K Looks Revenue (2023)

[00:00] Guys, klooks.com.br was launched back in twenty twenty one, twenty twenty two. He broke $500,000 of revenue in 2023, cleaning Brazilian financial data and selling it to big firms like Captivate IQ and Bloomberg, which many of you guys might actually be listening, might actually pay for directly. Some of that data is provided by these groups like Calix. He has specific OCR technology with his seven engineers, but then it's the human layer on top of that where they [00:23] clean the data, sort it, filter it, make sure and check for accuracy before they sell it off to the end market. That really is their secret sauce. 50% of his revenue today, and they'll do about a million dollars this year, 50% of that is data as a service. Another 30% is pure services, and then 20% is true software as a service. Completely bootstrapped, selling through two major resellers in Brazil. That's NeoWay and TTR Data, where he [00:46] takes a cut of the sales there. If he goes direct, he's selling for 300 to $400 per month and has over 50 customers to date. Hey, folks. If we haven't met yet, my name is Nathan Latka. I launched and sold my first software company back in 2015 and went on to write a book about it, which you guys made a Wall Street Journal bestseller purchasing over 30,000 copies. Thank you so much for that. After the book, [01:08] I launched this show and went on to create founderpath.com. I raised a large fund to do non dilutive deals with B2B software founders. So far, we've invested in over 400 software founders totaling 150,000,000. Here in 2024, we're doing three to four new deals per week. So if you're looking for capital and don't wanna give up equity, go sign up at founderpath.com for free to get your offer. Alright. Let's jump into the interview. Hey, folks. My guest [01:37] today is Alex Abu Jamra. He is a former m and a adviser and manufacturing CFO. Today, he's a CEO and founder of KLooks. He graduated in business administration, summa cum laude, and, again, is now building this tool which helps structure unstructured financial data and sell it in a SaaS, DaaS model. That's software as a service, data as a service model. Alex, ready to take us to the top? [02:00] >> Hello, guys. Great pleasure to talk to you again. [02:03] Yeah. We appreciate it. We had you back on back in January 2023, about a year ago. At that point, your revenue was about 60% data as a service, 20% software as a service, and then 20% just pure services. What's the revenue mix today? What are you selling? [02:21] >> Right now we are on around 50% of data service, 30% on service itself and 20% on SaaS, but everything has grown from there. We've had some interesting growth last year, kind of 50% growth. And this year we are probably reaching $1,000,000 in revenues. [02:48] That's great. So targeting a million in revenue this year. What did you end last year at? [02:52] >> It was kind of 800,000. Yeah, that was it. [02:57] That's great. Now who are you selling this data as a service data to? Is it banks and financial institutions? [03:04] >> Yes. Actually originally the data we would sell to other intelligence platforms. So we would sell to Bloomberg, to Capital IQ, Movies Analytics. So these guys would get our data, which is like financial data of private Brazilian companies and just put on their products, on their SaaS. And that was our original revenue model. And well, after a while we started selling it to banks as well. So that's our rush right now to put our data directly into [03:39] >> banks and not only in intelligence platforms. That's part of our priorities at the moment. [03:46] Is Capital IQ still paying you today? Are they still a customer? [03:50] >> Sure. Yeah. Yeah. Yeah. [03:52] And what are they paying you for? Is it specifically I know you specialize in financial statements from private companies in Brazil. Are they paying you for all your Brazil company data? [04:02] >> That's right. They are paying to have financial data of private Brazilian companies that we crawl in the web. [04:12] And if your data is really valuable to somebody like Bloomberg or Capital IQ, eventually don't they wanna come buy you so that you don't sell your data to all their competitors as well? [04:26] >> They might, but I don't think that that's attractive enough at the moment. I mean, it's, Bradesuza is a small target for them. It's not like, well, I'm the only one who has data of private American companies and that's a huge [04:44] >> competitive advantage. It's kind of a marginal market to them. So I'm not sure if that's something that would make sense at the moment, maybe once we reach like 5 to $10,000,000 in revenues that might make sense because our process is really unique. It's been proven operationally and [05:11] >> in practical. So, and financially has been growing. But as soon as we escalate enough, I think that that might be something they might look into because these guys, have thresholds for M and A, they're quite higher than our size right now. [05:29] Yeah, that makes sense. How many customers are paying you today? [05:33] >> Oh, directly might be 50 or 60 and indirectly might be 500, 600. [05:40] Give