Vertical AI Innovations: Abacus LLM in Finance & Synthesia's Video Avatars | E2112

Hey, everybody. Welcome back to Twist. This is Alex and I have two amazing interviews for you on this very fine Tuesday. The matigly they are linked because we are talking about the application of AI to particular sectors. This is often called vertical AI, kind of building off the idea of vertical sass, but taking generative AI tools and applying them to one particular industry. First up, we're going to talk to advocates. Now this company wants to bring gen AI into regular industries, think credit unions, banks and insurance companies. It's very interesting to hear how they're going about approaching that market. It's quite different than other AI first companies and how they're going after their own markets, especially if they're more consumer oriented. Then second, we're going to talk to Synthesia, a company that I actually demoed on the show a couple months back. There are twist 500 company and they're working on basically video generated AI models of people for things like training videos and so forth. Well, they have a pretty important revenue milestone to talk about and some very cool technology and they have notes on where people are buying genetic technology inside the enterprise today. So if you're a little tired about hearing about the newest model this and the newest benchmark that I want to know where does the rubber meet the road. Well, here you go. Let's start with the topic. This week in startups is brought to you by Atlassian from MVP to IPO. Atlassian for startups provides your team the right tools to plan track and collaborate on work. Head to Atlassian.com/startup/twist to see if he qualified for 53 seats for 12 months. VAPI, at real time AI powered voice conversations to your apps or business in minutes, not months. Go to VAPI.AI/twist and get 1000 minutes free per month for life. And HubSpot for startups smart founders aren't piecing together random tools. HubSpot is the customer platform that thousands of startups used to scale efficiently get up to 75% off plus three months of perplexity AI for free. Go to HubSpot.com/startups. Go back in time. A couple of quarters maybe a year, a year or a half. Everyone was curious. What are all these AI technologies going to be used for? Will they have a real world application? Are they just very fancy toys that cost a lot of money to ride? Well, the market has answered that. The answer is yes. AI models do have a lot of value. Bringing AI to the enterprise does have real grit to it. But some companies are taking this in a different direction. Instead of building a tool or a model that works for everybody, they're going niche and trying to take one industry down to the studs to build something just for it. One of these companies is Abagus because you've been find a go abacus dot co. We're going to talk to founder David Moscow tell you about what he's building and why he decided to go after a regulated industry so hardcore that I think he must be at least half insane to have selected it. Please welcome to the show. It's David David how you doing? How's it going Alex? I am part insane. That is true. So I appreciate the intro. I don't have to surprise anyone. We were just talking before we hit record and I learned that you're based in Chicago. And I have done four Chicago winters. And I have gone through snowpacolus. I have lived the lake effect. I learned about depression. So just give Chicago a shout out for me and then tell me why you're building in my beloved windy city. Yeah. Chicago's great. I've tried to ask my parents tactfully before why do we live here? Why of all places? Why did you pick this? Then they've never been able to give me a good answer. But in Chicago, we're building a company called Avicus. Avicus is generative AI for regulated and industry thing. Credit unions insurance companies. And what we like to say is Avicus is where AI meets assurance. So we're really about helping enterprises not only have an LLM solution with our on-prem solution, but also helping them with response control and then anti-illucination. So we have a whole package platform for enterprises. Remember doing quite well with that. From a relatively high level, instead of making a wrapper around something that open AI, built or enthrotic or pick a company, you guys have your own model that you're taking to the the financial industry, the lack of a better broad term, which is very much regulated. And also in my personal view, just not hyper technology savvy. Like I mean, we joke about checkbooks, well, who was sending those out still, right? So am I wrong to think that the world of financial services is relatively behind the times because it feels like you picked a difficult niche. I joke to my intro, but I'm honestly quite curious about their posture towards adopting technology, let alone generative AI and leading that. Yeah, so it's a good question that you ask. And you know, banks, I will say that the first bank I ever worked at, this is True Story. They gave us a package of red grease pencils to mark up bank statements, do you reconciliations? True Story. So you're not completely wrong when you say banks are sort of behind the technology curve. But I will say they've started to recognize this is a real serious need for their business, for their customers, when they can't avoid, but they want to do so in a bank like way. What does that mean? Very little risk, lots of control, right? Something that they can really have a lot of purview over. And so that's my advocate is kind of come up as a solution for them. And they're really looking for, if you think about Alex looking for three things when they want, you know, an LLM or any kind of use software, they want something that they don't have to worry about their data. So they want to be on-prem or very secure. They need something that's going to index all their data, right? So if you're a bank or a credit union insurance company, you know, they don't have one place where all their documents and data are, right? They have tens of thousands of documents and they could have hundreds of different data sources, right? You know, how do they bring all that together? Yeah, index for an LLM, right? So they need that. The second is the answer has got to be accurate, right? You can't ask an assistant what it's our mortgage rate and have the assistant give the wrong rate. Now you've got a problem, right? It creates all sorts of compliance risks, right? Yeah. And then you need response control, right? So advocates or any virtual assistant like software might give a response, but maybe that's not the response they want that piece of software to get. So they need some way to control the responses whenever they feel appropriate. So those three things, you know, in place as I've described them, I think banks are more willing to explore this avenue as a potential solution for not just internally, but their customer. You know, we ask a lot of companies that jump on the show, you know, what's your moat? And it sounds like really in this case, getting a product set up to work inside of an industry with those very strict regulations is a moat in and of itself because who wants to go do all that work? It sounds very, very complicated. But instead of going product to the model, let's start the route and then build up. So you guys have made something called the Apicus OS private LLM. It's a 30 billion parameter model. And I'm not going to lie David, when I hear we trained a model, I just imagine a crater in the ground with just cash burning coming out of it because that's the narrative we hear, right? From