Revolutionizing Investment Research with AI

Speaker A: Foreign.

Speaker B: This is Product by Design, a podcast by Prodity where we explore technology, artificial intelligence, user experience, product management and the philosophy of building products and companies.

Speaker C: Welcome back to another episode of Product by Design. I am Kyle. And to welcome this week we have another awesome guest with us, uh, Andrew Einhorn. Andrew, welcome to the podcast.

Speaker A: Thanks Kyle, thanks for having me here. Appreciate it.

Speaker C: Yeah, very excited to have you here. And let me do a very brief introduction for you Andrew, and then you can tell us a little bit more about yourself. But Andrew is the chief executive officer and co founder of Level Fields, which is an AI driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Which obviously we're going to talk a lot more about as we get into this. But Andrew, again, very excited to have you on and to learn more about you and what you're working on. But why don't you tell us a little bit more about yourself.

Speaker A: Sure. Happy to. Had kind of an, a traditional career. Actually started my career as an epidemiologist. So I was looking at cancer clusters and contaminated communities and trying to figure out the source of the contamination early on or trying to figure out how mercury gets into tuna fish. That was one of my early projects. Love data. Have been working with data for, not to date myself, a better part of two decades now. And after kind of getting into the data science part of it on the epidemiology, I started working for a, uh, large management consulting firm that did some public health consulting projects as well as data analysis projects companies, uh, called ICF International. I was there through the IPO bunny stock unfortunately back then regretting that decision now as it's at 600% increase since then. But that's for a different time probably. And worked through lots of different consulting assignments. So had clients like faa, Office of Commercial Space Transportation where I was assigned to analyze the impact of orbital debris based upon future commercial space transport. So all the stuff we're seeing now, SpaceX was regulated through uh, these kind of environmental impact statements. And I was part of a team that wrote these 2,000 page documents that nobody but the government read but greenlit. A lot of the space programs we see today work some with uh, NASA to develop some of their environmental metrics. Program worked a little bit um, for some nuclear agencies as well. And then my final stint there was to build a software system for the Defense Department. It was a risk management system. And I kind of fell in love with the process of building software, realized that you can kind of mold it and shape it the way you want the world to work. Had no exposure prior to that and no formal training in it, but spent three years trying to put a software product across most of the defense department, you know, 13 different countries and five continents. Learned a lot. And once I had that kind of experience under my belt, I said, you know what, I love this. I think I have an idea for private sector company and that I started in 2009 and ultimately sold in 2019. And that software company looked at events for publicly traded companies, had clients that were like, you know, an Exxon or Discover csx, you know, some large companies out there. And our software system was designed to kind of monitor what was happening around that company. Major events that could be, um, either, you know, pro and con for their reputation. Our job was to find it, uh, get it as fast as possible to the corporate communications folks so they kind of spin up their talking points and make, uh, sure that they were doing the job that they were supposed to be doing by getting their core messages out. So we ran that company for 10 years, had a good time doing it, uh, all the way from literally a napkin sketch at a bar to selling to private equity, you know, multi billion dollar fund. Um, was pretty exhausted by the end of that process. I took a bit of a break in 2019 and regrouped with our technical team, who all did not join the company that acquired. And we set out to solve another problem. Um, we had this sort of background in understanding unstructured data and lots of metrics and events. And we tried to figure out, okay, what, what went right, what went wrong with the last company, what could we do better? And we started looking at, you know, how the kind of older approach of Boolean searches, sort of like Google's setup mostly was triggering lots of errors and information recovery and said, you know, let's build an AI system to address some of these issues, addressing like the linguistic nuances that, the way people speak, you know, so you're not just hunting and pecking for a keyword, but you're understanding tone and context and meaning and snarkiness and all the wonderful acronyms that we use on social media now. And as we're putting that together, that's when COVID 19 hit in February of 2020. So we were kind of, uh, a little bit of a solution in search of a problem at the time. And when we saw events, this major event, just changing the world and changing how stocks were performing, how companies were performing, it was like lightning bolt goes off in your head and Say, okay, that's the problem we're solving for. There should be an easy way to understand how to react to events when they occur. There isn't one. Uh, and so we've founded Level Fields AI, which is an event driven analytics system that finds and analyzes, uh, different types of events that are proven to move the share price of stock. Investors can use that to find short, mid and long term trading opportunities really rapidly. And it could be as simple as, you know, hey, Jeff Bezos left Amazon. Is that good or bad for the stock? Our, uh, data shows it's going to be bad for a month. Create a buying opportunity and then it'll be great for the next six months as it goes up. And if you see those analytics, then you can make better decisions accordingly. And so that's what Level Fields is all about.

Speaker C: Okay, there is a lot to talk about there and I'm excited to kind of dive into a whole bunch of the things that you, uh, that you brought up in your background. Um, but before we do, maybe tell us a little bit about what are some of the things that you like to do outside of the office. You talked a lot about your background, but what are some of the things that you like to do when you're not working any.

Speaker A: Soon as I can get on a boat I take, could be a kayak or it could be, ah, a giant yacht. It doesn't matter. Anywhere on the water, it's like the fish. Um, unfortunately we're not super close where we are to the water, so it's a little bit of a challenge to get there. Do a lot of hiking, a lot of activities with children and uh, yeah, I mean I think generally those are, those are the main things. You know, what everybody else would do. You go see movies and go out to dinner and whatnot. But the main ones, hobbies, I'd say fishing, hiking, anything outdoors.