me an example of an indirect customer. [05:44] >> So I sell my data to CapReq and to all these guys, but I have some clients that they don't pay me for my data, but they integrate in their product and they sell my module. So these guys, they have like a 100 clients, they use my module. So that's the indirect customer. [06:03] Can you give an example of one of those companies where your module is built in? [06:07] >> Sure. There's some guys in Brazil called NEOWAY, N E O W A Y. They're quite big in the big data environment here. And well, they resell our data in a module inside their products. [06:25] So if NIO Way in Brazil sells your module, the KLOOX module for a $100 a month to end users, how much about a $100 will you keep versus what NIO Way will keep? [06:37] >> Well, when they distribute me, I keep less than when I sell it directly, obviously. So I have large quantities, but I don't have [06:49] >> the value per customer that I would have selling directly, but I don't have any CAC as well. I don't have any cost of acquisition of customers. So I would sell directly for roughly 3 to $400 monthly, and they would sell me to like a $100 monthly. [07:11] So same question though. If NIO Way sells your module for a $100 a month to an end customer, what percent of that revenue will NIO Way keep versus what will they pay you? [07:20] >> Oh, no. That's what I would get. They would sell it for a little bit more, and then we split it. [07:26] How do you contractually make sure that your value added resellers like NIO Way don't cannibalize your own direct sales by decreasing your module price point inside of their channels super low? [07:39] >> Yeah, it's an awesome question, really good. And the answer is, well, I'm not sure about it. I don't have a way to make sure that they don't cannibalize. It's more of an [07:53] >> empirical conclusion. Well, I think it's not cannibalizing. I'm not getting any feedbacks that indicate that that's happening. Hence how we conclude it. [08:03] I see. How many value added resellers like NIO Way drive you customers every month? [08:11] >> Have a NIO Way and another one called TTR. [08:14] Okay. So just two, two big value added resellers. [08:17] >> That's right. [08:18] I see. It's ttr.com? [08:22] >> It's ttrrecord.com, I think. It's transactional track records. [08:29] >> They originally sell data of transactions like [08:36] >> EV EBITDA multiples. That was their primary business, but they integrated our solution And they are focusing in emerging markets such as Brazil and other South American countries. [08:48] Is it ttrdata.com? [08:52] >> Let me check here. [08:53] I'm certain it's ttrdata.com, but you can tell me if I'm wrong there. So those two channels do a lot of a lot of your reselling. Is [09:04] >> that right? Yeah. T t r yeah. [09:07] Yeah. So they do a lot of reselling. You also go direct. Tell me more about your process. I'm always fascinated because, you know, the whole world I mean, maybe not the whole world, but anyone potentially can get access, right, to a private company financials in Brazil, I imagine, because they have to file something on some public domain. But you have some unique process you use to get to the data, to transform it, to clean it, to [09:29] then spit it back out so that these partners can sort of use it. What did I just say that's not accurate? Or is all that pretty much true? [09:36] >> No, that's pretty much true. But the thing that makes us unique is the data is really hard to find. So there are some sources that are easy to find, but most of the sources are hard to find. And we're the only ones that are investing heavily on crawlers to hard to find sources. And when I mean hard to find sources, it's not like, oh, it's a website that had everything structured. And then it's just like a [10:05] >> bot that goes there and gets to the end of this stuff. No. It's a complete mess because the hard to find places is like thousands of different files that you don't know if that's a financial statement or if it's not. So our bots need to find out if it is a financial statement or not. Those files are in PDF format. So the data is unstructured. So we need to have other bots that take the data out [10:31] >> of the PDFs. And then we have a quality assurance process with humans that make sure that everything is [10:39] >> perfectly classified and structured. We got pretty good on this routine of taking financial data out of PDFs and putting that into tables like JSON or Excel or whatever. So we started even selling this process to banks. Banks receive thousands and thousands of financial statements in PDF formats every day. And it's a complete mess to turn that into actionable data. That's something we do very well. Actually, haven't seen anyone doing that as well as we do anywhere [11:14] >> else. [11:15] Why does this why do you guys have this DNA? You know, how I guess, let me ask it differently. How many folks are full time today at Calux? [11:24] >> 40. [11:25] And how many are engineers? [11:27] >> Seven. [11:28] Engineer. Okay. Are you an engineer or no? [11:31] >> If I [11:32] >> am an engineer, no. I'm I'm I'm graduated in business