Stitchedinsights Revenue, Valuation & Funding (2024)

[00:00] Hey, folks. My guest today is Dmitriy Pavlov. He's the Founder and CEO of Stitched Insights, a Silicon Valley deep learning pioneer in predictive and consumer insights. He's a guest speaker on emerging tech trends, cross functional team building, and science in marketing. Dmitriy, you ready to take us to the top? [00:13] >> Been ready for a while. Let's go. [00:15] Nice. So, hey, last time we spoke, man, was back sort of middle of COVID. Give everyone a quick reminder. What are customers paying you for? [00:22] >> Yeah. For sure. So we're creating ideal experiences for consumer brands and helping them understand how their customers think and feel against their competitors in their entire category. And so we're charging at a base $10,000 a month per channel. Things like Amazon is a channel or an internal support request channel or Twitter is another channel. And so we've been working with predictive innovation teams, sustainability teams across luxury groups, fashion retailers, leading consumer electronics companies, and essentially understanding, [00:53] >> working with their predictive innovation teams and sustainability teams and e commerce teams, helping them really measure at a really granular level what their customers care about at the attribute level of each product versus their competitors. So they can actually start measuring what is the size of the opportunity in the entire market for their products, for their consumer segments. Basically, is there a consumer segment that's really, really happy with a dimension of a product outperforming, or is [01:20] >> there a consumer segment that really hates a specific dimension of a product their competitor has that maybe you have a really huge opportunity to hit on. [01:27] $10,000 per month per channel is significantly different than, I think, we last spoke, you had, I think, nine customers paying on average sort of a grand a month. So have you really moved up market intentionally? [01:35] >> Oh, yeah. We've completely shifted sort of the entire go to market strategy. What we've realized is basically and before, it was really like MRR and really early POC revenue that we had. Now we have actual ARR revenue. We're actually nearing a little over a million ARR this coming year. And so [01:55] What does that mean? You mean like you take today this month's revenue times 12? [01:59] >> No. We we actually have some some new engagements coming in. So we have a little less than 10 customers at this point. Basically, we have now engagements that are annual contracts as opposed to, just a month by month or a POC, just non recurring revenue contracts. And so we've switched gears completely and and kind of how we're we're monetizing this a little bit. [02:20] Well, so help me understand. So you use in 2020, you said you had nine customers. So you still have nine customers to say, but there are different customers and paying you way more. Is that right? [02:27] >> We were running POCs in 2020 with a number of folks like Sephora's of the world, trying to understand basically what we are deploying and what we are solving for them. And what we realized is really where the real pain point is across all of these brands is they're running surveys on all of their customers. But what they're not doing is they're not serving all of their competitors'customers at that same scale. So when they make product [02:51] >> changes or estimate market opportunity, they're really only estimating in a bubble, in a vacuum. They're estimating what they're doing. What we realized is by actually looking at the external data, we can actually measure the size of the opportunities for these brands. And what we found is who specifically is using these insights. So what we were initially doing is just exploring with [03:13] Got it. So so a year ago, those nine were not customers. They were they were pilots. And so now today, you have nine fully onboarded paying customers, and it's true ARR. [03:22] >> We have live under 10. We'll have over 10 fully true ARR customers by early this coming year. Yeah. We have number customers now for for annual agreements. [03:31] I see. I see. I see. So so what when you just look at booked revenue today, what would you say MRR is? [03:38] >> We're we're sharing ARR essentially. So we're we're gearing up for a series a this coming year, at the in the new year. So we're basically our our target is a little over, 1,300,000 in ARR is is basically from from our existing contracts and their expansion revenue that's coming in from those contracts. Yeah. [03:54] When do you wanna hit 1.3 a year in, like, an ARR? [03:57] >> Yes. Correct. And and When realize No. [03:59] No. When when do you wanna hit that? When? [04:00] >> Oh, probably before q two of this coming year. [04:03] The new year. So that so 1.3 in ARR calculated by, you know, that month's revenue times 12 forward looking when we needed about a 110,000 a month in revenue to get 1.3 in ARR. So you think by q one of next year, before your series a, you'll break a $110,000 a month in revenue? [04:18] >> Oh, for sure. For sure. [04:19] Yeah. And where are you today? [04:21] >> And and so we're we're on our way to that today. We're we're we're not there yet, but we have really, really large contracts coming in right now. And what we've realized