Speaker C: Very nice. Okay. Um, so I want to kind of go into your background a little bit because you mentioned a kind of a non traditional background that led you to a number of different areas. So starting off in the health sector, you know, doing some research around a variety of different things that then led you to using data and then doing some other projects. What were some of the key moments when you realized, hey, either this is the part of the job that I really like and I want to focus in on this, or maybe some of the things like, hey, I don't think I want to focus my career necessarily on this thing and I want to kind of shift it and then how did you work through some of those changes as you kind of pivoted your career into some of the areas and to eventually founding a company that, uh, you were at for, for quite some time? Like what were some of those things? Both a thought process and then maybe some of the key moments where you kind of pivoted from one thing to the next.

Speaker A: Yeah, you know, it's easier to see looking back than it was at the time. Um, I would say just general personality of mine is I like to solve problems. I tend to look for solutions to problems and if one doesn't exist, I try to create it that it kind of drives me nuts. The solution doesn't exist out there and I'll, I'll make it. And so, you know, early on it was all right, well how do we figure out whether this community is going to be contaminated by this toxin that's in the ground? That's the problem. And I had to come up with a mathematical way of figuring that out. And I was using, you know, some of the off the shelf software back then, like SPSS and SASS were the biggest statistical analysis packages. And you're sort of forced to use their approach. Um, and you, there were some hacks for it. And so that, that was a little bit limiting. And at the same time, you know, working with this large. I was working for a large public health consulting company that was global and just on the side was very interested. What's now called esg. Back then it was called something else, um, type of investing where, you know, you're looking for not just an economic benefit but also a social benefit, uh, to the company as well. And it's always, it was always sort of an interest of mine to be in investing. I ended up, you know, paying largely for m all of my expenses in graduate school with stock trades, you know, and a copper company that I was buying and selling around the time and goes back even earlier to high school when it was buying stocks and selling them. So I've always been sort of interested just as kind of a sub thread of my life of investing. And so it's always been there. And as a career trajectory kind of went forward, you know, officially I was dealing with building software systems and creating solutions. But then I was always investing in stocks, trading in stocks on my own and kind of trying to build wealth that way. So eventually those two things merged. Took it, uh, took a long time before that would happen. But you know, when you sort of recognize that something that you were passionate about just on your own can become a career. Then all of a sudden, work doesn't really feel like work. It's just enjoyable. And it's kind of where I'm at now. It's like, you know, day to day we're looking at stocks. It's not necessarily just about, you know, the money. It's more about understanding how the whole economy is interconnected and how it all works from a global perspective, all the way down to an individual company. And when you look at the way an event, you know, macro event or micro event can affect a company, it's very much analyzing like a butterfly effect. And for me, that is the same as looking at how a disease spreads through a population, right? There's sort of this initial event, you know, where one person gets infected and then they infect two people and it spreads accordingly. And, uh, and with events, it's. It's very similar. You can have this small change in the weather in West Africa and all of a sudden your chocolate crops aren't growing, which is going to affect the price of cocoa, which affects all the chocolate manufacturers, which affects the price of chocolate that we're all paying for. And ultimately the, the earnings of companies like Hershey's, um, which I've been dropping. And, you know, if you are able to kind of put the dots together and see that story, it's really interesting because there's never, there's never a day where you're not learning. And so that's, I think probably what drives me more than anything is as long as I'm learning something new on a daily or at least weekly basis, I'm, um, more happy. And if I'm doing the same thing and not learning about it, I tend to start looking for something else to do where I'm challenged. So, you know, a little bit of a mindset, I guess, of an academic and had some time in academia for a bit when I was an adjunct professor at Georgetown and enjoyed it, but it was, it was a bit too theoretical for me. I like to get into the details and find the problem, but not just write about it, but also solve it. So, you know, anywhere I went, that was always the case. It was like, hey, this thing, this thing's broken. Or this doesn't work the way it is the way we want it to. How do we solve that? And I loved for a time being able to sit down with clients who are largely chief communications officers of public companies. Though, uh, at some points we were sitting down with the chief executive officers and asking, you know, what are the challenges that your organization faces? You know, where are the problems? You know, you hear enough of those types of problems, then you can create a solution if you're creative enough and kind of know just enough about the process of creating software to be dangerous. And, uh, you know, I'm, I'm not a coder at all. I can't do a line of code. But I am on the architect side of, okay, this is, this is the problem that we need to solve. Here's where the interface needs to bring the user and that's the part that I enjoy doing. Instead of working through that iterations and understanding really what is the best pathway to get to an answer and how to cut through a lot of the steps along the way to explanate it.

Speaker C: Right. Uh, you've mentioned a couple really interesting things and I want to kind of talk about Level Fields AI, which is the company that you have now, and maybe how you got there, because it sounds to me very much like you kind of drew the parallel of events happening in health and how that can affect populations and then your previous company, you know, watching events happen in the market and kind of highlighting those four companies in order to address. And now it sounds very much like this same conceptual framework, really taking that and monitoring it and putting that in the hands of people to use for investment purposes. So maybe walk us through a little bit more about what Level Fields AI is. What does it do? Am I right in kind of the thinking as far as the framework of is that what it is? And you can kind of build on that a little bit more for us?

