This Week in Startups - AI-Driven Auto Shops with Mastertech.ai and Data Observability with Monte Carlo | E2035

Starting point is 00:00:00 Can I try a query? Yeah. All right. Can we do a 2019 Subaru Outback? Why won't the passenger window entirely go up? Why does it get stuck halfway up and then make us want to screen? So there was actually like two TSBs that were related to issues with the power window for this model. So this is at least a good starting point for diagnosing this particular problem.

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Starting point is 00:01:06 elections. Visit calshy.com slash twist to see live election odds, place to trade, and get $20 when you deposit 100. Hey, everybody. Welcome back to this week in startups. My name is Alex. I'm Alex over on X. You can also find me on LinkedIn, pretty much anywhere around the web. But today, I have good news. We have an excellent founder on the show, part of the launch family. Here we have someone with deep technology experience that seems to have taken a bit of a turn away from her original work in applying startup magic to an entirely different industry. So please welcome to the program. It's Linda Gray. Linda, how are you? Hi, Alex. I'm doing well. Just really thrilled to be on the program. And yeah, thank you for all the support from launch. And it's been fantastic to help us,

Starting point is 00:01:52 you know, kind of get traction and accelerate our company. Awesome. And I just realized I didn't actually say your company's name out loud. So let me fix that. MasterTech.aI. And Linda, I really wanted to talk to you, not only because I like what you're working on, but you spent 15 years at Microsoft as a principal software engineering lead manager, other roles. You also worked at Niantic at the Pokemon Go game.

Starting point is 00:02:17 And, you know, I used to cover Microsoft. I know that company. I have known many people there. I'm a little surprised that you went from those two jobs into what MasterTech is. building. So first of all, tell us the founding story, and I can't wait to hear how this came to be. Yeah, absolutely. It's definitely not a straight line. If you look at my career from Microsoft to Niantic, and that was a little bit of a pivot in of itself. And then now to Master Tech AI, kind of building AI for mechanics, for technicians, you know, front-line workers in these shops.

Starting point is 00:02:52 So, yeah, so my career, I pretty much joined Microsoft out of of college. So, you know, kind of rose up to the ranks from intern when I first interned back in 2004. That was my first stint at Microsoft and joined full time. Kind of work my way up the chain to like I was principal engineer on the Outlook web team for a number of years. Went into engineering leadership, engineering management. So I led teams at Microsoft Outlook, Microsoft Teams at Xbox and build a lot of great software, a lot of great teams, and learned a lot through that process. You know, I think for me, like, you know, after 15 years at Microsoft and, you know, 17 years in tech in general, like I really just didn't want to just do one thing, you know, in my life, right?

Starting point is 00:03:42 And I was like, there's so much more that I'm excited to do. And, you know, and I think being in a large, larger tech company like Microsoft and Niantic, it offers you a lot of great opportunities to make such like large scale impact in the products that you work on. You know, the products that, you know, I'm used to serving like, you know, 200 million monthly active users, right? With kind of the office, you know, set of users, Xbox, you know, Niantic, etc. But it is like hard to kind of really go do the true greenfield projects, the zero to one, like really build something new and innovative that, you know, big tech companies. companies are not really going to necessarily want to take the risk to invest in. So that's sort of what motivated me is like I knew I wanted to eventually like really go

Starting point is 00:04:35 to a startup, do a zero to one project and, you know, and with kind of the innovation that was happening in AI in the last couple of years, the technology shift. It was like perfect time. It was really, you know, like that's what excites me as a technologist. as something that we can really apply in the real world and make real world impact. Yeah, but Linda, so all that tracks. Yeah.

Starting point is 00:05:01 Technology experience, lots of time, different teams, different projects, want to go out there, want to go zero to one, go into something greenfield, but why did you pick, you know, essentially auto shops, mechanics, and the care of cars as the place to apply AI? Yeah, absolutely. So, you know, so when I started looking into the space

Starting point is 00:05:21 and looking at the potential problems that I wanted to solve with building a new product, building a new startup, I was looking at sort of the gamut of the problems that I had experienced in my career, the problems I was familiar with, as well as problems like outside in other domains. And for me personally, I really didn't want to build the same set of or solve the same set problems that, you know, I always see being solved in tech. It feels a lot like a tech bubble, you know, through all my experiences at Microsoft and, you know, these larger, larger tech companies. And I was like, if I do a startup, I don't want to be like, you know, the 20th company that just solves another variant of this particular problem. You know, I don't want to give specific

Starting point is 00:06:09 examples, but it's a lot of like tech companies and engineers solving problems for other tech companies and engineers. And I felt like there was this big rest of the world where there were so many industries in the real world that was underserved and overlooked, you know, and we can actually make such a bigger real world impact by bringing technology to these like underserved markets and actually have a bigger real world impact. So one thing, if I go back into my Microsoft reporting memory bank, and it's, it's been a while since that was my, you know, core day job. But I recall one time talking to a Microsoft team, it may have actually been the team's team about how they were trying to bring the product out to more frontline workers.

Starting point is 00:06:52 Yes. And if I think about frontline workers, people who are actually literally on the ground working on cars as they come into shops and so forth are about as frontline as you can get in the employment space. So is there any connection between the Microsoft frontline push and where you pick to build your startup? Yeah, definitely. And I was on the D-to-D platforms team on the apps ecosystem for teams.

