Knowd Revenue, Valuation & Funding (2025)

[00:00] Guys, knowdot ai got going, call it, six months ago researching. They just closed a $500,000 convertible note with a 10,000,000 cap. So call it, they sold 5% of the business. Pretty healthy. The question now is, can they get MBA students as their initial ICP target? Can they get these students using the tool? And then after that, can they start turning on a revenue stream here? We will see what happens. Justin's got experience, though. One two time [00:21] exited first time exited founder, second time exited founder, Bootstrap VC, notice his third project. We'll see where it goes. Hey, folks. My guest today is Justin Shum. He's building Node dot ai, which extends human reasoning and decision making abilities. He's building on his own experience. He's a three time founder, one bootstrap, one exit, and one venture backed. Justin, you ready to take us to the top? [00:42] >> Absolutely. Let's do it. [00:44] Alright. What was the name of the bootstrapped one? [00:46] >> Yeah. Ready Chat. Founded back in 2013, exited it in 2015. [00:51] Ready Chat. Okay. And what was the you you bootstrapped that. Right? [00:54] >> Yep. You got it. [00:56] So, like, no no angels. You just put in your own money, basically? [00:59] >> Yes. That and a lot of blood, sweat, and tears. Yeah. [01:02] And how many years was that again, you said? [01:04] >> We operated for three years and then we had an acquisition. [01:08] Okay. And did you I mean, did you guys break a million bucks of ARR at that point or what were you when you exited? [01:12] >> We were pretty close in terms of Canadian dollars. If you converted that to USD, yeah, it was it broke a million and because it was a hybrid between, you know, software and services, we didn't get a huge multiplier. It was about three x revenue for the acquisition. [01:29] Okay. This would have been back [01:31] >> Bootstrapped entrepreneur, that was a lot of money for us at the time. [01:34] Yeah. I mean, what? That's a $3,000,000 deal back in 2017. Right? You guys own how many cofounders? [01:40] >> There was three. [01:41] I mean, so if you each can pretax take home something like 300 k, that's a good gig back then. [01:46] >> Yeah. You know what? And in Canada, you get you're tax free up to 750,000. So it was basically all of it in our pockets, which was nice. [01:54] That's okay. So I changed my math. You each get $350,000 pre pre pretax and get to chemo. So that's something like that. Yep. That's great. Looking back, do you go, man, I wish I kept building that thing. There was a lot of potential there. Did you sell too early or no? [02:07] >> Yeah. I think every founder has seller's remorse for sure. But, you know, that was a it was somewhat of a platform that launched us into other businesses and, you know, all of us are, you know, successful in in different areas now. So I don't think I have too much regrets. [02:23] Yeah. I I rarely meet founders that regret their first sale because it creates so much optionality for you not to mention the cash in your pocket. So alright. You get that done in 2017. And then what was the next company? I guess this was the VC backed one. [02:35] >> This was actually not. We the second company was Ask Avenue, again in the prop tech space. [02:40] Ask what? [02:41] >> Ask Avenue. [02:42] Avenue. Okay. [02:44] >> And, you know, we had some of the largest enterprise customers at the time, REMAX, you know, Royal Page, another massive brokerage with 20,000 agents. And we got a lot of press, won a pitch competition to kick it off. But that one was angel backed actually. And it was a completely different experience from, you know, bootstrapping where you own I own 51% of Ready Chat, Ask Avenue slightly less, but it's super important to note this that taking [03:10] >> on angels, they have to be the right angels. The angels we took on were not experienced investing in tech and so they had different agendas, different strategies they were trying to enforce and so it got pretty messy, pretty ugly and I actually exited that one willingly and didn't make too much money off of it. I just wanted to move on to new things. [03:30] Why do you say ugly? [03:32] >> Yeah, just different objectives. They were opposed to the markets we were entering, the go to market strategy. And there was just a lot of debates, a lot of bats, which led to backstabbing eventually. So it became somewhat of a toxic environment. [03:49] Backstabbing? [03:50] >> Yeah, yeah, yeah. So for example, you know, I was the CEO at the time and I really wanted to continue to go after enterprise within real estate, continue to go up market and just double down on that segment but they wanted to expand into other verticals. And so eventually, you know, it came down to, you know, voting and and I was outvoted and and I was actually removed from the company, but I was willing to leave. [04:17] Interesting. What was the board? How