is this user doing good?
Are they performing, you know, the way we expect on the product, or is their product usage going down?
And you know, that's a very simple, simplest way of putting it.
Now when you think about it, I Travis, I assume you use ChatGPT in your, in your off time.
[00:14:34] Speaker B: Been known to play around with some chats.
[00:14:37] Speaker A: Exactly.
You know, like I could give you a huge code block that takes some data and exports a number. Or what you could do is you could go to ChatGPT and say, hey, I'm going to give you how much a user used our product five months ago, how much our user uses our product. Today and how much a good user uses our product. I want you to score user. The user that I give you on a level from one to five.
You tell me a five being great, one being bad, and suddenly like, you've taken this thing that took six months before to create a perfect model and, you know, made a sort of hacky version of it.
[00:15:18] Speaker B: Yeah.
[00:15:18] Speaker A: And that's what that was. What we were doing is connecting people's data to these models for at least at the beginning, exporting a number and then using that too.
[00:15:28] Speaker B: Wow. So when you. When you got started, did you bootstrap or did you have. Did you know somebody that put a little startup funding into you or was that all. All out of pocket?
[00:15:40] Speaker A: My co founder, the way I found him was he actually had just got into yc.
I had met him trying to invest in YC companies. I met him, joined him very, very soon after I left Peloton. I left the venture fund and you know, we raised a little bit of money coming out of that and we've raised a little bit more money since.
[00:15:59] Speaker B: So what. Walk me through what that first year looked like for you guys. Then you've left Peloton. Were your parents like, what are you doing?
[00:16:05] Speaker A: They were honestly, like, very, very supportive. I like, I like went to them. I had. I had like a handful. It was, it was definitely. I was definitely sure it was time to leave Peloton.
I had a handful of job offers again. Zerb era. Like, I could have gone and become an investor full time. I could have gone and done the Peloton thing again, which was like, join a small company, write it till ipo. Write it till, like, growth, you know, you. It's a. It's a good financial outcome for somebody who's in like their early 20s. If you can do that properly, if you can do that twice, it's not a bad place to be. Or it was this like, mystery of go and start a company and see what you can do with it. That.
And I did that. Like, I did. I took the third option. My parents were very supportive. My family, my. My then girlfriend, now wife was very supportive of it.
And it was. The first year was just us.
We had a thesis. We emailed people, we very much. YC teaches you. And a lot of like, sort of incubators teach. This is like one person builds, one person does marketing and sales. And that's what we did. We like eat cold, emailed tons and tons of people a day. We use part of that money to hire an intern.
That intern was A sophomore at the time, she's now or she was one of, she recently joined us full time, which is awesome. As an engineer of all things. So she was helping us write cold email. We were just emailing people, talking to them about what we were building, talking to them about what we were doing.
And yeah, that was, that was, that was it. We had a thesis, we pitched it to people, we said, hey, we'll do this for you for really cheap sometimes. We said, we do it for free. Don't do that like people. If you say you'll do something for free, everyone will take you up on it. You don't actually know if you've got real market demand. Don't make that mistake.
And that's what we did. We did that for a year and then we, we found a handful of people that kept asking us for the same thing, which was, hey, I have all this customer ticket data I need to make sense of. Can your AI like read our customer tickets and categorize them and, and pass the data back into our support system so that we don't have to have, you know, our human agents spend a lot of time doing manual ticket categorization? And we're like, sure, okay.
And yeah, then we were off to the races and that was what started our new agentic platform.
[00:18:30] Speaker B: Wow. So, I mean, what a novel concept.
People are asking for something and enough people are asking for something and you're like, maybe we should build what people are asking for. Yeah.
[00:18:40] Speaker A: Yeah.
[00:18:42] Speaker B: What a brilliant concept. So now, today. So how, how long, how old is Kotera now?
[00:18:47] Speaker A: 2 and a half, 2 and a half years.
[00:18:49] Speaker B: And what's your team size now?