is there's expansion revenue with our software, especially where we are sharing these insights with one team, like the predictive innovation team. Yeah, to be clear, I [04:39] totally get all that. Like, I understand the upside. I'm just trying to get a sense of how much you have to grow between now and your series a to hit that mark. So so are you not sharing on purpose sort of revenue today? Or or if not, like, where are you? What's the range? [04:50] >> Yeah. So we're we're actually right in the middle of of a fund raise right now of a of a small, like, c plus round. So we're not Okay. Sharing externally those numbers, but it's it's it's well on the way to it. [05:03] Got it. So, I mean, can we so let's put a big range on it. Can we say you're above $50,000 a month in revenue right now? [05:09] >> I could do rate well, I could tell you that we're under, we're under 700,000 ARR still. [05:15] And and you're calculating ARR, we're taking last month's revenue times 12. Right? [05:19] >> Annual recurring. Yeah. It's it's anything that's signed, contracted for recurring revenue moving forward from 2020 or 2021 on. [05:29] Got it. Still a little confusing, but we'll move on so we can learn other stuff. Got it. So so signing enterprise accounts, you mentioned seed plus. Does that mean you already raised a seed? Did you raise a seed earlier? [05:39] >> So there's not really a name for this round. Yeah. We raised a small chunk, the previous year. We raised a little over a $100,000 basically just to help us commercialize some of this platform. And we've used that essentially to to figure out what is this platform that we can start expanding out. [05:55] And And that was in 2018 you raised the pre seed? [05:59] >> That was about a year and a half ago. I'm I'm getting lost in what year we're in now, honestly. [06:03] Okay. So 01/10 and sort of, like, early round, and you're closing sort of a seed plus right now. How much are you looking to raise right now? [06:09] >> About 3,000,000 is what we're looking at. And we already have some really, really great investors that we're working with right now in middle middle of the conversation with or so. [06:19] Why do you need to raise right now? Like what what what why do you have to, you know, have that kind of money to grow? [06:23] >> Yeah. So I've actually this past year, I've just switched gears from anything other than sales. And I've had the opportunity to speak with dozens and dozens of CDOs, CTOs, CSOs, CMOs, CEOs across these Fortune 100 brands. And I've literally spoken with 30 or 40 Fortune 100 c level executives. And what I found is that we can actually, we can launch this pretty easily because we are offering something really different than what's in the market. We're not [06:52] >> looking at sentiment. We're not looking at overall what's happening for your overall brand. We're understanding each individual dimension, what matters. And I've been able to get a, and we have a new subscription process that we go through. It's an annual agreement that has a three day trial that's attached to an annual agreement already. And so because of all these conversations and this easy piloting process, I've actually created a pipeline of way too many active engagements basically [07:19] >> that I'm realizing we can hit all these if we have additional revenue, if we have additional funding. [07:24] Why why can't you just go [07:26] close them now and you get them to pay upfront and use that money to drive growth? [07:29] >> So we we will potentially do that starting starting next year is is to leverage the money upfront. The easiest way has been to say, give you like for $10,000 we'll give you a snapshot analysis of one of your products against a competitive category set. And then at the end of that month, it turns on into a $10,000 a month subscription. And then we start expanding to additional channels. So that process to get to get paid, like, [07:55] >> large amounts from these brands is is a longer process. So right now, we're still we're not getting enough revenue in to catch essentially all the opportunities that are coming in. So we're trying to accelerate this. [08:06] On the 3,000,000 now, what valuation are you targeting? [08:09] >> Yeah. So that's also we're not sharing that exactly. We raised under $4,000,000 valuation previously, and we're up up ahead of that now basically. How [08:19] much I guess, what's a range of how much percent of the company do you think you'll have to sell to raise 3,000,000? Are talking like 10 to 20, the standard stuff? 10 to 20%? [08:25] >> Probably less than that. We're we're so one of the one of the investors is with the SPV, and they're actually, like, a very, very, very friendly terms so far. [08:36] Not you're looking at debt? You're looking at debt or equity? [08:39] >> Safe. We're looking at a safe. [08:41] Okay. S you said that the one of the partners is at SPV? [08:44] >> SVB, like a special purpose vehicle that [08:46] Oh, SPV. I thought you said Silicon Valley Bank. Got it. Okay. So so got it. So you're raising 3,000,000 at greater than a 30,000,000 valuation, so you can sell less than 10% of the business. That would be a great deal for you. That's what you're targeting? [08:58] >> Yes. Basically. Mhmm. [09:01] And I guess why if you're planning to go raise a big round, like, when you hit the 1.3 ARR mark in