Starting point is 00:07:14 So yeah, there was a lot of opportunities to integrate with third-party apps on the on the developer platform and you know to to work with frontline workers like not directly but indirectly. Got it. But it was still, you know, very approaching problems in a very horizontal manner. And that's sort of what you have to do in big tech companies. When you build like outlook, when you build teams, it is sort of sometimes ends up being a least common denominator for what is kind of the platform. that can enable sort of horizontal experiences. And I was more, like, but it does end up being like a least common denominator experience for any particular, you know, set of problems.

Starting point is 00:07:55 So I was more excited about like, hey, let's really just make the best freaking solution for this particular problem and use technology to us fullest. And that's what I was really excited about. Founders, do you want to sell to bigger customers? I know you do. You got to get that ACV trending up. And you want to push your turn down, right? Sounds good. But to sell to those big buyers, you need to clear all of these compliance checks. You know that. That means you got to have things like SOC2 sorted out. What's SOC2? It's a standard and ensures that companies keep their customer data safe. And if you aren't SOC2 compliant, you can kiss those big deals goodbye. You're not going to land the lighthouse customers. You're not going to be able to operate at the highest end of the market. But Vanta makes it really easy for you to get and renew. your SOC2 compliance. On average, Vanta customers are compliant in just two to four weeks.

Starting point is 00:08:47 It can take months without Vanta. And they automate compliance for GDPR, HIPAA, and more. So you could sell to bigger customers in whatever markup your startup is going after. Vanta is going to save you hundreds of hours of work and up to 85% on compliance costs. Stop slowing your sales, team down, and use Vanta. Get $1,000 off at vanta.com slash twist. That's vanta.com slash twist for $1,000 off your sock two. Okay, so I mean, like, I've owned cars. I don't drive much anymore. My spouse is a great driver, so she does it. But, you know, we have to take care of our vehicles.

Starting point is 00:09:21 We bring them into places, auto shops, dealerships, and so forth. I'm really curious, how technologically savvy is the average car shop that works on vehicles? Because what you guys have built looks really cool. And I think we're going to do a demo here in a minute. But to me, I'm just curious how it translates to folks who are, you know, with a wrench in their hand. a socket in the other. So I get that question a lot. And I think that, you know, the, it is an industry that I think it's more with on the shop

Starting point is 00:09:50 owners side of things. That's more resistant to change. For the technicians, they're actually pretty tech savvy. And it's sort of in the name of the of the job where to learn a lot of different tools that to keep up with, you know, different things for different vehicles. And, and, you know, like a lot of them are Gen Z. They're like much, you know, they, you know, adopt technology. they are TikToking, right?

Starting point is 00:10:12 They're like, so they're used to like, you know, all of these latest technologies. And if there just hasn't been a great tool and application, that's been built for them. And that's what we were really excited about. So, you know, how I got into car repair to answer your previous question. You know, I met my co-founder, Dave. He is a 20-year experience in auto repair industry, you know, working as an ASC master technician, sort of highest rank of being a technician and shop owner. So knows every pain point in the industry.

Starting point is 00:10:41 So as we got together to kind of talk about what's possible with AI, it just really felt like the perfect application of AI. And we're really going to do it in a way that works on the ground in these shops. And we're seeing the adoption, which is really amazing. Like our engagement is going up week or week. It is being used on the ground in these shops by the technicians. So it is pretty cool to see. To further explain what MasterTech does,

Starting point is 00:11:07 I was going to just through all your materials. and I'm going to attempt a summary here. Essentially, if you are a technician working out a car that comes in, there are so many manufacturers, cars, models, different models, different years, different issues that can come up, different technologies. And so what if you had a place that you could go and ask questions and the piece of software using AI, I presume, could fetch the information you need and then present it to you so that way no matter what vehicle comes into your shop,

Starting point is 00:11:34 you know what's wrong and then how to fix it. How is that? Yeah, I think that's a really good summary. So for the job, for these technicians and mechanics on the ground, the job is really two parts, right? It is, one, navigating all of this digital information and technical specification for the vehicles. And then second is a hands-on job that they're performing, like wrenching and replacing components and replacements, etc. So, you know, when we talk to the shop owners, they say that on average, 25% of their technicians, time, it's actually spent doing computer research because every single, yeah, every single vehicle

Starting point is 00:12:14 has a different set of specifications, procedures, issues, and it's a lot of just navigating through all of that data to be able to know, like, what to do on the vehicle. And you have to be very precise if you, like, put in the wrong amount of fluids, you know, for a specific engine that could cause a ton of damage. So, you know, so it's kind of the analogy, like, we like to make is like MasterTech is sort of like analogous to health care, right, for for people, where there's a lot of AI investments in health care right now helping to, you know, coalesce patient history, diagnostics for for doctors and, you know, all of this data, like reading the test results, et cetera. So we're doing that for vehicles. But unlike, unlike humans,

Starting point is 00:13:03 like actually each vehicle comes with a blueprint for, you know, all of its specifications. So, you know, like for you or I. It's like, we don't come with like, okay, here's all the parts and here's all the numbers. And here's like, you know, what, you know, that's something unique for, for this human, right? Whereas this is all the information they have to work with on, on cars. And so it is about bringing that together.

Starting point is 00:13:27 So in terms of the data that we are sort of. That's where I wanted to go with this. Because I was so curious where you guys got all the information, because I presume it's, you know, everywhere in old books that are in glove compartment. and old databases and manufacturing stuff that you can't get your hands on. So tell me about how you got all the data not really ingested, but correct. Yeah.