many folks? [04:20] >> There was, I believe, six people at the time. [04:22] Wow. That's a lot of people on a board for a company at Angel Stage. [04:26] >> Yeah. Yeah. So you can imagine how complicated that was. [04:29] Well, yeah. And you and also you rarely I mean, you don't wanna have an even number on your board because then you have to deal with ties. [04:35] >> Exactly. [04:36] All right. What was the third company? [04:38] >> Yep. Third company is note, which I'm currently working on. This one's venture backed. So I've gone through various structures and I can say that this one is a lot easier when you're venture backed, you have just access to stronger advisors, more resources, and you really can I wouldn't say take your time, but you can really methodically plan out your next steps? You're not trying to put out a bunch of fires, trying to make payroll or anything [05:04] >> like that. You have more time and focus, I would say. [05:09] Oh, what's going on there, YouTube? Good to see you guys. Now imagine this. You love watching these interviews with SaaS founders, but imagine if we took all of the valuation data out from over 2,807 interviews I've done manually. Saves you a lot of time. Well, we've done this. We've built it into the beautiful interface inside of Founderpath. Check this out. I'll show you how you can access this in a second, but you log in, you connect [05:32] your Stripe account, you see your valuation real time, you can see what it changed over the past eighty eight days and even set goals for valuation this year. Now the secret evaluation is there's many different ways to value a SaaS business. So the reason you're gonna see three or four different valuations inside of your Founderpath dashboard, this is all free by the way, is because depending on who's doing the buying of your SaaS company, you're gonna [05:56] get a different valuation. A VC is gonna pay a different valuation, private equity firm is different. If you're gonna do a minority sale, that's different. And if you sell the whole business, that's a different valuation. You can see all those when I hover over here. Right? So the teal is what a VC would pay. Yellow is what private equity and red is if you sold the whole thing outright. Now what's cool about this is this is [06:18] not built off random data. Again, you guys hear these interviews on YouTube. All these datas are built from real time valuation data points founders share with us on the show. So traction, 1,200,000 seed round, 3.7 raise. They sold 22% of their business. Go in here and filter by the event. Maybe you only wanna see companies that have sold the whole business. Well, here are a bunch that have been acquired, the valuation and the multiple. Maybe you're [06:44] going out right now and you're raising your seed round. We'll go in here and look at all this recent seed deals that went down, what they raised, what valuation they raised at, and what percent that they sold. There's never been a larger dataset of SaaS valuations than what you can get now inside of Founderpath. And we're thrilled to bring it to you. Alright. We're gonna go back to the YouTube video here in a second, but if [07:06] you wanna check this tool out, if you wanna jump in and sign up, you can check it out for free to get your valuation at this link. This link, founderpath.com/products/valuations. Or if you go to founderpath.com and hover over products, click on get your valuation here, and go ahead and sign up to give it a whirl. Again, all that valuation data live right inside the platform. I hope to see you there. Alright. Let's jump back into the [07:32] interview. So how much did you raise in what year? [07:36] >> Yeah, so we just closed it actually last month. So it's pre seed 500,000 USD from Drive Capital, amazing partners. We're contemplating taking on a little bit more for our pre seed with I don't want to name them yet because it's not done, but their resources in AI and we're building an AI space would be invaluable to us. We're considering opening up a little bit more and then maybe we'll raise either a small A or a larger [08:04] >> seed in about six to eight months time. [08:07] What cap did you negotiate? [08:09] >> So it's convertible notes. [08:13] >> Sorry. What was your question? [08:14] What cap did you negotiate or was it an uncapped convertible note? [08:18] >> It's capped. Yes. I don't want to get into too many details, but I will share that we did raise like we're probably in the top 10% in terms of valuations and terms. We came out of a program called Entrepreneur First. Not sure if you're familiar with them. That's where I actually met my co founders this time around. And their terms were like 10 for like 200,000, not the greatest. And so we actually [08:46] >> didn't go through with that term sheet. We actually decided to hit the market, the open market and we secured 500 at 5% instead of the 10%. So much more