[00:18:51] Speaker A: About 11 people.
[00:18:53] Speaker B: Wow. Wow, that is fantastic. All right, so you know, part when you get on the Kotera website and you look at it, a lot of it is like this whole, you know, idea of like AI agents and what that.
So I work with a lot of people that are very successful entrepreneurs.
AI is moving so fast, most people are getting overwhelmed and confused.
[00:19:15] Speaker A: Oh yeah.
[00:19:16] Speaker B: So let's walk through a little bit about what an AI agent actually is and why that's such a big deal, especially for let's say mid sized businesses making a million to like 5 million a year. Yeah, of course, I guess that'd be a small business, but yeah, I mean.
[00:19:33] Speaker A: It depends on, it depends on who's like bootstrapped. Great. Like that's a great place to be. It, it all depends on the timing that you're in.
Yeah, let's, let's focus on a very specific Use case.
I'll put myself. I'll put myself into, like a salesperson shoes. And specifically, Travis, I'll try to put you. I'll try to put myself in your shoes.
There are probably constantly, you know, people talking on LinkedIn and talking on Reddit about, hey, I am. I'm an entrepreneur. I'm a vp. I, you know, I'm some person who has done something. I want to write a book. How do I do it? Right? That is something that you are one of the best people in the world to go to and ask for and get advice on and get help from. Right now. You are a. I don't know how big your team is, but if you're starting this business from scratch, you're saying, hey, I want to go out and I want to talk to founders or does that. Want to write books?
I want to find them where they are. They're most likely talking about this on LinkedIn or Reddit.
Okay, I want to create an AI agent. Now, there are sort of. There's a lot out there, right? The most, Most of the. Most of humanity is going onto something like a chat GBT and speaking to it like it's a search tool, right? That is kind of like how we interact with AI. How most people interact with AI is through this, like, chat bot interface.
And so maybe you'll start with saying, hey, like, what subreddits should I go to? Or who should I go to on? Like, who are these people? Who are. Who are people on LinkedIn that I should maybe talk to? And you'll see the chat GBT thing, do a little bit of, you know, it's a cursory amount of research, and it'll, like, respond and say, hey, these are probably good subreddits that you should go try, right?
And you'll say, okay, cool. And then you'll spend a few hours a day probably scrolling those subreddits, reading what people are putting on there and then responding in the chat whenever somebody says, hey, I'm looking for help writing a book. Does anyone have any advice? You can say, hey, my name's Travis. I have this cool podcast. I have all these 19 books. You should go check it out. By the way, PM me if you need any help, right? And you're just doing this manually now, you can upgrade that process with what's called workflow automation. So you think make.comzapier n8 and these are not agentic products. These are workflow products.
You can, you can have them talk to an AI and they can, you know, do some AI capabilities, but they do not make their own decisions. And so that makes them a workflow product. So then you can say, okay, I want.
I want to automate this process a little bit. I don't want to be notified unless there is actually a. You know, I have my list of subreddits I have. I go through them every single day, and I respond to these. I want to automate a portion of that, which is the sourcing.
Now, you can set N8N to go visit. You know, you can pull an RSS feed basically of all of those subreddits every single day, and you can pass it to like a Claude API or something and say, hey, here's like 10,000.
Here's today's. All of today's posts. Put it into memory.
Return me the ones that you think are good, and it'll do that for you once a day. And then you don't have to. Now scroll. You've saved yourself like a few hours a day. What makes that truly agentic is.
And this is kind of where I come to is we operate on three core primitives. One is the prompt. You should not have to wire any API calls or any blocks or anything together. If you want something to be truly agentic, it has to have a single point of input, which is a prompt that has all of the information that the AI needs to be able to do its job. The second part is you need to give that AI access to very, very specific tooling.