Starting point is 00:13:48 So the data is like extremely important to get it right. So this is something that we were very conscious of from day one because, you know, with AI, there's always concerns about trustworthiness, about accuracy. So we knew at the start that we did not actually want to train a custom model where, you know, this data is sort of baked into it because at the end of the day, all like LLMs and ML models, it is still a probabilistic answer. And for a lot of these specifications and things, you never want to give an answer that's like probably right. You know, like it has to be right or not right. So, so, you know, we, uh, we make sure to, you know, get our data from the sort of the only real

Starting point is 00:14:33 sources of licensed, uh, OEM data providers. So they, um, license out the data on behalf of the OEMs, but we still have to get approval for our use case with each of the OEMs. And that's been like an ongoing process we've been going through for the past year. But we have majority of the approvals that we need, where all of this data is directly from the, from the OEMs for the procedures, specifications, and everything else. And we're using AI to navigate the user's intent to,

Starting point is 00:15:03 and then serving, pulling the correct data and then serving it to them in the way that can best assist them. So does the AI component of this allow a technician to essentially ask questions and then it parses that, turns it into a query, and then goes to the database of information, the have from OEMs, and then grabs the right bits and then brings it up to them? Yeah, yeah. Something like that. We do have some, you know, kind of like just in time vectorization of the data.

Starting point is 00:15:30 So it allows for semantic search and not just like sort of a keyword search for the OEM databases. So try to be as friendly as possible, you know, which these technicians are not used to. They're used to like very strict like file folder look up for these documents or very strict keyword searches for full documents. So now it's really about having a conversation, finding answers. But every answer is backed up by the source and it's with the original manufacturer copy as well. So going back to the OEM data point, you said you have to get agreements with, permission from the OEMs themselves. And you said you had the majority of them. Do you guys need to

Starting point is 00:16:11 have all of them? Or is there a certain like critical mass of, okay, cool, we have 80% of the OEMs out there for cars. So now we have enough that we can take this out to, you know, the average auto shop and sell it to them. Yeah. So, you know, like it really depends on the shop. But, you know, we are able to have enough value with all of the OEMs we have right now and really only miss you know, two major ones with Honda and Toyota. And those are big manufacturers. I think Honda might be pretty close that we're working with. But there are actually a smaller volume for our primary customers,

Starting point is 00:16:52 which is the repair shops, because they are sturdy cars. So it's like, it is a little bit of like, okay, you know, it's actually like, it's actually fine. Like it is a smaller volume for a lot of our shops. And a lot of our shops are like, they specialize. and European vehicles or something like that. I'm sure there's a BMW specific place. If you have BMW information, you're good to go.

Starting point is 00:17:12 But can I just say how hilarious it is that you see fewer Honda and Toyota cars because they work? I mean, that's really funny. But those are both Japanese car companies. And Subaru is as well, has Subaru come to terms with MasterTil? Yes. Okay. So it's not a Japan problem. It's not like those, there's not like export rules from the Japanese economy that

Starting point is 00:17:32 disallowed this sort of thing. No, no. So we just, we have to get approvals from each OEM separately. And aside from Honda and Toyota, we have basically everyone else that we need. And yeah, we're really hopeful that, you know, Honda and Toyota, like we're seeing some traction with our progress with the approval process, data compliance and et cetera. But it has been a process. But yeah, so that's like kind of the first set of data that we're focusing on is OEM data, which is really critical in these shops. Like I said, you know, it is a pretty critical part. of the job to get all of this information before like doing the actual procedure on the vehicle. But the other two major sets of data we're focusing on is, you know, one, the community data. So, you know, like if you're a technician, right, like, and you're, you know, going from apprentice level to, you know, master technician level. It is really about like, yes, getting all of the OEM, you know, kind of procedures and specifications and knowing how to understand that correctly.

Starting point is 00:18:31 But two, it is a lot of personal experience, right? of like, hey, you know, I have, this is a person that's worked 20 years on Subaru's and knows everything about them, all the ticks and all of the, you know, quirks and issues that's for this set of vehicles. So there's a huge component of the job where it is relying on that kind of human experience and community data. And right now, you know, there are some places where there's forums or, you know, some other like specific places where they go and get this community data. But that's really going to be a focus of ours, you know, at the end of this year, launching like a, you know, user submission content pipeline.

Starting point is 00:19:10 And just the vision is really becoming like the stack overflow for automotive repair. Yeah. I was just thinking that, you know, what would make your service not only very good, but also entirely unique and uncopiable would be to have the technicians that are using it, leave notes, information, and breadcrumbs for people, coming behind them, because then you'd have the OEM data and the real world data, if you will, at the same time, which would be super powerful. Yeah, absolutely.

Starting point is 00:19:39 And that is really the essential parts of the job right now, right? And we just want to build a platform that can, you know, so where you don't have to necessarily spend 30 years, like working on one specific vehicle to gain kind of the insights and knowledge that can be shared across to help everyone else that's working on these vehicles. because this is a very hard job. It is a very dangerous job as well. So auto technicians actually one of the top 15 most dangerous jobs in America.