q one, like, why spend a bunch of time right now trying to raise 3,000,000? [09:10] >> Well, actually, we got preemptively reached out to by by a number of investors. I I was not going out and raising right now. I thought, actually, let's just focus on these contracts, get them to expanded revenue between each contract, and then next year, go out and raise a larger round. But the advice has been we can bring on additional folks. I can bring on sales support. I'm doing sales myself right now. Like, can bring on an [09:31] >> SDR. [09:32] How many are on the team right now? [09:34] >> It's essentially my well, we have we have folks that are part time and full time. We essentially have the same team that we started with still, and it's just not Dmitriy, [09:42] >> don't know how how many is that? [09:43] How many full time? [09:43] >> We have [09:44] >> we have nine people still, basically the same participating folks that were part time and full time. And what we started doing is bringing on some of those folks full time, but that's still not enough capacity. [09:54] How many right now are full time? [09:56] >> Full time? We have literally about three full time right now. And by the end of November, we'll likely have probably six or seven full time. [10:06] So I guess what happened because, like, back in April I mean, obviously COVID hit, but, like, in April last year, you told me you had six full time employees. Did you let three people go? [10:12] >> No. We we just we built out the platform. What we were building is data pipelines, and we were engineering these these flows. And now these systems work relatively hands off. So the pilots and the subscriptions that we're launching are through channels that we're already we already have full analysis of. So we're just deploying our platform right now. So there's zero development happening, basically, at this point. [10:33] So you let three engineers go, basically? [10:35] >> They're they're actually so one of my one of my head data scientists just started his own company also, and he's just part time helping us now, basically. But my yeah. My take is up until we need development time, like, don't need to develop anything else new up until after November, basically. And we're deploying just with with our existing platform. So So we're focusing on sales 100%, and so I need salespeople and I need support folks. [10:59] Got it, interesting. How do you manage dilution? Mean, this is a lot of capital in an early stage. You don't want to blink and only own 20% of the business. [11:08] >> No, for sure. I mean, honestly, dilution is important, and I don't want to dilute myself too much. But if the value that the investors, etcetera, bring in is more than what we're giving up, I'm happy with that. [11:23] Do you have co founders? [11:25] >> I have a co founding team. I was the original founder. By the time I realized what this market can look like, and by the time we became a portfolio company of One Valley, previously GSV, that's when I realized, oh, we actually have a much larger technology play. And that's when I brought on board Doctor. Johannes Eichstadt, Doctor. Andrew Schwartz. And since, by the way, I think I've mentioned that Johannes is now at Stanford's Human Artificial Intelligence [11:48] >> Lab at the tip of the spear for linguistic and AI research. That's actually been really, really useful for us to really deal with these CTOs and CDOs because they're really actually excited about these. [11:57] You pay him a bunch of money? [11:59] >> Johannes, he got paid a ton of money to help develop these systems. He's now he's now basically [12:05] From you? You you paid him that? [12:07] >> We actually we actually were the founding so we put in money ourselves. We put in about a 250,000 from the founding team to develop these things. [12:15] And a bunch of that went to this doctor that's teaching at Stanford. [12:18] >> Johannes was was also one of the folks that with the founding team that put money in originally. He himself did not take a salary for the first like year and a half. And they actually had like a 3,500,000 non dilutive government grant to help develop this technology. So we took that open source tech that Johannes and Andy created and created a proprietary version, commercial application, and they basically put in all of their sweat equity and additional [12:43] >> money in there. [12:44] So how many how many founders are there then? Three? [12:47] >> So myself and two two co founding teams. [12:50] Did you split a third, a third, a third or no? [12:52] >> No, no, no. Well, came in once we already had a product. [12:58] >> They're adding to this is technology that really separates us from anything else. [13:03] Got it. So you still own the majority of the business, more than 50%? [13:06] >> For sure. [13:07] See. I see. Okay. Cool. Very cool. What else? Anything I'm missing before we wrap up? [13:12] >> Yeah. So what I found is honestly that there's a lot of really interesting shifts going on right now, in the industry. And I'm really excited about that it's being recognized by these brands. So all these brands have been talking about, we really care about customers. We really want to focus on what customers care about. But what I found is that really none of them have the resources or tools to look at all of their customer