favorable. [08:59] So that I mean, so that would be a 10,000,000 cap? [09:02] >> Yep. 10 yes. 10,000,000 valuation. Yes. [09:07] Well, there is no valuation on a say I mean, there's a cap of 10,000,000. Right? So when you use the word valuation, what do you mean? [09:13] >> So it's 5 percent at a 10,000,000 value valuation. And then the conversion point is where the cap comes into play. So it is uncapped rather. [09:24] I see. Yep. [09:26] Got it. Point being, you sold 5% of the business once it converts that for 500 k. [09:32] >> You got it. [09:32] Got it. That's a high valuation. That can be a bad thing because you have to grow into it. It also reset the pricing of your options, makes it harder to recruit or defend a lower $4.00 9 valuation. Why'd you optimize for valuation? [09:45] >> Yeah. You know what? We that wasn't our plan. It was more so how we looked at it was we can get more money for the equity, but we're also building the AI space. So we're going after a pretty large market, So we feel that the valuation wasn't too steep for us. [10:00] All right. Let's talk actually about the product. So who do you plan to sell this to? How will they use it? [10:05] >> Yeah. Our initial ICP will be actually MBA students. Why MBA students? Well, [10:12] >> they're not tight on privacy, and, you know, we need your data. [10:16] Hate this idea. Aren't all MBA students broke? [10:19] >> They are. But here's here's the thing. [10:22] Why would you pick them in your initial segment if they're broke? [10:25] >> I wouldn't say they're broke. I would say probably 50% of them are actually working in the field doing part time MBA. The ones that are like right out of undergrad entering their MBA, yeah, they're broke. But here's the thing, they're open to trying new technologies and we're really looking for the feedback as opposed to monetizing them. We want them to help us define our roadmap if we [10:46] >> see But why? Why would you build a roadmap around a customer that's broke? [10:48] Don't you wanna build a roadmap around a customer that's rich? [10:51] >> Well, here's the thing. They grow up into enterprise organizations and they're gonna if they adopt us according to our plan, they'll take us into their organizations as well. So that's the strategy. On top of that, they mimic our core customer that we plan to monetize with, which are management consultants. They do the same thing in terms of analysis and and they deal with a lot of unstructured data and and documents and things like that. [11:15] I see. When did you what month did you write the first line of code for the platform? [11:19] >> We actually just started writing last week. So we've been building my our co founders my co founders and I, we've been building across three different continents for the past six months. And before we wrote any line of code, we went through deep discovery. We through a dozen different rabbit holes to arrive to this final conclusion where we invalidated our hypothesis before we started writing and building. My previous startups where we just built and started selling, like [11:45] >> I sold packages in my previous startups before we even had a product or service. So this one was a lot more methodical in our approach. [11:52] I mean, how do you know? Mean, no one's ever come to me and say, I tested my hypothesis and failed. I'm not gonna start a startup anymore. Right? Everyone figures out a way to make that a good story. So, I mean, what were you looking for? And what were you testing specifically? [12:05] >> Yeah. We were trying to unlock value. Like, while building an AI, [12:12] >> it's scary because literally every release from OpenAI kills hundreds of startups, every single feature. And so we didn't want to build something for the short term. And so we really had to understand what value we could offer long term while while thwarting off threats from companies like OpenAI. [12:31] So how you know what OpenAI is gonna release six months from now? What if they release this product? You have no way to know that. [12:36] >> Well, you can kind of understand where LLMs are gonna go. Firstly, it's text based, like simple stuff and then now they're getting to multimodal. So video, audio and then you can kind of like trace what type of different things you're going to be able to do with LLMs for those different types of formats. But for us, we looked at the big picture and we realized, yeah, you're right. We don't know what they're going to release. It's [13:03] >> going to disrupt a lot of startups, but everything's going be chat based and conversational based. And so what we're actually building is a platform that's really easy for anybody to get into to start using and finding value with LLMs without having to prompt. So it comes down to design principles and we see a promptless future. You shouldn't have to converse with your machines in order for them to extract value from them. And I'm taking these