We actually at Kotera do not. We're not huge fans of mcp. I know MCP is all the rage on the Internet these days, but I do not want to give my AI access to the kitchen sink. I don't want to authenticate with Slack and have it be able to read all the messages and. Because what'll happen is, if I'm running that AI 10,000 times, it'll probably mess up two of those times, but it might mess up very, very crucially. So I want to give it very specific actions. You can go read a Reddit thread, you can go make a Google search, you can go do these things, things I will give you a specific set of tools to do. So now I have a prompt, and I have a specific set of tools.
And then three is access to data. We, you know, what data do you need? Whether that's data to be able to make a decision, or data like a, you know, a spreadsheet to be able to be able to run through.
The most important ones are the first Two though the prompt and the tools.
And so now what I've done is I've said, hello, Mr. AI agent, you are now alive. I have given you access to run a Google search and go visit Reddit. I want you to every 10 minutes. And I've given you access to like a, like a Google sheet, right? I'm going to make you run off of a Google sheet. What I want you to do is I want you to every five minutes run a handful of searches on Google, figure out if anyone has posted anything on Reddit.
Anything. Just, just search for founders that are looking for founders that are looking for help writing books. We. I don't care what subreddit it is. Here are some that you should explicitly check, but I want you to check all of Reddit and I want you to use Google as the search engine and then I want you to dump all of the resulting threads that you think are good inside this Google sheet. And I've given it access to do a Google search, pull a Reddit thread and add to a Google sheet. But crucially, I have not wired together a bunch of disparate API calls. I have just done it through a prompt. And that is an agent because it can make decision making power and it has the ability to route itself its own way, use tools in whatever method it thinks is feasible and use whatever underlying model provider it wants. I've been monologuing and I hate it when people monologue on my podcast.
[00:25:51] Speaker B: No, I'm just like, I'm so like, what you're sharing with me? I was like, no one's ever explained it that way before. Normally I have tons of questions right now. My brain, I'm just listening. And I was like, I didn't know that. I didn't know that either. I didn't know that either.
[00:26:02] Speaker A: And that's how we think about it.
[00:26:04] Speaker B: So let's back this up then and go, okay, from your perspective again, taking like a, you know, basic business of, I don't know, 15 employees or less, what are, what are three critical ways they should be using agents in their business right now that most businesses just aren't.
[00:26:22] Speaker A: This is a really good question.
Three, the three top ones this is.
And let's say that this is like a B2B business. I think one of them is just definitely outreach. Like, I don't think that we should be like performing AI slop. I don't think we should be like spamming people or doing anything like that. But like you actually outreach to me from somebody on your team, right?
[00:26:48] Speaker B: Yep.
[00:26:49] Speaker A: Yeah. One thing that somebody like that should be doing, and there are other companies that are good at this, like Clay is another one is doing lead scoring. Right. Hey, I have this list of maybe you're, maybe the people that you want on the podcast is every single series A plus founder, right? So I want to, I want to target everyone who has at least raised a Series A.
You know, sales and outreach is number one. Create an agent that does lead scoring and figures out who your top prospects are based on what you care about so that like, you know, every time you sit down and record a 45 minute podcast, it's with someone who like really matters. That's, that's probably one. Outreach and sales is, is a no brainer. Second is customer success and customer support. Customer support specifically. Your customers are constantly asking questions. Your customers are constantly asking for help.
Develop some sort of system, whether that's in Slack or whether that's on your customer support channel that takes all of the simple things and, and all of the simple questions and answers them automatically for customers so that your customer support people can spend time doing what actually matters. Rather than like, you know, if you're an E commerce platform, for example, rather than like, you're probably going to get like, please cancel my order or where is my order request like 10 times a day. You can easily build an AI agent that looks up that information whenever a query like that comes in so that your, your customer support agents can focus on the, the things that actually matter. Hey, like I got this product and it made me sick. Like, please help me, right? Like those are the people that you want to spend the most time with, not the ones that are asking a very simple query.
So that's most likely number two and number three is brand.
Number three is brand and marketing.