Speaker A: Sure, yeah, happy to. So there's a few different ways to invest. Right? You can invest in stocks on the basis of the fundamentals that stock the Warren Buffett approach. I just look, just looking for stocks that are cheap, undervalued and have a competitive moat around them where they can just grow for years and years and kind of go unchallenged. Um, there is technical analysis which became big maybe 10 years ago or so. Uh, it's a lot of just kind of watching kind of patterns and graphs and volume changes and making adjustments short term to trades on the basis of momentum or when momentum turns against you. Um, and then there's event driven trading, which, you know, really until Level Fields wasn't something that was available to anybody outside of hedge funds. There was no platform for it, there was no way of doing it. The previous way was just called trading the news. And that was largely like you're looking at a headline that says, you know, there was a train derailment and there's a chemical spill and you realize that's probably not good news for the train company carrying that, uh, chemical stock's going to go down. Most people know that. But you just don't know how much is it going to go down and for how long is it going to go down. And so Level Fields aims to answer that, those two questions, right? What direction, uh, will the equity be affected, how long will it be affected for, and by how much will be affected for. For positive events and negative events. And you know, we often use the analogy like it's like a weather report. You know, you kind of look up in the sky. If you walk out and say, I think it's going to rain today, you know that that's the same as sort of your normal reaction to a headline, okay, this sounds bad, you know, looks like rain. What you don't know is it's going to rain at, uh, you know, 1 o', clock, 2 o', clock, 3 o', clock, super hard. Then it's going to lighten up, then the sun's going to come out at 6, right? That's what comes out of the weather report. So we aim to do that with stock price reactions to events and try to make it really easy. So you look at the application and you'll look at 25 different event types. Each one will have an impact on the share price, uh, that will be short, mid or long term. And you can see, you know, the percentage time that it goes up or the percent of time it goes down. We call it the win rate on the application. And then you can also see generally how, how far it goes up or how far it goes down on average. So you have more of kind of a decision criteria for what to do. Right. And I think most of us have been in that situation where like, you kind of know sort of what to do with a headline, but not necessarily the timing or how much money to allocate. You know, I'll go back to my, my Bezos leaving Amazon example, which I think most people can remember, and, and understand that that sounds like bad news for Amazon. You know, you have this very iconic founder who brought the company from uh, your bankruptcy to being, you know, one of the biggest companies on the planet and one of the most important. And when he leaves, there is a fear that, oh my God, is the growth story of Amazon over? Is that it? You know, can, can it keep going? And is this going to sell off? Should I sell my Amazon stock? The, the answer to that question is, well, depends what you want to do, like long term. According to the analytics that we have on Level fields, there's a 2% dip in the share price for an event like that in the first day, it continues to sell off for about a month and then at that point it will usually start to return to the mean, you know, the price prior to the announcement. So what do you do with it? If you are owning it and you're thinking about selling it, this might prevent you from selling it and regretting it. If you're owning it and wondering what to do, this might also be an opportunity to buy more shares when it's cheaper. Or if you're just looking to trade it, then you can follow the analytics, say, you know, I'm going to wait for a month to go by, I'm going to buy the stock, I'm going to wait another month, I'm going to sell it, I'm going to make 14% in a month, which would have been the trade for Amazon. And so you can do that with lots of different events. And so that is what we call a, um, CEO departure event. On Level Fields, just about weekly there's some CEO of some public company that sometimes it's amicably, sometimes it's a nasty battle, sometimes they're just thrown out because of poor performance. And so the share price changes depending on a little bit of the type of company, which is the other part of it. Not all companies are created equal. So if you have a poor performing company that gets rid of their CEO, often you'll see a share price go up because the investors are celebrating what might be a possible turnaround. If a new CEO comes in and does a better job and they sort of like, you know, okay, this person's gone. I think, uh, there's a chance to resurrect the company. And on the other end of it, you know, really great company or really founder, um, driven company like a Tesla or an Amazon, not going to be great. Get on the headlines and it's going to drive the share price down. And so our system allows you to within like one click and just click to say, hey, what about poor performing companies? Click a button and it will show you know what, for those types of companies, the share price goes up and how long it will go up. We try to make it really easy. You can also use it for research. So if you're just researching stock and trying to understand what happened over the last two years and you're looking at a chart, a lot of times you see the chart going up and down, up and down. You have no idea what's causing these big moves. So what we do is we sort of correlate the events to the share price movement and then we append the events right on the chart so you can see, oh, okay, yeah, I can see why it went up 15%. They just announced a dividend, uh, increase of 105%. That's huge. So now instead of getting a dollar per share, I get two and people like that. So it drove the share price up. Let me just make it easy because otherwise you have to go back and forth between news feeds and, and the chart and try to figure out what happened. And to do that across, you know, thousands of stocks that are out there is just, it's huge time suck. Nobody has that kind of time. And as a result, what we have is a big bias towards about, uh, 30 stocks, really even 10 that everybody hears about the Nvidias, the Teslas, the Amazons, Apples, Microsofts and so forth. Outside of the 30 of them, there's very little coverage, very little knowledge of the rest of the market. And that alone creates kind of unfair advantage to large asset managers, hedge funds or wealth management shops that have teams of people looking through financials of hundreds of companies to try to find good investments. When an average person doesn't have the time or, uh, resources to do that, they're on their own. They can't look through thousands of companies. They don't have hundreds of people working for them. So now what we've done is we've created this AI system that allows any individual to use the AI to parse through all this information automatically to find those juicy tidbits of events that are catalyzing share prices or serving as a signal that something major is happening. To look at how product launches, you know, might be going or have, uh, resonated in the past with an investor audience and try to make that really easy. So you can just go into the system and say, you know what? I'm interested in this type of event, this type of company, and I'm going to set the AI to go look for that over and over and over again. And it's tireless. It's, you know, it looks 24, 7 and only cost a couple hundred bucks a year. So, you know, for the time saving alone, we think it's hugely valuable. And obviously, if it's pulling really good opportunities for stocks you've never heard about in ways that make sense intuitively, you know, through common sense, like, hey, this, this company's giving away more money, therefore the share price is going up. Um, trying to make it really easy for, for individuals to participate and really grow their wealth.

