Stitchedinsightsrevenue, valuation & funding

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