and administration, but I like code a little bit. Just it's been a necessity to, you know, try some stuff and make sure that we can develop some things I envision. [11:48] So I guess thousands, let's say you get a data dump thousands of documents. It's they're in weird languages. You're not sure if they're even financial statements or not. Your your team of seven engineers have built some process to go analyze those. I guess my question to you is, isn't this something that one day sort of AI and and sort of these LLM models take over? In other words, have you built some moat? Have your seven engineers [12:08] come up with something that OpenAI and these other folks have not thought about where you will always be in need in terms of cleaning that data? [12:17] >> I don't see OpenAI and these folks solving this problem so early, because when you go into PDF tables, there's a lot of things that OCRs don't solve. So if you don't have like a system that goes into PDF and structures that perfectly for the AIs to then take, have an opinion over it. It just doesn't work. The technology needed to solve that is not the chat GPT or OpenAI technology, it's the OCR technology, which [12:55] What's that stand for, OCR? [12:58] >> OCR is optical character recognition. It's a system that looks at a picture or a PDF and identify characters. So it turns like an image to text. So that's the thing. There is one specific problem, which is like grammar errors. Sometimes very often systems, they think that a G might be an eight, for instance. And these confusions are not solved yet, be solved off contextual. And LPs that might understand that in the middle of a sentence of [13:38] >> a word, there's not a number. That's something that AI might help. But when you go into tables and when the OCR, there's another problem that is very common. They make confusion in the line. So they might put a value that is for assets in current assets. And this confusion totally deadly for a risk analysis. [14:03] Yeah. That's interesting. Do you have an example like on your desktop or something that you could screen share of some PDF you got that OpenAI could never deal with because it's so confusing, but your OCR technology is able to read it and extract sort of characters and things that we can read in a programmatic way? [14:23] >> Yeah, the key thing is not exactly the OCR because the OCR, we use different OCRs bought in the market. Real difference is the process, the quality assurance process we do over the OCR process. So we have a system of alerts like, hey guys, this total assets is not equal to total liabilities. There might be something wrong here. So our quality assurance teams just goes into and fixes the problem. And we have like more than 500 alerts [14:55] >> that make our data get out of this process completely perfect and validated. [15:03] >> Mhmm. [15:04] That makes a lot of Tell [15:06] me a little bit more, Alex, as we wrap up. I mean, it's clear the technology here is powerful. You've got big folks like Bloomberg and Captive dot a q paying for it. How do you get more customers? You know, you launched this. I remember back in 2022, chatted, you're doing like 200,000 of revenue. 2023, you get up to $7,800,000. This year, you wanna break a million. You're bootstrapped, so you're in full control, which I love, but [15:24] is there any way you can grow this faster with creative marketing and advertising, but also stay bootstrapped? [15:31] >> Yeah, we have three different products, right? So in our data as a service, I don't see that making the big difference that would change us from a million to a 100,000,000. Where I see that happening is on our service that turns PDFs into [15:50] >> structured data. If we do that internationally, if we can find the channels to [15:58] >> go international with this service, I think it's very unique and [16:04] >> the big tech folks, they're not focusing on that right now. So that might be an advantage at the moment for at least a few years. And I see our SaaS as a nice catapult of growth. And that's something we've never invested that much, but we've been having really nice results in lead generation for the last four to five months. And that's making us very happy and excited about what might come in the future for our SaaS [16:37] >> solution. [16:38] Mhmm. Well, I'm rooting for you. We'll see what happens. In the meantime, let's wrap up here with the famous five, Alex. Number one, your favorite business book. [16:46] >> Oh, favorite business book? [16:49] >> Sapiens? It's not a business But book, it's a book I like. [16:52] >> It works, that works. [16:53] Number two, is there a CEO you're following or studying? [16:58] >> I'm super cliche on these sort of questions. I would say like Elon Musk at the moment. [17:02] Number three, what's your favorite online tool for building KLux? [17:08] >> I'm still with the answer I gave you last year. I love Google Sheets to prototype stuff. [17:15] Number four, how many hours of sleep do you get every night? [17:19] >> Oh, six to eight. [17:21] Okay, that's fair. And did you have a birthday? Are you 38 now? [17:25] >> I'm 38, that's right. [17:27] Okay, still