Starting point is 00:20:09 And a lot of it is because that, you know, there's so much pressure in production and not enough like software help or time to even find all of this precautions or information and safety. And so it is about like, you know, really, you know, using AI, but, you know, not really, you know, not really, you know, our end product is not going to be artificial intelligence. It is going to be sort of helping to accumulate human intelligence, right? That has all of these experiences that has been built up over the years from all of these technicians and, yeah, help to make the job faster, easier, safer for everyone. All right, Squarespace is the place to build beautiful websites. You know that.

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Starting point is 00:22:03 And everybody likes to save a little cheddar and Squarespace is so generous. Squarespace.com slash twist for a free trial when you're ready to launch. You go to Squarespace.com slash twist you get 10% off your first website or domain purchase. That's Squarespace.com slash twist. We love them here. Longest running partner on this week in startups and we really do appreciate that. So Linda, I would love to see this in action. It's great to talk about it, but it's a little hard for me to conceive of what it looks like kind of on the ground. So can you give us a quick tour? Yeah, absolutely. And as you do this, Linda, just for people who are on the audio version of this,

Starting point is 00:22:34 can you just live sportscast as you go through and explain and detail kind of what you're doing? Yeah, sounds good. So, yeah, so as you guys can see here, so basically this is the Master Tech AI platform. You can add your vehicle into the platform that your shop is working on. It is fully mobile optimized as a web application, but we're going to be, building a fully native mobile application in the future as well. So voice, vision, the whole gamut for multimodal AI assistance. But once you have your, and you can also scan in your vehicle when you're on mobile with a VIN scanner,

Starting point is 00:23:16 camera-based VIN scanner as well. But let's say we already have this vehicle added in here for this 2010 Mercedes. So it is like a AI first, chat first. interface where you can get any kind of assistance that you need. We do have some quick actions pre-built for some of the most common actions for performing a procedure, diagnosing an issue, looking at specifications, you know, managing labor times, you know, for your shop, etc. So, so let's just say we are diagnosing an issue today and there's a noise that's on this, on this Mercedes that's coming into the shop and we're just going to say like, hey, let's do a

Starting point is 00:23:56 noise diagnosis and get the kind of assistance that we need on that. So we're going to go through. We're going to pull up the OEM procedure for noise diagnosis for this particular model. It actually comes with some detailed flow charts for what to do, which our AI can help you navigate as you try these different things. And it comes with some of the known issues for this model as far as noise issues that has been reported, you know, this is to NHTSA, National Highway Transport Safety Administration, and all the OEMs are required to, you know, report all of the known issues for any model, which we have fully indexed into our system as well. So it basically gives all of this information about how you can approach this generic issue and, you know,

Starting point is 00:24:49 give some known issues. But, you know, we did give a pretty generic, you know, kind of ask. So, you know, the system detects is like, hey, we can actually narrow this down a little bit. If you can give me a little bit more information. So when is this noise issue occurring? Is it, you know, under certain conditions? So we can say like, hey, it's happening. Let's say when the vehicle is accelerating, right? By the way, you're only putting in, like, noise issue and when accelerating, like you don't have to give it hyper detailed requests.

Starting point is 00:25:22 It's pretty much just giving it a couple of words. and then it goes to work for you. That's right. So when we add in the more specific information, then it's like, hey, based on the fact that it's happening when it's turning, here are some more likely causes of this issue based on all of the available documents. And here are some known issues that's like, hey, there's this one that's been reported that's known for this engine that, you know, there's this noise issue when you're going forward

Starting point is 00:25:47 or reverse. And so it really kind of helps to narrow down kind of the specifics of what the technicians are looking for. So today, without this platform, they're going off to all these different sources, database sources to try to find this information and cross-correlate. And so it's really about doing it faster, doing it more accurately, more comprehensively. And yeah, it's like, it's kind of what I think technology should be used for is, you know, in really helping productivity in the real world.

Starting point is 00:26:18 So can I try a query? Yeah. All right. Can we do a 2019 Subaru Outback? Okay. So let's add that vehicle. 2019. So we'll add it by Yermic model this time and set up in.

Starting point is 00:26:36 Let's say this is the sub model. It's the V6 version, if that matters. Okay. Well, hopefully this one is, this is still good enough. Yeah, because we actually do have a minor annoying issue with it. Okay. So why won't the passenger window entirely go up? Why does it get stuck halfway up and then make us want to screen?

Starting point is 00:26:57 Okay. Let's say why does passenger window get stuck? Perfect. I'm so curious that this is going to work. If this works, Linda, I'm going to jump for joy. So it looks like there were actually some known issues that were found. So it looks like the most likely issues is a faulty power. Windows switch, mechanical issue with the window regulator.

Starting point is 00:27:23 So there was actually like two TSBs that were related to issues with the power window for this model and as well as a Subaru procedure for how to like reset the module and work on it. So this is at least a good starting point for diagnosing this particular problem. So how has traction been in the market? And how much are you guys focused on on great? today versus kind of still building out the product itself? Yeah. So we actually launched publicly to shops May 1st.

Starting point is 00:27:59 So that was only about five months ago or so. So we've gotten so far about 40 shops signed up on monthly subscription. Yeah. And we've actually, the product has we've built a lot more data into the the product, a lot more features. So, you know, with product market fit, it's always an evolving process, but it's been like really amazing actually to see the engagement from our users increase over time. So even the, you know, the users, the same set of users are coming back to the platform as we add more data, as we add more features. And the engagement is growing week over week.

