Linda Gray (2:32)

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, frontline workers in these shops. So my career, I pretty much joined Microsoft out of college. So you know, kind of rose up through the ranks from intern. When I first interned back in 2004, that was my first stint at Microsoft and joined full time. Kind of worked 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, Xbox Live 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, I really just didn't want to just do one thing, you know, in my life, right. And I was like I, 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 opportun, 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, et cetera. 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 are not really going to necessarily want to take the risk to invest in. So that's, that's sort of what motivated me is like I, I knew I wanted to eventually like really go do a startup, do a zero to one project and you know, and, 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 you know, something that we can really apply in the real world and make real world impact.

Alex (4:58)

Yeah, but Linda, so all that tracks technology experience. Lots of time, different teams, different projects, want to go out there, want to go 0 to 1, 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?

Linda Gray (5:17)

Yeah, absolutely. So, you know, so when I Started looking into this space and looking at the potential problems that I wanted to solve with building a new product, building a new startup. You know, I was looking at sort of the gamut of, you know, the problems that I had experienced, you know, 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 of 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, this particular problem. You know, I don't want to give specific 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 and we can actually make such a bigger real world impact by bringing technology to these underserved markets and actually have a bigger real world impact.

Alex (6:35)

So one thing, if I go back into my Microsoft reporting memory bank and it's been a while since that was my core day 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. 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 picked to build your startup?

Linda Gray (7:07)

Yeah, definitely. And I was on the D2D platforms team on the apps ecosystem for teams. So yeah, there was a lot of opportunities to integrate with third party apps on the developer platform and to work with frontline workers, not directly, but indirectly through serving them. But it was still 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 Outlook, when you build teams, it is sort of sometimes ends up being the least common denominator for what is kind of the platform that can enable sort of horizontal experience. And I was more like, but it does end up being like least common denominator experience for any particular set of problems. 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 its fullest. And that's what I was really excited about.

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Alex (9:11)

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. 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 and a socket in the other.

Linda Gray (9:42)

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 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, have 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, they, you know, adopt technology. They are tiktoking, right? 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 a ASE master technician, sort of like highest rank of being a, being a technician and shop owner. So knows every pain point in the industry industry. 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 you know, and we're really going to do it in a way that works on the ground in these shops and we're seeing the, the adoption which is, which is really amazing. Like our engagement is going up week over week. It is being used on the ground in these shops by the technicians. So it is pretty cool to see.

Alex (11:05)

To further explain what master tech does, I was going just through all your materials and I'm going to attempt to summary here. Essentially if you are a technician at 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 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, you know what's wrong and then how to fix it. How is that?

Linda Gray (11:38)

Yeah, I think that's a really good summary. So for the job, for these technicians and mechanics on the 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 the hands on job that they're, they're performing like wrenching and you know, 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 is actually spent doing computer research. Because every single, yeah, every single vehicle 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 what to do on the vehicle and you have to be very precise. If you put in the wrong amount of fluids for a specific engine that could cause a ton of damage. So it's kind of the analogy we like to make is master tech 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, etc. So we're doing that for vehicles, but unlike, unlike humans, like actually each vehicle comes with a blueprint for, you know, all of its specifications. So, you know, like for you, for. Or I, it's like we don't come with like, okay, here's all the part cars and here's all the numbers and here's. That's something unique for this human. Right. Whereas this is all the information they have to work with on cars. And so it is about bringing that together. So in terms of the data that we are sort of.

Alex (13:30)

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 everywhere in old books that are in glove compartments 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 only ingested, but correct.

Linda Gray (13:48)

Yeah, 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, we made sure to, you know, get our data from the sort of the only real sources of licensed OEM data providers. So they 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, classifications and everything else. And we're using AI to navigate the user's intent to, and then serving serve, pulling the correct data and then serving it to them in the way that can Best assist them.

Alex (15:09)

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 that you have from OEMs and then grabs the right bits and then brings it up to them?