There are tons and tons and tons of place. There's tons and tons of whether that's, you know, sit on top of all of my customer calls and generate a case study from each one automatically and send it to, send it to get approved by the customer after every call. You know, a customer will oftentimes drop like what they found valuable in a, in a conversation. Have an AI agent use Gamma to generate like a very custom case study and just, you know, submit it to the customer and say, hey, like we actually generated this case study. Is it okay if we post on our website nine times out of ten, I'm sure they'll say yes, like if they're happy with the product and they'll say yes. Use that as free marketing, right? Like use that to generate content, to put on social, use that to generate ads.
That's really, really important.
I don't know. Those are just three that come to mind immediately.
[00:29:32] Speaker B: That, yeah, those are all very, very simple. So, so if somebody has not started using agents in their business yet, where do they start? Do they come to somebody like Kotera or do you, are you guys very specifically focused on like a particular type of business?
[00:29:48] Speaker A: We are, we are not.
The, the way that I would try doing it is I think a lot of people are. I think a lot of people come to us to try to automate the wrong thing. Think like a lot of times, like we say no to customers more often than we say yes because it's a bad implementation of AI and I don't blame the customer for this. Like you would be surprised the amount of time somebody who sits on like a product team that comes to me and says, hey, I want AI to analyze all my data and tell me like stuff like we want to like, we want to like figure out why like our churn numbers are high. And it's like, dude, like that is something AI is going to be really bad at. Like you should not use AI because you have a lot more context than AI ever will. It's not going to be able to like do. It's not going to be able to tell you how to like make your bad business and do a good one.
That is something that only you can do. What you need to do is you should remove the repetitive task load that you have in your head, right? Like there are, there are things that I like to call TV TV problems where like you can, you can do the work while you're still watching tv.
You should try to remove that, like take the things that are repetitive, point and click. I'm doing the same process over and over and over again. It takes me half an hour, 45 minutes, an hour, you know, like generating, generating a one pager for all of my customer calls. At like each customer success person will take a call. They'll have to like update the CRM, they'll have to write a one pager, they'll have to write a follow up email and send to the customer automate like 99% of that work so that the one pager is done by the time of the call is finished. The CRM is updated with the tasks, the asana board is updated with the tasks and you have the email drafted so you can proofread and say, okay, cool, this is great, let's hit go.
Like that's the kind of thing that you need to think about, that you want to automate.
[00:31:55] Speaker B: I love that.
What are your TV tasks? If you can watch Netflix while you're doing it, that's, that's the first thing that needs to get off your plate.
[00:32:02] Speaker A: Exactly. Focus on your Netflix. Right? Like, don't work from the hours of seven to nine. You don't need to do that. Like, like there's all these things that we leave toward the end of the day and we do. And we have the minimal amount of downtime. Just take the downtime like, or if you want to like, be more effective.
[00:32:17] Speaker B: That's not American culture. I must be hustling all the time.
[00:32:21] Speaker A: Yeah, listen, you live in Vegas, I live in New York. Like, if you're not working, if you're not 996ing, what the hell are you doing?
[00:32:29] Speaker B: So what, like from where you're sitting and what you're involved in, like, especially. Let's just keep it, let's keep it short. Right. The next five years, what's got you super excited with where agents are and where, where you think they're gonna, we're gonna be with them in five years.
[00:32:45] Speaker A: I think we're at the beginning. Like, I think, I think that we're very, very much at the impetus. I think that like a lot of people have tried doing a lot of things in house and they haven't worked. I think what we're starting to see is like, we're starting to see these businesses emerge that are hyper specific use cases of AI. I think that similar to, you know, similar to how somebody ate a good chunk of this market. I think that there is space for a more generalist product that anyone that can write in any language, it doesn't even have to be English anymore, can easily just explain what they want to a bot, give it access to things and have it like save them a significant amount of time per day.
And the thing that I'm excited about is people actually going past this, what can I do with AI and into actually exploring and making things and, and starting it. And we want to be at the forefront of helping, actually educate people and teach them and make it really easy to just get started. Right. Like, I have something I want to automate. Like a great example of the Reddit thing that I was talking about earlier.