Starting point is 00:28:43 that's per user per shop in addition to the new shops that are that's signing on. So it is really cool to see. Quint with the AI-based platform, the really nice thing is that we can, we know exactly what the users are looking for using our system. It's not a guess game of like, hey, why didn't they click on this button or what were they trying to do? Right. You can see exactly what the users were looking for and whether we were able to help help them. And that really helps us to prioritize what kind of data, we need to get and help with next.

Starting point is 00:29:15 So, Linda, I know your kind of average tier is like $180 a month, 40 shops. You guys are getting close to 100K and ARR. So it seems like there's some good early momentum going. Yeah, yeah. It's been really exciting. And the momentum has been sort of accelerating as we get more data incorporated and seeing better signs of product market fit to now where we're getting more customers from word or mouth. for some of our shop owners to telling other like Facebook groups for shop owners and,

Starting point is 00:29:49 you know, about our product. So we're getting, you know, more and more of that as we, you know, see more signs of product market fit. We are still incorporating a lot more data, you know, so we have, you know, I think most of the data that the shops need on the ground. So procedures, specifications, fluids, like DTC code, diagnosis, help labor times. We're incorporating like wiring diagrams, we're incorporating like maintenance schedules, we're incorporating like the shop management data. So plugging into the solutions they use for record keeping for shop management, getting to customer history, navigating the vehicle history, etc. And then of course, the community data that's going to be coming soon. But yeah, as we're kind of, you know, growing in the product,

Starting point is 00:30:35 we are seeing better engagement, better traction. We're getting into a lot of these kind of coaching groups for shop owners and we're going to be present at some trade shows to help our growth as well. So that's kind of the plan to go forward. All right. So one less question for you. And this one's slightly rude because I love what you built. I'm glad you're seeing traction. I think it's really cool.

Starting point is 00:30:56 But one reason, Linda, why I want to buy an electric car is how simple they are. I don't want to deal with fluids just like I didn't want to deal with carburetors as a child. So does the advent and kind of growth of EVs make car maintenance? so much simpler that it undercuts the future growth potential of Master Tech? We don't think so. So with EVs, it does eliminate some of the maintenance that's associated with the traditional internal combustion engine vehicle, you know, when it comes to like oil change and, you know, things like that.

Starting point is 00:31:33 But actually, it is adding a lot more complications for that the shops today, most of them are not really equipped to fully transition and adopt to. So kind of with the rise of EVEs and also with hybrids as well. So hybrids are even more complicated because you have both sets of systems to service at one time. But we really see a huge potential with our platform, since we're already embedded in a lot of these shops to help them with the transition to servicing EVs to servicing hybrids. So, you know, Tesla is doing a really great job of actually having their service data information open versus some of the more traditional like OEM manufacturers. So being able to have a way to access kind of their service data or, you know, even some of the onboard,

Starting point is 00:32:28 on-board diagnostics and things like that remotely. So it's actually really great. And they're very like, you know, kind of opening the way to like how things. could be done in the future. So we're really looking to kind of leverage that in the future as well. All right. Well, listen, when it hits a quarter million error, give me a call. I'll have you back on and hear how things are going.

Starting point is 00:32:49 But Linda, I really appreciate it. And Godspeed on the go-to-market motion. And in the meantime, where can people find you and the company online? Yeah, thank you so much for having me. So you can find us at mastertech.a.i. Yeah, we're just getting started, but we're really excited for the future. you know, and speaking of the future, this is really something where kind of we're starting in the auto vertical, but, you know, kind of what we're building, we see it easily translating to other verticals as well.

Starting point is 00:33:21 So, you know, we've talked to like HVAC companies that's like, gosh, please build something like this for our industry because the service information we have to work with is even worse than with in auto. So, you know, with something like Master Tech, we really envision a future where, like, hey, let's say you can scan the serial number for your HVAC unit or any kind of like cars, boats, like machinery, and get all of the, you know, service information that you need, like the blueprints, the wiring diagrams to help the technicians on the ground. And it's something with AI, with AR, you know, voice. So that's really like kind of the future vision. There's so much more we could have talked about. I had a whole augmented reality segment that I didn't get to because I talked too much earlier on. But I can also imagine solar panel installer groups would we like to have this to help troubleshoot different things.

Starting point is 00:34:15 Wind power, batteries for both grids and for the home, pretty much anything that requires a lot of maintenance and has a high value. I can see being a master tech vertical in the future. Yeah, absolutely. Well, that's not a small idea at all. Linda, you better go get back to work, but thank you for coming on and we'll talk to you soon. Yeah. Thank you so much for having me. I talk about prediction markets all the time on my podcast, and prediction markets are brilliant because they allow people to buy and sell contracts on future events with prices set by market

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Starting point is 00:35:24 protect themselves if their preferred candidate loses. So go check out the real time election odds, and if you feel confident about who's going to win, why not play some trade? I'm going to do it. So here's your call to action. I want you to check out Kalshi. That's kalshi.com slash twist, K-A-L-S-H-I, K-A-L-S-H-I, K-Sh-Sh-Tw. And get in on the action. You will get an additional $20 with your first deposit of $100. That's calshy.com slash twist. We have an interview coming to you right now with one of the coolest companies in the world of startups. I have tracked this company for a very long time through it series A, through a series C, through a series D. It's been a long time coming. The company is Monty Carlo. Now, with generative

Starting point is 00:36:07 AI making data even more valuable than before, it's the right time to talk to the company because they are a key name in the data observability movement, and that means they are right in the spotlight today. Please welcome to the show. It's CTO and co-founder, Leor Gavis. Lior, hey. Hey, Alex. Good to be here again. Good to see you, man. So, first of all, I would I was prepping for our chat today, going back through the stuff I've written by your company, the space. And I literally did to search for data observability. And one thing that hit me was how popular that term is now. There were so many companies that were advertising against it and trying to grab essentially attention from it.