single with one kiddo? [17:30] >> That's correct. [17:31] Awesome. Last question, something you wish you knew when you were 20? [17:36] >> Something I would sorry. Say it again. [17:38] Something you wish you knew back when you were 20 years old. [17:41] >> Oh, I would like to be less afraid of making mistakes. I think that would have helped. [17:48] Mhmm. [17:49] Guys, klooks.com.vr was launched back in twenty twenty one, twenty twenty two. He broke $500,000 of revenue in 2023, cleaning a Brazilian financial data and selling it to big firms like Captivate IQ and Bloomberg, which many of you guys might actually be, you know, listening might actually pay for directly. Some of that data is provided by these groups like Calix. He has specific OCR technology with his seven engineers, but then it's the human layer on top of [18:12] that where they clean the data, sort it, filter it, make sure and check for accuracy before they sell it off to the end market. That really is their secret sauce. 50% of his revenue today, and they'll do about a million dollars this year, 50% of that is data as a service. Another 30% is pure services, and then 20% is true software as a service. Completely bootstrapped, selling through two major resellers in Brazil. That's NeoWay and TTR [18:33] Data, where he takes a cut of the sales there. If he goes direct, he's selling for 300 to $400 per month and has over 50 customers to date. Alex, thanks for taking us to the top. [18:43] >> Thanks, Nathan. Great pleasure to be with you again.

Introduction klooks.com.br now doing over fifty thousand dollars a month in Revenue up from twenty five thousand dollars a month just a year ago they have three business models that is selling all kinds of financial data to Banks they have a data as a service which is sixty percent of the revenue SAS which is 20 of the revenue and service which is another twenty percent of their revenue they've got a team uh today of about 35 folks 26 of which are in engineering and data cleaning uh they help these private Equity firms clean up you know balance sheets profit loss cash flow statements then also aggregate publicly traded data off the internet via scrapers and sell back to the big PE funds or the banks of the world hey folks my guest today is Alex Abu genre he is he's worked an m a advisory for the start of his career and was the CEO of a Manufacturing Company prior to being the CEO of K looks at K looks he's developed routines of structuring unstructured financial data integrating with ocr's automatic data classification quality assurance and also acted as a sales closer in their main contracts with large Brazilian Banks and international financial data aggregators Alex are you ready to take us to the top yeah sure all right this is a really tough space we rely on plaid data for example or teller or codat and so many of them mislabel transaction data in Banks and there doesn't seem to be a good source of Truth for this sort of thing is this the problem you're trying to solve uh yes it is in this uh in this environment but we we are specialized on um structuring data out of PDF files um which is a pretty uh uh difficult task it's quite a mess to to get financial data out of PDF files and put it into let's say tables or you know SQL or any other sort of uh um uh data format uh and we do that usually for for banks and um and we what we structure uh is is a financial statements we get financial statements from companies and we turn those uh those numbers which are pretty messy and confusing uh into something that um credit analysts investment analysts can use to to make decisions so for example if I run a large structured credit fund doing 10 million dollar debt deals into software companies and they're all giving me different formatted QuickBook exports of their of their QuickBooks files and they all have different labels I might just send them all to you and you will translate the labels the founder used with the internal labels that we use to triangulate the profit loss or the balance sheet with the cash flow statement something like that perfect that's that's exactly what we do um it could be a QuickBooks file could be a PDF file out of any accounting system uh we we get that into a structured format and then we classify it into the bank standards uh we do that for for banks and insurance companies but we also do that with public financial statements we have crawlers in the web getting uh financial statements there are public and structuring that into our database and that what that's why we sell to to international data aggregators interesting okay so are you would you say you're a SAS company or you're saying more of a service company uh it's a it's a bit of uh of both we have a assessed service which is a platform you can you know uh log in online with a password and and you can um navigate into our public financial statements Universe uh uh and that's that's our SAS uh but for banks we what we do is service we we so over the last 12 months what was the split would you say percentage-wise between service and SAS um it's there there's a third a third category which is like uh uh data to distribute to