Linda Gray (15:23)

Yeah, yeah, something like that. We do have some, you know, kind of like just in time vectorization of the data so it allows for semantic search and not just like sort of a keyword search for the OEM databases. So try to, you know, be as friendly as possible. You know, which, which these technicians are not used to. They're used to like very strict, like file folder lookup for these documents or very strict, like 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.

Alex (16:01)

So going back to the OEM data point, you said you have to get agreements with or permission from the OEMs themselves, and you said you had the majority of them. Do you guys need to 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.

Linda Gray (16:23)

Yeah. So, you know, like, it really depends on the shop, but, you know, we, we are able to have enough value with all of the OEMs we have right now. And really only missing, you know, two major ones with Honda and Toyota, and those are big manufacturers. I think Honda might be, might be pretty close that we're working with. But they're actually a smaller volume for our, our, our primary customers, which is the repair shops, because they are sturdier cars. So it's like, it is a little bit of like, okay, you know, it's, 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 in European vehicles or something like that.

Alex (17:08)

I'm sure there's a BMW specific place. If you have BMW information, you're good to go. 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 Masterpiece?

Linda Gray (17:26)

Yes.

Alex (17:26)

Okay, so it's not a Japan problem. It's not like those, there's not like export rules. From the Japanese economy that disallow this sort of thing?

Linda Gray (17:34)

No, no. So we just, we, we have to get approvals from each OEM separately and aside from Honda and Toyota, we have basically everyone else that, that we need. And yeah, we're really hopeful that, you know, Honda and Toyota, like we're seeing some traction with, with our progress with the approval process, data compliance and et cetera. But it has, 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 sh. 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. 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 Subarus and knows everything about them, all the ticks and all of the, you know, quirks and issues that's afford the 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 at the end this year. Launching user submission content pipeline and just the vision is really becoming the stack overflow for automotive repair.

Alex (19:14)

But I was just thinking that what would make your service not only very good but also entirely unique and uncopyable 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.

Linda Gray (19:38)

Yeah, absolutely. 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. 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, our, our end product is not going to be artificial intelligence. It is going to be sort of helping to culminate human intelligence. Right. That has all of these experiences that has been built up over the years from, from all of these technicians and yeah, help to make the job faster, easier, safer for everybody.

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Alex (22:20)

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?

Linda Gray (22:29)

Yeah, absolutely.

Alex (22:31)

And as you do this, Linda, just for people who are on the audio version of this, can you just live sportscast as you go through and explain in detail kind of what you're doing?

Linda Gray (22:39)

Yeah, sounds good. So yeah, so as you guys can see here, so basically this is the master tech AI platform. Platform. You can add your vehicle into the platform that your shop is working on. It is fully mobile optimized, it's 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, 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 a 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 more most common actions for performing a procedure diagnosing 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 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 just the OEM procedure for noise diagnosis for this particular model. It actually comes with some detailed flowcharts for what to do, which our AI can help you navigate as you can 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, this is to NHTSA, National Highway Transport Safety Administration and all the OEMs are required to 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 give some known issues. But we did give a pretty generic ask. 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 under certain conditions so we can say like, hey, it's happening. Let's say when the vehicle is accelerating. Right.

Alex (25:13)

By the way, you're only putting in like noise issue. And when accelerating, you don't have to give it hyper detailed requests. It's pretty much just giving it a couple of words and then it goes to work for you.

Linda Gray (25:25)

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, that there's this noise issue when you're going forward 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, like it's kind of what I think technology should be used for is, you know, in really helping productivity in the, in the real world.

Alex (26:18)

So can I try a query?

Linda Gray (26:20)

Yeah.

Alex (26:21)

While we're here. All right. Can we do a 2019 Subaru Outback?

Linda Gray (26:25)

Okay, so let's add that vehicle, 2019. So we'll add it by year, make, model this time instead of VIN, let's say this is the sub model.

Alex (26:39)

It's the V6 version, if that matters.

Linda Gray (26:41)

Okay, well, hopefully this one is, this is still good. Yeah.

Alex (26:45)

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?