Starting point is 00:36:45 But if I recall correctly, Monte Carlo actually coined that term a few years back. Yeah, that's right. We were the pioneers with using that terminology. And I can't claim credit to the name of server duty. We were kind of borrowing it from DevOps and from software engineering, but kind of applying it to what was there in a new space for it, which is data and athletics and ML. And that's kind of where we started from when we first researched

Starting point is 00:37:18 Before we even started a company, we figured that data themes kind of struggle with the same sort of struggles that software engineers struggle, which is making sure their stuff works reliably and that they're delivering high quality products to their end users.

Starting point is 00:37:34 Whereas the durability existed for a while in applications and infrastructure, data engineers really had no tooling and I dare say even like no serious methodology to deal with managing reliability and quality and we thought there was an opportunity to help them, to build both the ops

Starting point is 00:37:54 process, if you will, and the technology to support it. And we borrowed the terminology too. We called the data observability. And we built the equivalent of, you know, a data dog and a relic for people that build data systems. So back when I first spoke to Bar Moses, your co-founder, she had to explain the term to me. She had to explain why it matters. the market. And, you know, this was back in probably 2020 or so. And now, of course, so many people are piling into the space. Does that end up being a net positive for Monte Carlo that so many people want to get a bite at the apple? Because it implies lots of attention and so forth, but also more competition. So how does that net out for you guys? Yeah, I mean, we've considered

Starting point is 00:38:37 a big positive for us. You know, five, I think it's probably five years after starting a company after we first spoke and proud to serve over 400 enterprises today. We have customers in every industry, tech, pharma, financing, facturing, sports, education. You name it. We serve companies in space, so people are doing interesting things with data in pretty much every domain, and we're proud to help them make it high quality and high reliability. And it's just natural when there's demand and when there's a real pain and a real need, A lot of people are going to try to solve it.

Starting point is 00:39:17 And from our perspective, we love competition. It does make us better. I don't think we have a monopoly on all the good ideas. Competition does make us better. And it also, I think, signals to customers that the category is important, that there's real interest in it, and that they should be evaluating solutions. And so I'm going to say it's net net positive. And luckily enough, we were in most of the ones.

Starting point is 00:39:44 for big costs of the competition. So we're not doing the negatives too much so far. But I wasn't going to ask, you know, are you crushing Splunk? Are you crushing IBM, Excel data, meta play and strong team? All the companies that are talking about this online. And so it sounds like the answer is yes. But I want to go back to what you said about industries. That's a beautiful segue to where I wanted to go.

Starting point is 00:40:04 Because when I think about the earlier days of Monte Carlo, like I said, Barr had to explain to me what it was. And so I presume the industry, the tech industry, was coming to with the idea. But I think based on what you just said about pharmaceuticals and so forth, that the idea has clearly broken out of the tech space and is now well-known out there in the broader world of business. So I'm curious, was there a moment when you noticed that DataObs as a concept had, you know, escaped the cage and left tech and gone out into the world? Yeah, I was really surprised at that. In the early day, we talked about it.

Starting point is 00:40:38 Nobody really knew what we were talking about. In fact, even data observability didn't click right in. I think the language that actually kind of piques everybody's interest with data downtime, which is the problem that we solved, right? It's this idea that if you're building the data and you're serving the wrong data to your, to your end users, that's downtime, you know, in the same way that the folks at Gmail don't want you to try to load your inbox. and get 404 data people don't want you to load your dashboard

Starting point is 00:41:13 or use your model and get the wrong results, right? And so, kind of using that terminology data downtime resonated from the get-go. And I think over the past few years, we've been able to also educate people about the solution to data downtime, right? Data downtime is the problem. Data observability is one solution for that problem.

Starting point is 00:41:35 And, you know, I don't know there was a single moment that I recall, but, you know, at this point, yeah, most data professionals I talk to and heard the term and know what it means. Gartner has picked up the terminology and the market guides and all kinds of things. So that's pretty exciting. And, and, you know, this is probably from the last 12 months or so, we're seeing more and more. enterprises putting out RFBs for data observability or like, oh, cool.

Starting point is 00:42:13 You know what do you guys? And you want one. Okay, got it. We have one. Welcome to the party. You're all very, very welcome. So on the point of data downtime, the way that I understand

Starting point is 00:42:25 Monte Carlo is that it kind of keeps tabs on data that's flowing through your various pipes as a company and can spot anomalies. So, for example, if a data point comes in at a zero when it's never been zero out probably something's broken upstream. How much ML, I guess what we now call AI, is applied to that process of seeing kind of like anomalies and other issues as they happen? Quite a bit.