Speaker C: It's really fascinating because like you said, investment managers, hedge funds, large companies do in fact have large teams or at least teams dedicated to doing this type of research for specific sectors. So like equity sector or other sectors in the market that like you and I don't have the ability to do that. Like I cannot research like large cap all the time, uh, and expect to be able to make a return on that given the money that I have to invest. Now if, if I have like $100 billion to invest because I'm a large asset manager, like it makes sense to do that. But just Kyle, with the funds that I have available, like investing 100% of my time into that, it just, it doesn't make as much sense unless you can employ tools kind of like you're talking about. So I think this is fascinating because it starts to like you're saying, level the playing field between those of us who aren't large asset managers and those who can employ large teams and lots of resources to do this type of research. Um, I'm really, really interested in some of the things that you have found to be, maybe some of the most interesting things that you've been learning. Like as you have been doing this, um, as far as, as people have used the product, um, as people have been going out and um, have been doing the, the research or employing some of the things like what is, what has been some of the feedback you've been getting from investors who have been using it and kind of coming back with additional feedback or things that you have seen maybe have surprised you or, or you have learned as you've have been watching people.

Speaker A: Yeah, there's a few things, uh, I would, I would note, I mean from our group of users and subscribers, it's a pretty diverse audience. We have options traders, we have ETF buyers and sellers. We have long term, short term, midterm investors, swing traders, data, uh, day traders. So they use the, the app in various different ways. Um, I'll give you some concrete examples, right? We had an options trader for example, and their whole theory was whenever a major event happens that drives the share price high on the first day, let's say above, you know, 10% on the move, typically, you know, 70% or more of the time on the second day, there's a sell off. And so what they did was they would actually buy puts to short the stock on the second day, rather than try to crowd into the day. One trade of buying the equity on the way up. And they were making like twelve hundred dollars a trade. So they're very happy with that. We have another member that their strategy is they want to make a half of a percent every day. And that's all they were looking for. So they looked at certain types of events that happened very often that would, you know, drive the share price up somewhere between, let's say, 1 to 8% on average. You know, typical average was like 3 or 4%. And they just wanted to get in and out. They just want a half percent every day. Um, so that, you know, half a percent became two and a half percent per week, you know, which became nine and a half percent per month, and then shortly thereafter and so forth. And, uh, that was their goal. And they were accomplishing their goal for a couple of years and still are to my knowledge. And then there's others like. And I put myself in this category that look at the events that are coming through the system. More like a story that's being told. A way to kind of see kind of through the. Through the veil, if you will. Because if you think about it like, we get only small hints of what's really going on within a company in between their earnings reports. We have no real idea whether the revenue is going up or going down or the profits going up. Are they struggling? So they leave these sort of breadcrumb trails, the leadership all over the place in the form of events that might be, they increase the dividend or they engage in stock buybacks. They issue a special dividend, uh, they fire somebody, they hire somebody. You know, those are sort of the breadcrumbs you try to piece together. And periodically large macroeconomic events will start influencing the outcome of these companies and their performance. And so example is, you know, I was just looking, I browse the platform daily and, you know, started to see at one point, like all these coal companies started issuing special dividends. These are when they just do one time, uh, almost like a cash rebate, you know, to being a shareholder. This was a company called Arch Resources. They, I can't remember what the share price was they were trading, uh, or they were issuing a stock special dividend that was $11 per share. So for every one share you own, they were going to give you $11 in cash, which was like 8% of, uh, the whole value of the company just in a special dividend. It's a lot of money that they were giving away. And there was a couple other coal Companies that were doing something similar and you start asking the question, like, what is going on with coal? I had no idea. I never invested in the space. Did some quick googling just to see, you know, if there was something weird going on and saw coal prices were up like 2 or 300% because when Russia invaded Ukraine, uh, they basically cut off the natural gas pipeline to Europe at the same time. So all the rest of the European countries that needed energy and needed heat and power, they switched to coal. So demand for coal skyrocketed. So now you have all these companies that are not changing anything about their operations suddenly being able to supply their product at uh, two, three times the price and they just take the extra cash and give it back to shareholders. Now I didn't know any of this was going on. It was just the signal that came through the app in the form of this large special dividend and know enough to say, hey, when that happens, the company's in a position where they're giving away cash. Something must be affecting it. Is this a long term trend or just like a one time thing? Ended up, you know, being able to, to buy into the stock paid like 30% on the stock price in a month, um, plus the special dividend. So it was like, you know, 40, 45% in about a month time. And those types of events, they happen all the time. We saw the same thing around the same time with fertilizer companies. You start, you know, seeing these patterns that are emerging where sectors all of a sudden begin to outperform or underperform based upon what might be happening in the broader global economic space. And you as an ah, individual person who's sitting there, uh, reading the news that you like to read about, might not be following the price of chocolate in West Africa, but you might be owning Hershey's stock and wondering why it keeps going down. It's really useful for putting those stories together. I think one of my favorite stories that I learned from the application, this small company called Veritiv, they do consumer packaging. So they make like the packaging that, you know, let's say your iPhone or some consumer product comes in, like all the plastic and the beautiful wrapping and the nice, you know, pictures on it. And so when you go back and you look at the company's profile and you look at the stock chart, you'll see, uh, during the COVID sell off in February 2020, they were actually buying back shares of their own stock. So they were taking cash, purchasing stock. Everybody else was trying to conserve cash. Right? You had Companies that were cutting dividends, that were cutting staff, no one was leaving their homes and nobody needed oil. The price of oil went to like negative zero. And at that point, you know, it was just, it was wild. So this is one of the only companies on the planet that I saw taking the cash that they had and not conserving it, but instead spending it buying back their own shares because they thought it was so cheap. Why on earth would they do that? Well, because if you can't get your product directly to a doorstep of somebody in the midst of a COVID pandemic, you're not selling it. Can't sell it through Target, can't sell it through Home Depot. No one was going into those stores at the time. So a lot of companies that had products had to go to Veritiv and say, hey, can you design me a package so I can get my product direct to the consumer? Because that's how everybody started to sell. You know, within the next two and a half years, that company, that stock price was up 1,000% because they just hit boom time. You know, it was like, wow, the whole world wants our product. And they just kept selling it and growing and buying back their shares and doing these, uh, you know, special dividends or creating dividend programs. And so just that all of that came from that one contrarian indicator, you know, looking at it at the right time to just say, this is, this is weird. So that, that's how I like to use the system often. Although, you know, on a daily basis we have these, these massive events that, that can completely change the trajectory of a company's history. Like a small company suddenly getting a billion dollar government contract and you see the share price go up 600% in a day because, you know, it was a $50 million a year company and now it's a billion dollar a year company. So those things happen. They're not as frequent as a lot of the other events I've mentioned, but they do happen all the time. So it's fun to watch and kind of just have almost like a front row view of everything going on in the, in the economy.