learnings [13:29] >> from the chatbot era, right? I built in the chatbot era. As you know, a huge went hype cycle and then they fell off because they just sucked. Yes, chatbots are a lot more intelligent now, but you probably have experienced the ChatGPT, you understand that prompting the slightest changes, variations will result in a completely different outcome and output. And we're trying to eliminate that variance by again, creating a product that doesn't require prompts. [13:58] I mean, listening is gonna go, well, is your system gonna know what I want automatically without me prompting it? [14:05] >> Yeah, absolutely. So it really comes down to the metadata that's available to us in the context of the actual data that you're dropping into our system. So I mentioned earlier that we're dealing with unstructured data first. This is in the form of PDFs, video interviews, similar like this. You'd be able to take this recording, drop it in, we would transcribe it, we would understand the context of those conversations, and then we could have auto suggested next [14:30] >> steps. Very similar to like a chatbot experience now, this I hate saying this and comparing us to this, but when you have when you're chatting with a dumb chatbot, it has like the decision trees, the little buttons that you can select for a different outcome. So we're actually providing we're still leveraging prompts but we're offering auto suggested prompts for a user to simply select next output and so they go through this decision tree. And then we're [14:53] >> capturing that in a visual way so that you can arrive to a conclusion or a desired output and then backtrack to see, you know, what variants you can or changes you can make for variants on that output. Unlike, know, a conversation interface, all of your prompts get lost. [15:07] How do you know what variants though that the user wants to drive? [15:11] >> Yeah. So for example, if you were to drop in a PDF, let's say a Tesla earnings report, maybe past two years, and you wanted to run some analysis and compare those those two years in this certain segment, product segment, you could drop them in and we could But that's a prompt. [15:25] I would have to prompt I mean, I'm prompting you what you just defined as a prompt. [15:29] >> Well, you're not though because you're dropping both in and we know automatically there's two different folders. We can identify our files. We can identify the relationships and then have auto suggested products because we know you probably wanna do some type of comparison. And and so that's the design those are the design choices we're making. [15:47] How do you know that just from me dropping in two different PDFs that I'm wanting to automatically compare them? What if I'm wanting to concatenate them and put them together? [15:56] >> Yeah. Absolutely. So there are options, various options which open up different drawers to simplify that process. But at the end [16:04] of Isn't that a prompt? [16:06] >> It's an auto suggested prompt that you're selecting. You're not there's no cognitive load for you to think, how should I phrase this for desired output? So we're eliminating [16:15] >> that Isn't this the whole, like, son asks mom go to supermarket? [16:18] The son asks mom for strawberry jam. The supermarket mom shows up. There's 20 different strawberry jams. She ends up buying no jam because she doesn't there's too many options. I mean, isn't that what you're doing here? [16:29] >> Sure. Yeah. But what's great about us and this is what's somewhat proprietary, is the user interaction data that we're gonna get from these MBA students who are going to tell us what is the most accurate response or outcome or pass. [16:42] But the mom didn't buy any jelly. The supermarket learned nothing. She walked out hands empty. There were too many choices. [16:49] >> Maybe maybe in that example, but we think we think it could be different and and and more efficient in the way [16:56] >> we Why? [16:57] Well, how do you incentivize an MMA to spend their time to teach your program how to work and then you're gonna turn around later and charge them for it? [17:02] >> You know what it's about? It's about understanding your ICP. We're not trying to do this for every vertical and so we understand that MBA students as well as consultants leverage a dozen different frameworks. That's the outcome they want. They want to be able to do an analysis and apply framework. And so if we understand that desired outcome, then we can optimize a prompt. It is a prompt, but they're not having to prompt it. [17:25] You're right. Because it's the set number of prompt. Like, you know when MBA, it want to compare the Tesla earnings report a to b. It that the the reason that they don't have to prompt is because it's a defined because of who the ICP is, it's defined prompt. [17:39] >> You got it. Yeah. We are still leveraging prompts, but as the user, you don't need