Distributors so we just sell like large amounts of data so people could distribute I would say that 60 uh would be uh uh data in in large quantities to Distributors and and Banks like public infrastructure uh 20 would be SAS and the other 20 would be serviced interesting so you really have what we call Das right data as a service is 60 of your business that's your main business that's our main business interesting interesting oh what's going on there YouTube good to see you guys now imagine this you love watching these interviews with SAS Founders but imagine if we took all of the valuation data out from over 2807 interviews I've done manually saves you a lot of time well we've done this we've built the into the beautiful interface inside of founder path check this out I'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for evaluation this year now the secret valuation is there's many different ways to value a SAS business so the reason you're going to see three or four different evaluations inside of your founder path dashboard this is all free by the way is because depending on who's doing the buying of your SAS company you're going to get a different valuation a VC is going to pay a different valuation private Equity Firm is different if you're going to 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 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 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 1.2 million seed round 3.7 raise they sold 22 percent of their business go in here and filter by the event maybe you only want to 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 going out right now and you're raising your seed round well 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 data set of SAS valuation than what you can get now inside of founder path and we're thrilled to bring it to you all right we're gonna go back to the YouTube video here in a second but if you want to check this tool out if you want to jump in and sign up you can check it out for free to get your valuation at this link this link founderpath.com forward slash products forward slash evaluations 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 all right let's jump back into the interview okay very cool uh put this on a timeline well actually I guess before we go there for the data as a service like the average customer what are they paying you per month or per year to access this data the technology uh well when I talk about SAS it's um it's there's more tickets uh it's um 1800 gray eyes which is roughly let's say 300 400 monthly um and but when you when you go to to the the data structuring uh as a service to Banks uh we charge it by by unit we charge it by financial statement that we structure to them and depending on the on the quantity they heard from us it goes to from to an expensive unit uh uh a price to uh uh quite a cheap one and it goes from let's say 25 to five dollars uh at the range per financial statement interesting so if I was going to be a profit loss casual segment and a balance sheet that's five dollars five dollars five dollars so fifteen dollars total yeah that's it interesting okay tell me more about your team how many folks are full-time Financial uh balance sheet and income statements uh it's five dollars or we don't charge per um okay okay that's great how many folks are full-time at your company today calebs ah 35 more or less wow okay how many of them are engineers we have six engineers and uh uh more or less 20 people in our um data quality assurance and well the rest is like administrative sales and stuff how many sales reps do you have that carry a quota uh that's three people counting me oh wow okay you're the you're the head of the chief sales guy right yeah that's right all right very cool okay let's put this on a timeline when did you launch the business what year uh there are different views on that we started the business in 2012 but it was a side business to all the founders at that point um I turned into full-time uh CEO of the company in 2017. so well uh uh till 2017 the company wasn't really uh flowing uh it's starting to happen in 2017. and how many Founders are at the company how many co-founders we were three co-founders and we have one partner which is a fasting uh our CEO interesting now did you guys just split Equity evenly at the start or was it different we did equally ah so you guys are friendly you just said right 25 each 25 each yeah well 33.3 and well I had 20 33.4 uh the four so one had to have a little bit more but uh that's it we we opted to to do it equally that's great now have you guys Bootstrapped bootstrap today or have you raised capital no we bootstrapped it all the way along oh I love that all right and tell me about how you got your first customer oh actually we were accelerated and we had we at that point uh we received like 50 000 or something like that uh but we don't count that because how much Equity at the accelerator uh they never converted it uh it was an accelerator inside a big insurance company in Brazil and they just phased out the project and didn't convert any startups yeah they just lost money at that point okay so so um okay