feedback. [13:35] >> They actually don't know what all of their customers care about. And on top of that, they have no idea what their competitors'customers care about. They're not looking at any of that. And I think that's such a huge missed opportunity for folks to, not just for the brands, but for customers, to have companies really listen to them and start fixing stuff. And so that's where we're kind of trying to wedge ourselves in is to really help [13:56] >> these brands understand what are the things, what are all the things that are top of mind, really the most important things to your customers to drive loyalty and adoption and to drive sustainability. And we're working with these luxury brands. They're building multi year, multi billion dollar strategies to we have sustainability initiatives. We want to fix animal welfare and carbon emissions. But they don't know what about animal welfare, what about carbon emissions is important. Do their customers [14:20] >> want more sustainable leather wristwatches for the animal welfare? Do they want plastics that are more sustainable? They don't actually know. They're just moving on all these things. And where we're coming in is we're actually measuring across all their Maisons, across all their brands, what are those things that matter to your customers the most? What will move the needle to your customers on sustainability? And then after you launch these initiatives, what of those things have impacted you [14:41] >> positively? Have you increased sales because of these changes? And have you increased over the category? Or has the category itself shifted and you just moved to the category? These brands are blind in this space right now. They think they're performing well or underperforming and they're not really taking into account the whole thing. And so we now finally have tools to look at all this massive data. And I'm just really excited that now it's actually getting recognized [15:03] >> by these brands. And I don't know if it's a shift of COVID or just AI is becoming more understood in general, but it's it's such a great trend. I'm really excited about it. [15:13] We'll see what you move, what moves you make next. In the meantime, let's wrap up with the famous five. Number one, favorite business book. [15:19] >> Favorite business book. Oh, man. You know, kill Ganesh. It's an interesting book. [15:24] Number two, is there a CEO you're following or studying? [15:28] >> Yeah. Since I read Bob Iger's book right of a lifetime, I think, I've been following him closely. He's been really interesting. Yeah. [15:35] Number, three, what's your favorite online tool for building your business? [15:38] >> Honestly, like, Google Slides have been really awesome. [15:42] Number four. How many hours of sleep do [15:44] >> you get every night? At least seven and a half. Once in a while, I get, like, three hours just if I'm up all night, but then I'll I'll sleep in later. [15:51] And what's your situation to meet you? Are married, single kids? [15:54] >> No kids. Not married. [15:57] And how old are you? [15:59] >> I'm I'm 34, I think. [16:01] Four. Last question. How many we wish you knew when you were 20? [16:04] >> Oh, man. When I was 20, to learn how to prioritize things correctly and to prioritize experiences and things that shift your perspective more because that's really how you grow. So I that new experience, like traveling, like reading new authors, like mentors, like those things really that shift your perspective is is kind of that that the most important thing to really prioritize, I think. [16:26] Guys, stitched insights founded in 2018, finally helping large luxury brands understand what their customers really want, customer feedback. They have nine paying customers today, call it $5 to $6 thousand dollars a month in terms of ARPU. So flirting with call it $40 to $50 thousand dollars a month, hoping to break the $1,300,000 AR mark by Q1 twenty two, so they're to do another raise. They're currently raising, call it $3,000,000 We'll see what happens there. Raised $110,000 back last year in [16:48] the middle of pandemic at a 4,000,000 valuation. Team of three building this bad boy out. He's looking to hire sales folks. Reach out if you're interested. Dmitriy, thanks for taking us to the top. [16:56] >> He's awesome. Thanks, Nathan. [16:59] 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 alive. It is fun to watch every Thursday one [17:23] p. M. Central. 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hello everyone my guest today is dmitry pavlov he is a product leader on a mission to use technology to understand each other better over a decade of nose-to-tail experience in silicon valley he's uh that's enabled demeter to hone a distinct data-driven vision and cultivate an app an aptitude for creating dramatic category growth before he had prior leadership roles at adp due to conduit wellsphere and tyro wireless dimitri are you ready to take us to the top let's go to olympus mons all right man i love that stitched insights what's the company do and and what is the revenue model how do you plan to make money totally stitch insights is actually transforming the ways that companies are using customer data currently so we're a machine learning engine and we're capturing the underlying thoughts and emotions from customer data and we're actually