to prompt yourself. You don't have to deal with that. And it reduces the cognitive load. [17:48] How are you gonna make money on this? [17:50] >> Yeah. Great question. So we are actually taking we we we see this as, you know, we're trying to actually disrupt platforms like Wikipedia. We think, you know, the single simple web page encyclopedia hasn't evolved. And so what if we could give creators, anybody who wants to discuss and analyze and create a community around a topic, the ability to create intelligent workspaces where they can run analysis analyses and then users, their followers could fork their analysis, create [18:29] >> their own, add to it, add more data sources to it, and then build communities around these types of, you know, topics. So again, going back to the Tesla earnings report, huge, huge community around, you know, retail investors around Tesla earnings. What if you had created a note board and had this analysis and had a community contributing to it as well? So this is living, breathing, evolving type of workspace and that's our vision and go to market [18:55] >> strategy. [18:56] Alright. We'll see if it works. In the meantime, let's wrap up with the famous five. Number one, your favorite book. [19:00] >> Favorite book? Oh, man. I think it comes down to what I'm reading. Actually, favorite book is a sales book. How to pitch anything from Orn Clef. Any any fledgling entrepreneur looking to get started in in in business, they need to learn how to sell. That book is great in terms of framing conversations. [19:17] Number two, is there a CEO you're following or studying? [19:21] >> There are so many. So many. [19:26] >> I think Sam Altman is, like, amazing to follow. Very unassuming, extremely intelligent, really interesting to to follow him right now. [19:35] Number three, what's your favorite online tool for building node? [19:39] >> Favorite online tool? I would say, because I'm not non technical, I would say Miro has been extremely helpful for us. [19:49] Number four, how many hours of sleep do you get every night? [19:52] >> Oh, that's a loaded question. You know, I used to be about four or five hours of sleep. Now I've prioritized it as my number one thing, objective of the day, even before work. So now I'm getting about seven hours. [20:04] That's great. And situation, married, single, kids? [20:07] >> I am single. I do have a son who's eight years old. [20:10] Oh, very cool. Okay. And how old are you? [20:13] >> I'm 38. [20:14] >> 38. [20:15] Last question. Something you wish you knew when you were 20. [20:17] >> I wanna change a thing. So ignorance is bliss. I would like to know the same as I knew back then and follow the same path. [20:25] Guys, knowdotai got going, call it, six months ago researching. They just closed a $500,000 convertible note with a 10,000,000 cap. So call it, they sold 5% of the business. Pretty healthy. The question now is, can they get MBA students as their initial ICP target? Can they get these students using the tool? And then after that, can they start turning on a revenue stream here? We will see what happens. Justin's got experience, though. One two time exited. [20:47] First time exited founder, second time exited founder, bootstrapped VC, notice his third project. We'll see where it goes. Justin, thanks for taking us to top. [20:54] >> Thank you so much, Nathan. Have a wonderful day, everyone. [20:57] One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers, they try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU, CAC, LTV, you name it, they share it and the buyers try and make a deal live. It is fun to watch every Thursday 1PM [21:22] Central. Additionally, remember these recorded founder interviews go live. We release them here on YouTube every day at 2PM Central. To make sure you don't miss any of that, make sure you click the subscribe button below here on YouTube, the big red button and then click the little bell notification to make sure you get notifications when we do go live. I wouldn't want you to miss breaking news in the world, whether it's an acquisition, a big fundraise, [21:45] a big sale, a big profitability statement or something else. I don't want you to miss it. Additionally, if you wanna take this conversation deeper and further, we have by far the largest private Slack community for B2B SaaS founders. You want to get in there. We've probably talked about your tool if you're running a company or your firm if you're investing. You can go in there and quickly search and see what people are saying. Sign up for [22:07] that at nathanlatka.com slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode and if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, but I do it so that we can all learn. We have to counter those people. We [22:26] got to push them away. Click the thumbs up below to counter them and know that I appreciate your guys'support. Alright, I'll be in the comments. See you.