Linda Gray (26:57)

Okay, let's say why does passenger window get stuck? Perfect.

Alex (27:03)

I'm so curious if this is going to work. If this works. Linda, I'm going to jump for joy.

Linda Gray (27:10)

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 window switch, mechanical issue with the window regulator. So there was actually like two TSBs that were related to issues with the power window for this model and as well as a, a Subaru procedure for how to like reset the, the module and, and work on it. So, so this is at least A good starting point for diagnosing this, this particular problem.

Alex (27:42)

So how has traction been in the market and how much are you guys focused on on growth today versus kind of still building out the product itself?

Linda Gray (27:52)

Yeah, so we actually launched publicly to shops May 1, so that was only about five, 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, the product has, we've built a lot more data into 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. That's per user, per shop in addition to the new shops that are, that's signing on. So, so it is, it is really cool to see 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 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.

Alex (29:15)

So Linda, I know your, your kind of average tier is like 180 bucks a month, 40 shops. You guys are getting close to 100k in ARR. So it seems like there's some good early momentum going.

Linda Gray (29:26)

Yeah, yeah, it's, it's been really exciting and the momentum is, 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 of mouth for some of our shop owners to telling other like face group Facebook groups for shop owners and 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 increasing, bringing 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 their customer history Navigating the vehicle history, etc. And then of course, the community data that's going to be coming, coming soon. But yeah, as we're kind of, you know, growing the product, we are seeing better, 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.

Alex (30:49)

All right, so one last question for you and this one's slightly rude because I love what you build. I'm glad you're seeing traction. I think it's really cool. 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?

Linda Gray (31:17)

We don't think so. So with EVs it does eliminate some of the like 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. 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 EVs and also with hybrids as well. So hybrids are even more complicated because both sets of systems to service at one time. But we really see a huge potential right to 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 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, onboard like diagnostics and things like that remotely. So it's actually really great. And they're, they're very like, you know, kind of opening the way to like how things could be, could be done in the future. So we're really looking to kind of leverage that into in the future as well.

Alex (32:45)

All right, well listen, when it hits a quarter million ARR, give me a call and we'll have you back on and hear how things are going. But, 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.

Linda Gray (32:56)

Yeah. Thank you so much for having me. So you can find us at MasterTech AI yeah, we're just getting started, but we're really excited for the future. And speaking of the future, this is really something where kind of we're starting in the auto vertical, but kind of what we're building, we see it easily translating to other verticals as well. So we've talked to H Vac 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 in auto. So with something like Master Tech, we really envision a future where, hey, let's say you can scan the serial number for your H VAC unit or any kind of, like cars, boats, like machinery, and get all of the 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 voice. So that's really kind of the future vision. Yeah.

Alex (34:03)

All right. 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. And solar panel installer groups would like to have this to help troubleshoot different things. 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 it being a master tech vertical in the future.

Linda Gray (34:25)

Yeah, absolutely.

Alex (34:26)

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.

Linda Gray (34:32)

Yeah, thank you so much for having me.

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Alex (35:52)

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 its series A, through its series C, through a Series D me. It's been a long time coming. The company is Monte Carlo now with generative 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 Lior Gauche. Lior. Hey.

Lior Gauche (36:21)

Hey Alex. Good to be here again.

Alex (36:23)

Good to see you man. So first of all, I was prepping for our chat today, going back through the stuff I've written about your company, the space, and I literally just did a 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, but if I recall correctly, Monte Carlo actually coined that term a few years back.

Lior Gauche (36:49)

Yeah, that's right. We were the pioneers with using that terminology. And, and I can't claim credit to the name observability. We were kind of borrowing it from DevOps and from software engineering, but kind of applying it to what was then a new space for it, which is data and analytics and ML. And that's kind of where we started from when we first researched, before we even started the 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. But you know, whereas verbally 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 process, if you will, and the technology to support it. And we borrowed the terminology too. We called it data observability and we built the equivalent of a data dog or a new relic for people that build data systems.