helping large companies right now gain billions of dollars worth of insights to help them influence how customers actually feel about their brand rather than just the express content okay and we're actually monetizing this through uh through a couple of ways we we found some early traction with some telecoms and some cpg companies and we actually have a a pretty simple straightforward model where we charge 25k a quarter for uh for one data stream so we can basically with with additional 5k per additional data stream a data stream would be something like internal customer support tickets like a data lake or something like customer reviews so we're actually an evolution kind of of what nlp and sentiment analysis is and our technology comes from actually a lab called the world well-being project at a university of pennsylvania um we can jump into that in a minute here yeah and i'd love to you mentioned earlier though i want to understand how early are you set on before the call you are really early what are you doing today in terms of revenue per month totally so we have a number of sows out right now with fortune 100 and 500 folks we've gone through some really really solid early stage pocs with some of these guys and they're now you know expanding into into pretty large pilots we have some uh recurring mrr but it's it's way south of of you know where we need to be at this point yeah we're aiming for a 50k mrr within the next about 12 months or so when will you hit like 10k you think uh 10k probably is coming right around the corner within the next quarter i think we'll be able to do that okay um so what you're like four or five-ish right now something like that yeah it's around there yeah by the way every hundred million dollar company today started at nothing per month so this just means when i have you on in a year i'll be look i'll say look i had him when he had nothing this is great oh yeah all right totally so good um that's great and and the five thousand right now you're doing per month that's across how many customers uh so we have uh we're mostly on sows right now we have a couple of the early customers that we had when we launched in beta about a year ago uh and they're still ongoing on the on the previous platform so this new platform that we just launched is is actually you know way more substantial and it costs a lot more yeah no that's great i mean by the way that's typical but but how many of the early folks you have on youtube like three or four or five something like that uh yeah yeah a couple okay good and then um are you gonna force them into a higher price plan is the big thing everyone always hits when they're early is to move them up oh right so actually we started with aiming at startups initially so we we partnered with gsv labs here actually work portfolio company of gst live yeah and they they have a number of really cool startups here that we started working with and and kind of launched the beta version of our product uh we did that for for a number of months and then we realized that you know in this ecosystem with all the corporate partners around here you know in-house actually here we have amazing folks like ge appliances and 3m and really times of indian big corporations so we realized hey there's a there's a pretty good opportunity to condense our sales process here and actually go through uh go through large companies and actually do do some poc that way and we launched a couple of really early just bare bones products that were able to capture internal data and we used our and our engine basically to look at reviews we looked at things like internal customer support tickets and we were able to actually just really easily show insights that these teams really didn't think we can get to uh what we're doing essentially is like automating market research in a sense where we can understand the entire space of everything that customers care about with our engine basically the not just the express things but the things that make them anxious the things that make products really sticky and this this came out of the project the the world well-being project originally that looked at psychological states from people rather than the expressed uh content they're saying i want to dive more into that in a second but round out the economic stormway so you rate you raise capital it sounds like how much total yeah we're a little over a quarter million right now just from private investments um we're actually raising a 500 convertible note right now um basically to help us finish off some of these larger pilots and go into kind of loud commercial mode and and how many folks on the team is it just you and the co-founder or just you or oh no we actually have a quite a quite a good team here um we have uh dr johannes eichstad who's heading up our data science team uh we have andy who's on our who's our cto as well uh we have a team of about 10 or 11 folks now we actually just had one of our key advisors uh come in and participate in a more kind of a leadership role as well and everyone's in california yeah actually most of us in california half the folks are in the leadership roles are just doing this out of the goodness of their heart for the past the like 10 plus months um some of the more entry-level folks some of the data scientists and engineers are a salary okay got it but