Starting point is 00:42:49 So one of the key innovations in the space that we brought on, and that later many other companies about to, was this idea that, hey, if you're a data engineer or data analyst, you can't be expected to track. every single table, every single field you had in your data bases and data warehouses, and really deeply understand how it behaves and what it means for it to be broken. And so we have to use some form of ML and Mal AI to help basically to scale the thing, right, to allow a small group of people that build the thing to actually monitor a lot of data. So there's a lot of machine learning that goes into a novelty detection, right, basically looking at fast patterns of the data, predicting what it should be, and then being able to alert when it breaks from that pattern.

Starting point is 00:43:46 But there's also other use cases of AI and ML there. We can also use AI techniques to analyze not just the data, but also the metadata around it, like descriptions that the humans have created around it, or logs of how the data has been used. or analyze the past that really help inform how to spot breakages and issues. Can you the AI we do to help people get to root positives quicker, right, to find how, what happened and why it happened, where the problems originating from, because these data pipelines are increasingly more and more complex. Yeah. So lots of applications of a valid

Starting point is 00:44:25 AI and data visibility. Yeah. So I was thinking that's what I thought you were going to say. And so my question is very simple. You know, since you founded the company, we have entered into a new AI wave, AI boom, if you will. And I knew you guys were using AI to actually power the engine of the product. And so I'm curious, Leior, why haven't you raised, I don't know, $6 billion at a $100 billion valuation? Oh, good. Well, we've covered some of our rounds. We've been very well capitalized.

Starting point is 00:44:53 And so honestly, we just didn't need to, in a sense. I think the exciting thing about Monte Carlo right now, more than the use. of AI within our product, which has benefit our customers quite a bit. I think the even more exciting thing for me is the fact that we're able to help our customers build AI, right? Because, you know, the models, I think it's kind of weird to say, but all these models are quite incredible, and they're also a commodity. Literally in every company in the world has access to the most incredible models ever built. It's as easy as creating an API key with Open AI or Anthropic or what have you. And so the real differentiator is the data, right, the data that companies are able to basically inject into these models.

Starting point is 00:45:48 And that's where we fit in, right? Like companies are building pipelines that basically power AI applications or analyze on structured data. And guess what? Like these pipelines break like any other pipelines. And our customers are using Monte Carlo to monitor and alert and prevent downtime in those pipelines. And so that's probably one of the most exciting things that happened to us over the past five years. With the concept of data downtime gets really hilarious if you have an AI model ingesting your data and then using that to talk to customers because you might say like, when is my flight going to come?

Starting point is 00:46:22 And then United looks at its database. There's a zero and it goes, what flight? Screw you. And then the customer's recound. So it does really matter. But a beautiful segue because prepping for our chat today, I was just going to. going back through Montecarlo's site, which I have seen, you know, over the years. And you guys are like, we're the AI and data observability company.

Starting point is 00:46:39 And I was like, wow, that's a big. I didn't expect to see so much AI. But then I got thinking about it. And I think you're pretty much right that people want to build more stuff, want to use more AI, have to have the data right. So my question is for Montecarlo, how much of an accelerant has the moment been at people wanting to build AI and therefore paying more attention to their data? Has it been noticeable to your growth rate, to,

Starting point is 00:47:02 New customer acquisition? Close to 100% of our customers ask us about AI use cases while considering solutions because they are either building them right now or are planning to build and invest and they want to make sure that their data of durability provider

Starting point is 00:47:19 is going to support that. We get asked a lot about unstructured data and how we can help monitor unstructured data. You know, up until a year or two ago, we pretty much only did structure data because that's what people were doing with data. That's the only thing that was essentially accessible unless you had an army of PhDs

Starting point is 00:47:41 that can build custom NLP and vision models. And now again, it's a commodity. Everybody can analyze unstructured data. I've seen some really cool use cases with that. And customers are wanting us to help make sure these pipelines are working great. So I can't pay me tests. They can't tell you what our growth rate would have been

Starting point is 00:47:59 without Gen. I can certainly say it's been a booster to our, you know, to our business outcomes for sure. So when you talk about structured data and unstructured data, I'm thinking, you know, data lakes,

Starting point is 00:48:12 data warehouses, data lakes, data bricks. Has data bricks tried to buy you guys? Data bricks? Yeah. It's like you would nest really neatly in right there. I hope the answer is no,

Starting point is 00:48:23 but I'm just curious now that you brought up unstructured data. No, we are friends with beta bricks. You know, we talk regularly with their teams, but we're good partners. We have a lot of mutual customers, but we have never wanted to sell the company and they've never no, the answer is now. Good, good. I'm actually, I'm very glad to hear that because it would be disappointment if, after

Starting point is 00:48:43 all the work you guys have done, you exited before an IPO. I'm literally going to hold you guys to going public at some point in time in the future. So I do want to talk about the business a little bit because you and I spoke back in, I think it was around May of 2022. You guys raised your Series D, $135 million, $1.6 billion valuation. And, you know, I knew at the time you guys were coming off of an incredible period of growth. At the time of your preceding round, you had doubled your ARR in each of the last four quarters. Now, that's a little bit back in the past, a smaller company, but still a good data point.

Starting point is 00:49:15 But at the time when you raised that last round, you told me that you were going to invest across the board and you were going to invest in engineering data product and go to market work in the near future. right after we talked, the world changed. And suddenly everyone was like, don't spend money, don't burn, maybe pay back your growth rate. And so I'm curious, you and I spoke right before the wins changed, if you will. So how did the kind of lived reality of Monte Carlo come to be after that moment and did it match your earlier expectations? Yeah, great question. Yeah.