Alex (38:11)

So back when I first spoke to Bar Moses, your co founder, she had explained the term to me. She had explained why it matters in 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?

Lior Gauche (38:36)

Yeah, I mean, we consider it 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, finance, manufacturing, sports, education, you name it. We serve companies in the 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, a real need, a lot of. A lot of people are going to try to solve it. 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. So I'm going to say it's net, net positive. And luckily enough, we win most of our big cops, the competition, so we're not feeling the negatives too much so far.

Alex (39:49)

But I was going to ask, are you crushing Splunk? Are you crushing IBM, Excel, Data, Metaplane, 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. Because when I think about the earlier days of Monte Carlo. So, like I said, Barr had explained to me what it was, and so I presume the industry, the tech industry, was coming to grips 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 got out into the world.

Linda Gray (40:33)

World.

Lior Gauche (40:34)

Yeah, I was really surprised with that. You know, in the early days when we talked about it, nobody I think really knew what we were talking about. In fact, even data observability didn't click right in. I think what the language that actually kind of piqued everybody's interest was data downtime, which is the problem that we solved. Right. It's this idea that you're billing the data and you're serving the wrong data to your, to your end users. That's downtime. 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 or use your model and get the wrong results. 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.

Alex (41:29)

Right.

Lior Gauche (41:29)

Data downtime is the problem. Data observability is one solution to that problem. And you know, but I don't know if there was a single moment there call, but at this point, yeah, most data professionals I talked to have heard the term and know what it means. Gartner has picked up the terminology and is the general market guides and all kinds of things. So that's pretty exciting. And this is probably from the last 12 months or so. We're seeing more and more enterprises putting out RFPs for data observability. We're like, oh cool, you know what it is and you want one? Okay, got it. We have one.

Alex (42:17)

Welcome to the party. You're all very, very welcome. So on the point of data downtime, the way that I understand 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 zeroed 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.

Lior Gauche (42:49)

So one of the key innovations in the space that we brought on, and later many other companies adopted too, 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 have in your databases 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 now 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 anomaly detection, basically looking at past patterns of the data, predicting what it should be, and then being able to alert when it breaks from that pattern. But there's also other use cases of AI in the middle 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 analyzed in the past that really help inform how to spot breakages and issues. Can use AI we do to help people get to root causes quicker, right? To find out what happened and why it happened, where the problem is originating from. Because these data pipelines are increasingly more and more complex. So lots of applications of windmill and AI and data observability.

Alex (44:27)

Yeah. So I was thinking. That's what I thought you were going to say. And so my question is very simple. Since you founded the company, we have entered into a new, 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, Liora, why haven't you raised, I don't know, $6 billion at a $100 billion valuation?

Lior Gauche (44:46)

Oh, good. Well, we've covered our, some of our rounds. We've been very well capitalized. 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 benef quite 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 today, it's kind of weird to say, but all these models are quite incredible and they're also a commodity. Literally every company in the world has access to the most incredible models ever built. It's as easy as creating an API key with OpenAI 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. And that's where we fit in, right? Like companies are building pipelines that basically power AI applications or analyze unstructured data stuff. And. And guess what? Like these pipelines break like any other pipeline and, and and, and our customers are using Monte Carlo to, to monitor and alert and, 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.

Alex (46:13)

Well, with the concept of data, downtime gets really hilarious if you have a, a 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? And then United looks at its database, there's a zero, and it goes, what flight? Screw you. And then the customer freaks out. So it, it does really matter. But a beautiful segue because prepping for our chat today, I was just going back through Monte Carlo's site, which I have seen, you know, over the years, and you guys are like, we're the AI and data observability company. 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 Monte Carlo, how much of an accelerant has the moment been of people wanting to build AI and therefore pay more attention to their data? Has it been noticeable to your growth rate to new customer acquisition?

Lior Gauche (47:03)

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 durability provider 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 structured 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 that can build custom NLP and vision models. And now, again, it's a commodity. Everybody can analyze unstructured data, and we'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 ab test. I can't tell you what our growth rate would have been without generative AI coming, but I can certainly say it's been a booster to our business outcomes for sure.