Speaker C: Yeah, that's fascinating. How much of Level Fields is, uh. Well, I guess a couple questions. How much of it is automated in a way that somebody could go in and uh, say I want to do, you know, I want to employ some of the strategies that you talked about and how much of it is guided in a way that is say, you know, here's some strategies that you could employ and kind of allows users to do the research, like, I guess, what are the different types of levels of those types of implementations? And, you know, what is it now? And then maybe what do you see that, um, changing to in the future? Mm.

Speaker A: So when you first. So there's, there's two subscription tiers. Level one is like 100, do it yourself. Level two, we have some analysts that are kind of assisting by picking out some of the best trades that they see on the platform. Uh, but if you're coming into the platform for the first time, you'll. You'll see it's organized by the type of event. So you'll have an array of different events. So one event type I've mentioned, which was like a CEO departure. So you would see CEO departures, every event that has occurred that involves a CEO departure with a public equity over the last X number of years. And you can see how each event played out. So you'll see the event happened here was the price move on day one, day two, day three, day four. You can go out weeks, months, days, and then averages. Here's how the average on day one, the average on day two, and here's kind of how it played out. So just, just by looking at that very quickly, you can see, oh, okay. It's sort of like trying to figure out if you're going on vacation. Hey, what's the, uh, the average temperature, you know, in Ontario, Canada, in July? What, do I need to pack a sweatshirt or not? It's the same concept. You sort of look at that and you're like, okay, I get it. On average, it moves 4%. But there's, um, some of these events that might be 12, some that might be three. And if you want, you can just turn that into a singular trading strategy around that type of event. And that's very, very clear and obvious on the platform. For those who want a little bit more guidance, you know, there's a help section that kind of talks about alternative ways to use the data. For instance, you know, companies that are increasing their dividends by, let's say, double digits. You have multiple ways to look at that. You can say, you know, it's going to move the share price over the next few days because it's a positive event. It's that breadcrumb trail that looks really good, you know, good. It sort of reflects management's confidence in the company because they're giving away money. So that's pretty easy. If they're giving away money, they must be pretty confident into the future. So it's a good Indicator, multiple events of the type are even a more powerful one. So you get two or three of these in a row over the course of a year and you're like, wait, what is going on? Why is Dick's raising the dividend again and again? I thought consumer spending was dead and brick and mortar retail was dead. And then you realize, like, there are these narratives that are playing out in the news media and all this opinion pieces that are out there and it's just garbage. It will negatively impact the share price short term. But if you look through that and you're actually looking at what the company is saying and doing, you can make a lot more money, um, by just ignoring a lot of that noise and saying, you know what, I don't care what they're saying on these Talk shows or TikTok shows, this company keeps giving away more and more cash. They wouldn't do that unless they were really confident or they just didn't care and wanted to crash the company. And so it's usually the former. And by just putting those two pieces of information together, you're going to have an investing strategy. You don't have to know anything else. And so each layer that you kind of can peel back depends upon how much knowledge you bring in or how much knowledge you're willing to acquire. You know, it's all there. In terms of the help text that we have, the Level 2 subscription is a little different because you get all the data that's on the platform, you have all the different scenarios at your fingertips. But you also have our analysts, which on a weekly basis will kind of take the best trade ideas or bits of information and send out one or two trade ideas a week. That sort of, that takes everything I'm saying into account and says, okay, let's look at the macro sector macroeconomic picture. Let's look at the events, let's look at the company financials and the fundamental analysis. Let's look at a little bit about the technical analysis. And then we'll make an assessment based upon all these signals and say, yeah, based upon all these events, it looks like GM is undervalued, right? And then all of a sudden you'll see eventually the market catches up with the data. And you know, you have this typical process of analysts make a recommendation and then they talk about the recommendation on tv and then some of the news outlets pick up, you know, the conversation on TV and eventually everybody hears about it. But if you want to get it early, you have to go straight to the horse's mouth. Straight to what the company is announcing. And that's where we focus a lot of our effort and our energy. So it doesn't have to be hard. It can be as easy or as difficult as the user wants it to be. Um, we have users that have zero trading experience whatsoever. You know, spend a day reading some of the stuff and jump right into it. We're doing really well. We have others that have been on Wall street for 29 years and used to have a Bloomberg terminal. Now they're using level fields and they're happy as could be because they can ignore a lot of the raw data that gets processed by the AI automatically.