The we actually have a bot that goes through all of the common subreddits and whenever someone says something like, hey, I want, I'm trying to use AI in my business. How do I do it, it notifies me so I can go write and be a part of that conversation.
The biggest question that people have is like, how do I prompt? How do I actually use this? How do I get ROI from this? We start to go from the how to the okay, cool. I'm now doing it. And we want to kind of be at the forefront of that for people.
[00:34:24] Speaker B: So who is sort of your, like the, like your ideal client? Like, who's the company that you guys can just knock it out of the.
[00:34:31] Speaker A: Park for fast growing companies that are series B plus.
[00:34:35] Speaker B: Series B plus, that's fantastic.
[00:34:37] Speaker A: Fast growing series B plus companies.
[00:34:39] Speaker B: So if somebody is, is listening to this, the episode, or they're. They're reading a chapter in the book, like, and they're like, oh, my God, this is amazing. Where's IB Been my whole life?
Where do, where do they go to find you here?
[00:34:53] Speaker A: There's two places you can find me on LinkedIn. I'm just LinkedIn.com in Ibby. That's Ib as in boy B as in boy Y. Or you know what, you can also just. You can text me. Send me a text. 402-446-88492.
[00:35:07] Speaker B: Wow. Dropping this phone on the call. Way to go.
[00:35:10] Speaker A: There you go.
[00:35:12] Speaker B: And if they want to. Let's talk a little bit about your podcast. The numbers and numbers and narratives. What. What is that?
[00:35:19] Speaker A: And Travis, we, we need to have you on as a guest.
[00:35:22] Speaker B: All right, I'll come and do it.
[00:35:23] Speaker A: I've said a lot about myself. I need to hear more about how you got started and learn more about you.
[00:35:29] Speaker B: So what do you guys cover in numbers and narratives? Just how to use data to.
[00:35:33] Speaker A: How to use data and AI to further your business. That is, that is the actual, very, very simple premise. We have people that are marketers, we have people that are product managers. We have customer experience, customer support. We don't care who you are. If you are trying to. If you are using AI and data in a clever way to improve some portion of your business or some creative business of some kind, we want to hear from you. We would love to hear your story.
We'd love to. It's part of our. It's part of our whole brand ethos. Please, please reach out to me. I think it's Ibby at Numbers and just do Ibby at Koterra. C O T E R A dot Co.
[00:36:13] Speaker B: So for you as the founder, where do you hope Kotera, like, what's your vision for Kotera? Say we roll into 2030.
Where do you hope you guys are at or where do you envision you guys being?
[00:36:24] Speaker A: Our biggest metric that we track is human time saved. How much time have we spent? Have we saved for our customers? And we are currently sitting at something like, I think, 80 million hours.
Basically, what we've done is we take every Kotera agent and we assign it sort of like, hey, how much time would it take a human to do this task? And then we basically just calculate all the runs, and we have saved about 80 million hours of human times in the next five years. I want to get that number to 5 billion. I want to say 5 billion human hours.
And that is where. That's where I want to be. I think that the core that this. I think the. The maximum ROI that we get from these AI agents is human time saved.
My goal for the next few years is to get us to 5 billion hours.
[00:37:16] Speaker B: That's that. I think that's the title of Your first book. 5 billion 5 billion hours.
How AI is going to revolutionize humanity and save you time.
[00:37:25] Speaker A: Well, there you go.
[00:37:26] Speaker B: Get that one for free.
[00:37:28] Speaker A: You and I have to have another conversation on how I actually write a book, because I don't know anything about.
[00:37:32] Speaker B: It, so that's funny. I know a guy. So IB thank you so much for taking time out of your day. I know you're a busy guy. What a fantastic journey, and I can't wait to see what you guys are to do with Kotera.
[00:37:43] Speaker A: Thank you so much for having me on, Travis.