so bootstrap today that's great tell me how you got your first customer um our first customer was um wasn't an uh a private Equity that we knew uh were close to to them we uh uh from our city and they they had the the pain that was the pain that I had that I addressed uh specifically to this kind of guys at that point because uh I worked in lemonade I knew that private Equity firms needed that that sort of data uh financial statements from private companies that were public in the lab and the first the first one was uh was this private Equity company but um we the first real real customer that was that that maintained the business for for a while was um was a large data aggregator um it's um it was capital yq in 2014. so um they they basically didn't have any any data on private president companies Capital IQ was your one of your early customers yeah okay nice that's right that's great okay and then fast forward to today how many customers today oh let's um counting Counting the SAS might be because I had this we have the SAS we have the data as a service we have the the banks the banks might be like 10 more or less uh the our SAS might be something like 35 40. uh Big Data aggregators there are four or five and there are some guys that integrated our data into their systems and they sell that as a module and if you count those guys it's like 200 300 interesting so what does that mean I mean you're if I take that times the r pool the the Monthly recurring revenue average contract values you were talking about earlier I mean you're doing like 50 60 000 a month something like that um yes pretty much that's pretty much that it's um and if it's a good if you're doing that today what were you doing exactly one year ago so we can calculate growth oh half of it oh wow so you've grown 100 year over here yeah that's great okay so from call it 25 000 a month a year ago to fifty thousand dollars plus today what do you hope you can reach in 2023 um good question I think we can go to uh uh something like a hundred thousand dollars a month well we're certainly rooting for you I love that you bootstrapped here do you think you're gonna phase out you know two of the models and only focus on one I mean data as a service it sounds like it's the majority of your Revenue why not kill the other stuff uh that's a nice dilemma we have uh uh when talking among Founders um we we understand that data will be pretty much structured uh in the future so we don't want to quit sales right now it's a small part of our business but we understand that in the future like 2030 plus uh it might be uh the main business that we have because uh um structural data we will will become easier with time but creating intelligence out of it uh will not be commoditized so so early that's that's what so that's why you don't face out the others because we're looking for 2030 and on makes a lot of sense all right Alex let's wrap up here with the famous five number one favorite book our favorite book um sapience number two is there a CEO you're following or studying um well I'll be very cliche it's on Steve Jobs but you know number three what's your favorite online tool for building K looks my favorite online to ah I'm not the deaf I I'm the guy that uh that tries stuff and I I love to do things in Google Sheets and like prove to the devs that things work and then guys look I did it on Google Sheets so please put that in production it works more or less like I love that number four how many hours of sleep do you get every night oh depends on the time of the year but might be seven I'm a good sleeper fair and what's your situation married single kids I have a kid but I'm not married okay not married with one kiddo and how old are you I'm 37 now 37 last question what's something you wish you knew when you were 20. uh I I think that when I was 20 it would be good if I was less afraid of making mistakes dried stuff harder guys there you have it klooks.com.br now doing over 50 000 a month in Revenue up from twenty five thousand dollars a month just a year ago they have three business models that is selling all kinds of financial data to Banks they have a data as a service which is 60 of the revenue SAS which is 20 of the revenue and service which is another 20 of their revenue they've got a team uh today of about 35 folks 26 of which are in engineering and data cleaning uh they help these private Equity firms clean up you know balance sheets profit loss cash flow statements then also aggregate publicly traded data off the internet via scrapers and sell back to the big PE funds or the banks of the world anyways we'll see what happens next Alex thanks for taking us to the top thank you it was great talking to you one more thing before you go we have a brand new show every Thursday at 1pm Central it's called Shark Tank for SAS 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 our poo CAC LTV you name it they share it and the buyers try and make a deal live it is fun to watch every Thursday 1 p.m Central additionally remember these recorded founder interviews go live we release them here on YouTube every day at 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