everyone in california there uh yeah like 80 plus percent of us we have yeah one of our folks is actually up in in peru right now i think or machu picchu somewhere over there oh cool and and this year is year one or when did you launch the idea uh conceptually we started working on this actually a couple of years ago uh so dr johannes and his team started the the lab to develop this a number of years ago over four years ago and they've been developing this and this is now peer reviewed on their end um and they've actually worked with the cdc in the un to help identify health risks in third world regions and we've been on our side developing this these kind of methods for a couple of years now and we actually launched stitched insights at the beginning of this past year so we're about 10 or so months into it yeah yeah very good it's a 2018 launch date and then walk me through getting your first kind of proof of concept okay kind of going out right how did you get and how did you convince that first person let you try this yeah that was actually really interesting part of our go to market strategy that we figured out we've actually worked with a lot of really cool teams here around the bay ibm watson actually has has been really really useful for us uh we've worked with some folks on that team and they helped us actually frame a really clean go-to-market strategy uh basically we have external data immediately now and we actually targeted product teams that have things like competitive reviews so for our first actually poc a large scale poc we did for a fortune 100 customer was uh we looked at under sink water filters but hold on how did you get in touch with that person though first right they're not fair enough so that that was through our advisory network so gsv labs is actually on our board as well as well and they own a piece of equity um alec wright is who's their cio chief innovation officer he's on our board as well we also have tom kalinski who's uh i don't know if you're familiar with tom he was the ceo of sega of america uh in the 90s he basically introduced sonic the hedgehog and brought that company into several billion dollars he's also responsible for barbie for he-man for flintstones vitamins all these really iconic brands and these guys have amazing amazing networks and we're able to kind of make some really nice early introductions for us to validate some of our kind of early assumptions and the very first thing that we did is kind of tried to do a super super bare bones just the simplest kind of project that we could do and we delivered an interactive dashboard basically they had something like 10 broad insights and the team that we found was actually recommended to us uh through one of our advisors basically we said hey what we can do is we can pull external reviews a sample of about 12 000 reviews from amazon and we can go into that product team the under sync water filter team and actually give them insights about their own customers that they didn't have they so what we're able to do is kind of like validate and replace so how are you getting so part of this like feels like you strike me as someone that's extremely well-rounded extremely well-educated there's a kind of doctor component to this right so it feels very like official and to the point but like make this like dumb this down for me make this extremely real when you say like capture people's emotions it sounds and feels very pie in the sky to me like give me a very practical example yeah so i'll to set the context actually i think it'll be helpful to so the the original point of this of this technology was to look at survey methodologies being used by folks like the cdc center for disease control and what the cdc would do is if they wanted to predict a region's susceptibility to disease like miami's risk for heart disease what the cdc would do is they'll go out and they would survey about a thousand people to get statistical confidence takes a bunch of time a bunch of money so what dr johannes and his team did is they developed a new set of machine learning and essentially an evolution of natural language processing methods that are able to look at the linguistic structure and the syntax of text and by simply looking at tweets they were able to significantly outpredict the cdc for things like uh things like we're able to predict things like is the person depressed is the person anxious about something are they influenced by social factors like their family or are they more influenced by social factors like their friends um and what we realize is there's a really really beautiful application for this technology in the consumer space we can look at what customers are telling businesses in customer support requests in reviews and we can actually under understand a whole new different way of understanding how severity works so if somebody for instance reports hey these three things are broken so for a first poc for this understand water filter that we did a really neat thing came out that people were telling telling the company that hey installation is really critical to us uh the water flow is also really important and how the how much the water tastes is also super important as a product manager you have to figure out okay which one of those three do we actually do we actually focus on what's the most important thing to