Speaker C: Yeah, I'm interested in a couple of things I want to touch on with that. How much of this is enabled by a lot of the AI that you're using now because, uh, it feels like you're able to take now a lot of these things and process them in a way that we probably couldn't, um, just a few years ago, and then couple that with kind of, like you mentioned, some of the fundamental analysis that we're able to do and really bring a lot of that together. So you know, how much of that is kind of this confluence of fundamental analysis and AI that you're able to bring in lots of factors and sift through them. As far as factors and events and all of these things, how has technology enabled that? And then where do you see that going into the future as these things continue to advance?

Speaker A: So REI is um, solving for two problems. One is identifying whether or not a event is properly correlated to a movement of share price. And the second is, it's like a speed reader. So it's looking at about 30,000 documents a minute, reading it, analyzing it, extracting information, uh, and then flagging the things that are in accordance with what we're looking for on the platform. After that, when the event is sitting on the platform, we have the near real time data from the markets and the price movements. We have all the uh, historical financials on the company and there's a little bit of a interface UX at that point. Right. So the events are there and then you kind of can see, hey, this is how all CEO departures look, right? It's, it's a minus 2% on day one. And then if you want, just like you would go and book a flight and say, you know, I'll never fly Spirit Airlines and you could do the same thing and say, oh, I'll never buy an industrial company, or I only like technology and healthcare. You filter it out and say, you know, also like companies that are not very expensive or that I want companies that have a dividend. So you make a few of those extra choices and you see how does that affect the uh, event impact. And if you find what you're looking for and click a save button, create alert and just say, you know, every time a company that I'm interested in that have these particular attributes, you know, in the financials or in the valuation and this event hits, let me know and I'll decide whether I want to trade it or just watch it. So it's, it's super easy. I think that in the future, you know, if we were going to continually evolve the system, it will get smarter and smarter with maybe some of those preferences that can be made in advance of a person having to make those choices about the financials. And so we could just have instead of one CEO departure scenarios maybe like five, you know, where there's different subsets. So here's CEO departures of large cap tech, and here's CEO departures of, uh, underperforming biotech. Things of that nature make it even easier. So our users have called it like an easy button. They're like, can you just, just send this signal and, and give me a 1 to 10 rating of, you know, how screwed might I possibly get, uh, if I follow this or how much I'm gonna win if I follow this and if it's a 10 out of 10, I won't even think about it. I'll just, I'll just trade it. Uh, so I think that's the direction we'll, one of the directions we'll, we'll head down the road. Um, there's also some other cool features we're developing that allows people to have a little more control over the system and kind of build their own ideas and theories, but use our infrastructure to do that in a way that they haven't been able to do before. So I won't give it away now because we want to do a good release when it's good and ready, but it's exciting stuff. I mean, there's a lot you can do. We built our own AI, so we're not reliant on anyone else's system, so they can't a rug pull on us, but we are consistently evolving it because, you know, human language changes over time. Already seeing it change. I've been jokingly pointing it out to, to some friends that the word underscores never used to be in existence in writing, but because it's a chatgpt word. You see it everywhere. And you actually hear journalists, you know, reading from that, even during, like, the presidential debates, you know, you kind of laugh. You're like, wow, this was influenced by.

Speaker C: Yeah, yeah, no, I agree completely with that. And, and really, really fascinating. Um, I'm interested. What do you see as some of the differences between what you're doing and obviously somebody might hear it and, and think, you know, there are people doing like, newsletters or TikTok videos, kind of like you mentioned before, about similar things, like what kind of separates what you're doing and what you've built from some of those other types of things. I see them too. Like, you know, TikTok videos talking about different analysis or other types of newsletters. Like, where do you see some of the big differences between that and what you're working on?

Speaker A: There's a few. I mean, one is scale, right? So if you're analyzing a single equity and coming up with your theory on that, that's great, maybe you're right, maybe you're wrong. Don't know. But, you know, if you're running a TikTok video looking at one particular stock, and so we do that, but we do it for 6,000 stocks, you know, simultaneously, all the time. So the scale is kind of unmatched. Uh, the second part I would say is everything's data driven on the platform. There's no opinion. There's not a shred of it. It's just like, here's what an event happened. It's an irrefutable fact. Here's what happened to the price, irrefutable fact. Here's the average price, irrefutable fact. And you don't have to say, we think this is going to be 37 times bigger than Amazon in the future. All this crap that you see out there in the marketplace just trying to get you to pay 100 bucks, you know, to become a super subscriber to some of these newsletters. And our, uh, view, our kind of philosophy is no one can really predict what's going to happen in the future long term, particularly for companies. You can't. You're doing a best guess, you know, you can bet on Apple to do really well because they've done well in the past and they have a lot of money. But you don't know, you know, what's going to happen with these companies long term. Um, if you look back far enough, you know, the, the biggest components of The S&P 500 are either gone in out of existence or they're no longer, you know, in the top group of the s and p.520 years ago was GE was the biggest company, you know, and Exxon before that. And then they're not the biggest companies anymore. So things do change. But what we can look at is short term movements that are influenced by what currently is affecting stocks. And that could be, you know, macroeconomic cycle like inflation or deflation or pandemic or trade wars, policy decisions, conflicts. Uh, and we can look at how those companies are performing in the short term and make predictions in the short term because we know how those types of events have move the share prices in the past. So our, our general philosophy is like we don't try to do anything beyond a year time frame in the data because outside of that you're now dealing with pretty, you know, subjective projections of future cash flows that are not based on reality. Um, they're just based on projections.