Alex (48:07)

So when you talk about structured data and unstructured data, I'm thinking data lakes, data warehouses, data lake houses, data bricks. Has databricks tried to buy you guys databricks? Yeah. You would nest really neatly in right there. I hope the answer is no. But I'm just curious now that you brought up unstructured data.

Lior Gauche (48:26)

No. We are friends with databricks. You know, we talk regularly with their teams but we're good partners. We have a lot of neutral customers. But we have never wanted to sell the company and they've never. No, the answer is no.

Alex (48:40)

Good, good. No, actually I'm very glad to hear that because it would be disappointment if after all the work you guys have done, you exited before an ipo. I'm literally going to hold you guys to going public 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 D135 million, $1.6 billion valuation. And you know, I knew at the time you guys are 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. Yeah, that's a little bit back in the past. A smaller company, but still a good data point. 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 pare back your growth rate. And so I'm curious, you and I spoke right before the winds changed, if you will. So, so how did the kind of lived reality of Monte Carlo come to be after that moment and did it match your earlier expectations?

Lior Gauche (49:48)

Yeah, great question, Fun fact. I think we closed our serious D literally the weeks before the world changed or 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 set 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. Yeah, 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 if we're successful. Right? 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 spent the money that we had. We always think about the business and what the business needs and how do we get as many customers as we can, make them as happy as we can, while keeping, you know, 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 both directions. Like sometimes we over hired, sometimes we underhired and that happens because we're humans. But how dare you, sir. Changed our philosophy.

Alex (51:16)

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. 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 unit economics or has that stabilized by this point in the company's trajectory?

Lior Gauche (51:39)

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 costs. We've been trying to basically be on an improving trend of gross margins. So we keep improving it every year. You know, 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, seeing a positive trend as we scale.

Alex (52:12)

So I also saw that you guys, according to Your very own LinkedIn page, Lior recently hired your first chief revenue officer. I'm kind of curious, for a company of Monte Carlo scale, 5, 6 years old, series D, is that kind of like a baby CFO or is that really just a revenue generating firm focus?

Lior Gauche (52:31)

It's a great question. We feel it's or 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, and figuring out how to, how to do it once. And we, we are at a point where we need to get more consistency in 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 felt 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 has done before in A number of companies, most recently at Stack Overflow, where he kind of made Stack Overflow a generative 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 teams.

Alex (53:21)

You can't. You can't tell me you 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 called the. Yes, okay, that's exactly what everyone says, that they hire a CFO. Now we have to do our expenses within 60 days, not 90. It's terrible. Okay, so, so Lior, I put you guys on the Twist 500, which is our. It's 105, 106 companies now. It's. 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. 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? What does that mean?

Lior Gauche (54:18)

I've been doing startups long enough to know that, you know, things can always go wrong. I think the word fail, perhaps, is compared to our grand ambitions. Right. Like, we want to build, you know, an independent business that would eventually go public and then way beyond that. Right. Want to build the, you know, the best company of the decade, if we can. That's really hard. That's really, really hard. And, and, and the odds are, you know, not in your favor as a founder. Right. Like, there's more, there's always more chances of failing than succeeding in those kinds of things. And the business is, is going, it's humming, it's growing. I don't think it's going away. I think customers are, you know, 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 anytime soon, but, you know, we're shooting for the highest outcomes that we possibly can.

Alex (55:07)

Aspirations are good. I never liked the phrase, you know, shoot for the stars and maybe you'll hit the moon, or maybe it's the other 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 money selling picks and shovels during a gold rush. And I think we're currently in an AI gold rush 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 gotta say, well done, man. Lior, thank you for coming on the show. We'll have you back on next year to see how things are going. But in the meantime, it's Monte Carlo data dot com.

Lior Gauche (55:39)

Yeah, that's right. Thank you, Alex, for having me. Have fun.

Alex (55:42)

Thank you. See you soon.

Lior Gauche (55:43)

All right. Take care.