our customers and previously what we can do is we can look at you know word counts you can look at like tf idf term frequency to inverse document frequency which is useful up to a point directionally but really you're not understanding what are the underlying causes how do they feel about what they're saying so what we're able to do is actually look at those three issues and say actually you know what people are way more anxious about installation not working out than anything else and the thing that actually is predictive of a positive experience is a positive installation right we can actually understand with r value with the pearson product efficient how predictive of something is an experience right and this is kind of new in the space and what we're doing is we're looking at things like internal customer support requests and we have we have actually on the telecom side uh they have hundreds of thousands of calls coming in and an agent has to look at this call and say hey uh this is an issue or this is not an issue this is important and it's not what we can do is because we actually collect data externally anything that's open source we look at things like reviews and competitor views and what we're able to actually find is that if an agent says this is maybe not an issue we can say actually this one issue represents about 20 of the total issues your competitors are experiencing so this is going to be a really severe issue and furthermore we can actually catch that before issues come to these companies so we can start predicting a way really severe issues that they will experience uh and on top of that we can actually understand if a request is coming in if it's an anxious person really telling telling you something we can route that person to an agent that handles anxious people really well right we can we can understand kind of the the underlying intent behind this you actually kind of have a framework for thinking about it but yeah yeah look it's it's interesting i you know for me i am it all sounds wonderful i just i'd love to see it working after you launch one of these pocs and really understand like how it saves someone a ton of money and how it's directly attributable and it sounds like you're well in the way of doing that yeah so one of the one of the main levers that we're hoping to affect is on the market research side we found that on average it's like a 16 to 18 month r d cycle for any product development any month that you can accelerate that that the r cycle is product roi increases by about 15 so what we're doing is actually automating a good chunk of this data because we have all this all this market research already all this preliminary thing so we've done things like creating automated swot analysis where we can actually understand the internal strengths and weaknesses compared to the external opportunities and threats of the entire market and our engine picks this up automatically and it finds things that uh simply looking at frequencies and express things you couldn't understand so it's really quite unique and it's this technology in itself is peer-reviewed which is pretty unique in the space as well and you know our team is is kind of the you know what you want kind of kind of tackling this kind of problem yeah very good all right well look i hope you have it back on in a year we'll see how things are going for now though let's wrap up with the famous five number one dimitri what's your favorite business book uh right now uh console awards by blake harris that's a great book it's about actually tom kalinski and and how he did see what's it called uh console awards console divorce console wars by blake harris it kind of it's a story of how nintendo basically had 98 of the share in the market in the 90s now under tom's leadership basically sega came out victorious and gained billions in the u.s number two number two who's your favorite ceo or ceo fair enough uh you know i'll stick with tom kalinski in this in this in the space number three how many uh sorry what's your favorite online tool for building a business online tool um i like optimizely i think that's a really powerful tool especially for early stage companies number four how many hours i sleep to get every night uh at least seven to be fully functional good and what's your situation married single kids oh no single focusing on this thing until yeah not married no no kids and how old are you uh i'm 30 or 31 one of those 31 i think last and last question what do you wish your 20 year old self knew uh trust your brain trust your instincts trust your those sometimes are very different branded instincts fair enough yeah yeah fair enough trust your gut there you go trust your instincts guys coming from dimitri again paired up with um some research at a university taking it and trying to under understand and do sentiment analysis and really figure out how to drive attribution whether it's you know decreasing your r d cycle or something like that to drive real value into companies today they're a team of 10 people based out there in california launched the company in 2018. they're doing about five grand per month right now in revenue from a couple early customers they raised about 250 grand currently raising another 500 grand on a convertible note dimitri thanks for taking us to the top yeah man thank you for having me