Speaker C: Yeah, I, I think that's a really important caveat and a good way to look at it. You mentioned early on in the conversation that as you were founding Level Fields that you took a lot of the learning from the previous company, uh, that you had founded as well as some of the other things that you had done. I'm interested. What were some of the key things that you learned from founding your previous company that you took into Level Fields and what maybe would be some of the, I uh, guess both the key lessons and maybe some of the best advice that you would have given yourself going into uh, founding either this company or you know, founding a future company.

Speaker A: Well, it's a good question. I would say first and foremost it's listen to the customer. We, and we did that right and we also did it wrong with Level Fields, uh, just like we did in the last company, our last company, you know, we, we built the product, iterated it several times, pivoted, you know, as part of some of those iterations. And then ultimately once we found a real product market fit, what took it to the next level was just sitting down with a buyer and saying what do you want to see? You know, because internally we were all excited. We want to throw all these bells and whistles at the system and add this and add that because we thought it would be great. But then you ask the customer and they like asking for things that are so basic that they would take like a day or two to implement, but they would, they would absolutely delight the customer. And you realize like, you don't have to over develop your product if you're really focused on you Know those conversations because, you know, we, we did that. You know, we load things up and then like, what do you think? They're like, well, that's great, but I, I'm not going to use that. What I really want is like a spell check. And you're like, really? That's it. And, you know, something like super simple. Um, and it's always. It was always like that. And like, okay, so small improvements can go a long way. And the more that, like, a customer was able to make a request and then see their request quickly implemented, it did two things. It made them far more likely to recommend you to others. So we got a lot of referrals that way. And it also allowed them to give you more ideas because the human nature is m. You'll give somebody an idea, but if they don't listen to you, you're probably going to stop giving them ideas at some point. And it's. There's no, you know, that could be. It could be friends, spouse, family, like, we all follow the same approach at some point. You just stop raising your hand in the classroom if the teacher's not listening. So same approach with customers. You know, the more you listen and can prove that you're listening and implement, the easier it is to elicit additional ideas and then road test those with other customers. Second part of that, I would say is, you know, it's very difficult for any customer to, from a free recall basis, tell you exactly what they want. They usually have to see something and say, uh, now, now that I'm looking at it, if there was a button here that did this thing that I want you to do, that'd be great. But if you just sort of put them in front of a blank screen and said, now build your ideal product, almost everybody's gonna fail. And even if you ask, like, what can I improve? Very little of the, you know, small fraction of the time you're gonna get useful feedback. Better to just kind of show something very concrete and be like, what do you think of this? You know, could you use it? Or let's go to this page in the application. What would you improve on this page? And you get, you know, much better pieces of information from that perspective that allows you to really build out the product without overdoing it on the feature side, without, you know, spending too much in advance on things that you didn't really need. So, uh, that's. That's a big piece, I would say. And we use that, you know, through our beta test. You know, we first ran level fields through A beta test. We didn't have a search function on the application because we wanted people to just do event based analysis. Search function is usually used to look up a uh, stock ticker, like define a profile of a company. But that wasn't how uh, we want to be able to use the app. So we didn't put it there. And we watched and interviewed and found like that created immense confusion because the first thing people wanted to do was look for the three stocks that they knew. They wanted to go look up Nvidia, Tesla, Apple, and see what our application would say about them. Um, and we were like, no, that's not how you use level fields. Like you use level fields by finding an event and then reacting to it. And there was like this cognitive disconnect with the application because people were so set in their ways and the way they wanted to interact with it. And ultimately like we just, we had to comply, like we had to change it. We're like, okay, we can't change their behavior so we have to give them what they're looking for. And then through that process, watch. And, and they would learn how to use the app based upon a behavior that they were already doing elsewhere. Right. So they would come into the app, uh, they would search for Apple, they would see the Apple page, they would see events that took place with Apple and then they'd see how, how Apple price reacted. And then they would started to learn, oh, okay, I get it, there's an association between an event and the price move. And then it would start to slowly expand out of their comfort zone. Um, and that took a while for us to figure out because we're like, you know, the numbers, engagement numbers were low early on in the beta test and we're trying to figure it out. And so we did these interviews and they're like, I just don't know where to go, like what am I supposed to do? How do I use this thing? And we thought, you know, as designers, like this is so obvious, like it's how do you use a weather report? Right. But it's not always as obvious as you think. And so uh, the testing, the constant touch points with your users or your customers, really important paying attention to that. And then, you know, the third part is like, how do you accommodate a lot of different types of users? You know, the hard part early on is you can't really decide on a target user unless you know how that user is going to be interacting with your software. And so, uh, as I mentioned, we had like day traders, options traders, Long term investors, you know, midterm swing traders, they're all using it a different way. And when you ask like what features do you want? You'd get often very different requests. I want options contracts and somebody else wants to know, you know, not just the uh, close to close price, but they want to know like the price from the open to the day high and be able to switch back and forth. And you're like, well you can't build all the features, create a list and we start to look at like what are the features that are most requested. And then we only focus on like the top 10% of the features that keep getting requested. If you focus there, eventually you'll find, you know, you're, you're making enough people happy and then you're starting to realize like, okay, so now the story is evolving of like what our user looks like. You know, they're this age, they trade it this way, you know, they came in from these particular websites or whatever. Now you can kind of double down at that point on okay, let's, let's go hard at this particular audience and, and market to them. So that's kind of on um, the, the ux, like product development side, right? Just from a business or an application side of how the data science kind of worked and how we arrived here. In our previous company we did a lot of analysis. Four publicly traded companies who were the client and they would, they would pay us to do that kind of analysis, but we would just see patterns over and over again. Because we'd have these clients, some of them were eight, nine years, we would have them as clients. And as you're there and you're watching on a daily basis how their share prices are reacting to these events, you're learning, you're picking up patterns. And what we ultimately did was we just said there are patterns here. We can't really take advantage of it through this model. Maybe one day we could trade on it and kind of just put that in the piggy bank for a while. Figured we would let it marinate and maybe one day take it out. And sure enough, you know, later we look back, we say, yeah, you know, if we did this similar kind of model, little different technology, but the end user is an investor, not a company. I actually think it would be successful and we could learn from a lot of the lessons that we had. Parsing through unstructured data, analyzing large quantities of data, onboarding users to software technology, learning from the user what they want. Only instead of being able to market to a few Thousand companies, you can market it to a few billion people. So we're still learning, we're not done still fairly early on in our journey. We obviously want to keep innovating and we want people to know about the platform and trying to encourage users to always supply feedback in the moment something they touch doesn't work or not working as expected, let us know so we can keep putting that feedback in. But um, you know, those off the top of my head, those are a few tidbits I think we've picked up over the, over the years and those