Starting point is 00:49:50 Fun fact, I think we closed or seriously literally the weeks before the world changed or something. something like that. So it literally happened at the same time. I don't think it changed much in the sense that I'm happy you want to hold us accountable to going public because that's exactly what we stood out to do from the day we built the company. We never, we always had the intention to build a, you know, a long-lasting business. We may fail at it, but that's what we wanted to do. And so from our perspective, you know, from day one, we knew we were going to go through multiple economic cycles, especially for successful, right?

Starting point is 00:50:28 If we fail, we fail, but if we're successful, the company is going to run for, you know, 10 years and more. And in that time, they're just going to be good economic times, bad economic times. And so we never spend the money that we have. We always think about the business and what the business needs

Starting point is 00:50:45 and how do we get as many customers as we can, make them as happy as we can, while keeping the costs and unit economics. within reason for our stage. And so honestly, it didn't change our plans much because we always kind of had the intention of spending responsibly. Now, over five years, of course, there were times where we made mistakes in both directions.

Starting point is 00:51:07 Like sometimes we overhired, sometimes we underhired. And, you know, that happens because, you know, we're humans. But how dare you, sir? In our philosophy. You know, earlier we were talking about generative AI, people wanting to have their data prepared and so forth. I presume people are pushing more and more data through Monte Carlo's vision, if you will.

Starting point is 00:51:27 And I'm just curious, are there economies of scale still at the business that are helping you guys in terms of gross margin and union economics? Or has that stabilized by this point in the company's trajectory? There are certainly economies of scale. And, you know, we make it a point to, we start out pretty early in our, as part of the time being to be responsible with cost. We've been trying to basically be on an improving trend. of gross margins.

Starting point is 00:51:55 So we keep improving it every year. It's part economies of scale. It's part, you know, active efforts. You know, for example, we can, and we do optimize our infrastructure spend as we grow and optimize our code. Overall, it's being a positive trend as we scale. So I also saw that you guys, according to your very own LinkedIn page, Lior, recently hired your first chief revenue officer.

Starting point is 00:52:20 I'm kind of curious for a company of Monte Carlo. scale, you know, five, six years old, series D. Is that kind of like a baby CFO or is that really just a revenue generating focus? It's a great question. We feel it's hard. For us, it's basically a scaling play, right? Like, I think the team that's been there for the first five years are people that are really, really strong at, you know, cracking the playbook, if you will, figuring out how to,

Starting point is 00:52:46 how to do it one. And we are at a point where we need to get more consistency and a larger team and kind of execute across, you know, execute the playbook that we learned across, you know, across the board. And that's why we thought it was time to bring in, you know, a responsible adult, if you will, that will take us in that direction. And, you know, Tim's done before in a number of companies most recently at Stack Overflow, where he kind of made Stack Overflow at a journey of AI business. And we're very excited about what he brings to the table in terms of, you know, scaling playbooks, if you will, across larger team. You can't, you can't tell me, you

Starting point is 00:53:23 hired a guy named Tim to be part of the finance team to be the adult in the room and have it not be a baby CFO. I call BS. That's exactly what everyone says if they hire a CFO. Now we have to do our expenses than 60 days, not 90. It's terrible. Okay. So, so, Lear, I put you guys on the Twist 500, which is our, it's a 105, 106 companies now. It's our list we're building out of the companies we think are going to have the biggest financial outcomes, which is a proxy for innovation, market disruption and so forth. Startups to watch is kind of the basics of it. And I think you guys clearly fit that bill.

Starting point is 00:53:57 But you said something earlier on that I want to close with. You said, you know, we could fail. To me, you know, Monte Carlo at its age, and if I kind of make up some numbers and put them through your historical growth rates, it's a pretty serious business now. You guys have access to lots of capital. What do you mean if we fail?

Starting point is 00:54:17 What does that mean? I've been doing startups long enough to know that things can always go wrong. I think the word failed perhaps is compared to our grand ambitions, right? Like, we want to build an independent business that would eventually go public and then way beyond that, right? Want to build the best company of the decade if we can? It's really hard.

Starting point is 00:54:39 That's really, really hard. And the odds are not in your favor as a founder, right? There's always more chances of failing than succeeding in those kinds of things. and the business is going, it's humming, it's growing. I think it's going away. I think customers are, yeah, there's a real pain, and there's a real need, and we're there to serve it. And so in that regard, I don't think we're going away in time soon,

Starting point is 00:55:03 but, you know, we're shooting for the highest outcomes that we possibly can. Aspirations are good. I never like the phrase, you know, shoot for the stars and maybe you'll hit the moon or maybe it's the way around, but I think that applies here. Keep going for it. I'm all about it. And as a last thing, we talk a lot about how you can make a lot of Monday selling picks and shovels during a gold rush.

Starting point is 00:55:21 And I think we're currently in an AI gold rush. But in the case of Monte Carlo, you guys actually started your picks and shovels business before people knew there was gold. So I got to say, well done, man. Leor, thank you for coming on the show. We'll have you back on next year to see how things are going. But at the meantime, it's montecarlo data.com, yeah?

Starting point is 00:55:40 That's right. Thank you, Alice, for having me. Have fun. Thank you. See you soon. All right. You care.

This Week in Startups - AI-Driven Auto Shops with Mastertech.ai and Data Observability with Monte Carlo | E2035

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