Speaker C: are great, great tidbits. I couldn't agree more with some of the things that you said as far as, you know, sticking close to users, understanding the feedback and being able to incorporate it and just really listening. Like you said, I uh, think those are great, great points for not just, obviously not just for your product, but for anybody who is developing a product or a company. So I think those are absolutely superb. Well Andrew, this has been I think a really, really great conversation. Uh, not just for like product development and ux, but on the financial side as well. I think some really fascinating things that are going on with the technology, the ux, the things that you're working on. Where can people go to find out more about uh, level fields, about you, about anything that you're working on?

Speaker A: Yeah, just go to Levelfields AI. Um, we're, we have some videos on YouTube, you know, there's a few social platforms but largely you're going to find good information on level fields AI. And if you're jogging or riding a bike while, or working out while listening to this, please do you know, send yourself a text. Level fields like leveling the playing fields AI and we have a discount code if you're looking to subscribe, just type in the Word podcast and the number 23. They'll get you a discount on the product. If uh, you're listening to this and you know, hopefully we'll, we'll see more people using the application. I think that even if this doesn't sound like it's for, you know, for you as a listener, I'm sure you know somebody that could benefit from, from knowing about it. So we'd appreciate you sharing that information.

Speaker B: Okay.

Speaker C: And we'll put that in the show notes as well because I completely agree. I, I think this is fascinating. Uh, so I, I think that this is, has been a great discussion and appreciate it. Andrew. Well, we like to wrap up with a couple of final questions and these don't have to be technology or Finance or product related. But have you watched or read or listened to anything recently that you would

Speaker A: like to share, watched or read or listened to? Let's see, what did I watch recently? Um, I haven't had a, a ton of time. I mean, I was, um, reading, you know, some old investing books like the Intelligent Investor recently about how to value companies and, and revisiting some of that, which, you know, if you're interested in, uh, investing in stocks, I would, I would recommend. That's kind of the definitive starting point for fundamental analysis. But nothing terribly fun, unfortunately. It's been more on the academic side for me.

Speaker C: Yeah, some classic text for investing. Um, all right. Are there any products that you have been using and enjoying recently? And they can be digital technology products or they could be physical products. Anything that you've been using, um, that you'd like to share?

Speaker A: You know, I have been using a little bit of a video creation tool that's been neat called Pictory. Just makes kind of creating videos easier for uh, for those without the video editing skills like myself. So I've enjoyed that. It's another AI tool. It's not perfect, but it's uh, it's far better than anything I've used.

Speaker C: That is, that's a really great one. And I'm pulling up, I'll have to pulling, uh, up Pictory AI because that is actually had the CEO of Pictory AI on the show a little while ago.

Speaker A: That's too funny.

Speaker C: We'll give a shout out to Pictory as well because great, great tool. And I'll link the episode in the show notes as well.

Speaker A: Yeah, no, it's great. It's a fun tool. I mean it made it a lot easier to do some of the things, uh, that we're trying to do. Yeah, I'm assuming they're doing very well, but I have no idea.

Speaker C: Yeah, uh, yeah, very, very cool product. And I agree with you. Like being able to take a lot of the AI creation for content and other things and being able to make it a lot easier. So cool. Very, very cool. Shout out. Uh, so awesome. Well, Andrew, again, appreciate all of the insight, everything that you've been able to share. The uh, business creation, the product side, the finance. Excited to see where a lot of this goes in the future. So appreciate everything that you've shared.

Speaker A: Thank you. Thanks for having me. Appreciate it. It was a nice conversation.

Speaker C: Uh, it was. And thank you everyone for listening.

Speaker B: Thanks again for listening. If you like the show, be sure to follow or subscribe on your favorite podcast app. You can follow the show on TikTok at Prodity Co and on Twitter @ProdityCo. You can also follow me on both of those platforms. Kyolaryevans if you want more product conversation, check out my newsletter proddity@prodity.com you can also follow me on Medium at Kylerievans or check out my Medium publication Proddity. Of course you can check out all these links in the show Notes.