a lot of folks. I know you can do it cheaper, Databricks has, etc. But tell me about this model. You trained how you went about it and just the cost books. I'm sure a lot of founders listening are curious about the process and the why. Yeah. So we started with a base open source model, which was a mosaic 30 billion parameter model, right? Sure. And then we have a lot of data. So from all of our clients, right? We have about six million queries every single month that come in. So we have a ton of data in terms of the questions asked by our clients. And this data is very specific, right? So folks ask, Hey, how do I open a new debit card in DNA, right? If you ask any general LLN that question, they're going to think DNA means the genetic structure. Really, if you're a banker, credit union, you know DNA stands for Pfizer, right? Things like that that were able to extract out and then fine tune in layer on top of that base model. Some really impressive enhancements, right? The second thing we do is at Abecus, we have what we call a parent model. So very large, very bloated model. This is a lot of different things. And then what we deploy on the banks infrastructure credit union insurance infrastructure is called a sister model. And the parent model teaches the sister model how to behave, right? So the sister model mimics the parent behavior. This is often referred to as knowledge distillation. Okay, I was curious. I'm like, wait, this sounds incredibly familiar. So you made the enormously expensive ridiculous oversized hard to use thing. And then simply let that train the smaller model, which is cheaper, faster, easier to run. And therefore, it's a better fit for a GPU cluster. You might find at a bank versus one at say, an Azure data. So that means all of the compute that the on-prem models eating up, the cost is actually not even material, right? It's so small because if you think about it, I hate to demystify this for you. But an LLM model is just a CSV file with weights, right? To get those weights, that's where the expense of training comes in. And then there's some software to then run those weights, right? Predict the next series of words and a sentence to get the query, right? So, at the end of the day, that's what we need to run when we're deploying. And so that sister model is very lightweight. It's completely on-prem within their control. And so that gives us the flexibility to then have that within their own first structure. And this allows them to do two things as far as I can tell. One is you have an enterprise search function. Essentially an AI powered search product lets people go into their own data and pull things out. Some companies are working on this. I think Glean is doing this for the generic enterprise. I've actually used that once, add a company that bought it and it was medium. Maybe it's gotten better, sorry Glean, that wasn't very nice. But I was not blown away by it. And then the other thing you're building is a thing called Abby ABBI, which is an AI agent as far as I can tell that you have tuned to work in a Finnserve call center-like environment. Exactly right. So if you think about Abby, our company is called ABBI, but some of the first clients that kept referring to ABBI, and so we adopted that name Abby, so we call it Abby Assist. All right. If you're shipping a product or rolling out an update, You're building a company. You need to be organized, right? We know that. Atlassian has exactly what you need to streamline your work and smash your goals. And the Atlassian for Startup program is packed with all the tools you need, like JIRA, where you can track every task, sprint and bug. That's the industry standard, confidence, another industry standard for team collaboration and documentation. And of course, LUM for quick video explainer creation. Now LUM is really brilliant. My team started using LUM on their own. They started paying for it on their own. Why they wanted to get credit on the investment team for communicating to me, the general partner of the firm, why they wanted to invest in a company. So they would do a LUM where they recorded over a recording of an interview they did with a founder or visiting their website and going through why they want to invest in a company. And this was so great for me. I would be skiing in Japan. I would be on a flight to New York to see my parents and all of a sudden I get a notification for my team members, hey, watch this LUM and I get the link for the LUM. I click it and then I can put comments at any time. So it's like doing a conference call, but on my time asynchronously and I can communicate right there on the video also included in that Lassian for Startups is Compass, Jura product discovery, Bitbucket, so much more all powered by Lassian intelligence. That's their built in AI that Lassian software helps companies like Canva, Cloud Flare, and Rivine keep growing and keep innovating whether your brainstorming on sticky notes or scaling to the big leads at Lassian is here to accelerate your startups growth. Check out the last time for startups where eligible startups get up to 50 seats for free for one full year. That is absurdly generous. Why can they be so generous at Lassian because they're the standard at Lassian is the standard and they are generous to startups because they were once a startup. I remember meeting them 20 years ago in Australia. What a great company. Head to at lassian comm slash startups slash twist for complete details and ABS is the sort of that front end chat beats he like interface that bank credit users ensures use the answer questions. And this is the kind of thing that, you know, if you're thinking about servicing a client or a customer, you're on the phone with a customer, you're in front of a customer and you're trying to get information to them. So there's the Abby agent, which, you know, allows the folks internally to look up information and Abby agent has a whole bunch of things, you know, sink to it. So they have compliance guard. Then we have chain of validation. We could talk about those. But then the piece that you mentioned that's really important is the indexer. So I spend eight months of my life building software that just connect one piece of data to the next. It's very painful. I don't want to relive that, but that was my life. Ray month. And so we connect to a lot of data sources, but what's really important is we connect to the really hard one. But you mentioned Glean. Glean's a great company. You know, they connect to a bunch of different data sources, but the ones we have to connect you for our industry are things like Fyseur and Symitar. Very complicated core systems that require due diligence and compliance checks and RN. So I presume unlike linking into Slack, there's not an amazing API perfectly built just for you to drive developer adopt them. Exactly right. So we help to solve that problem, save the insecurity while connecting those core systems. The piece about our indexer that's really important is decentralized, right? So most of our competitors, Alex are going to say, Hey, we have this AI assistant. Take all of your documents and upload it to this one location and we'll index it. And then we'll give you the answer. And if you're an enterprise, that's just not realistic, Alex, right? So the what's unique about our indexer is that it goes out where the information lives today indexes that data and brings it together. So there's no internal alignment meetings or changes in processes and procedures. That makes sense. Yeah, but the indexer just make sure that I'm fully tracking here because we're talking about on prem is running inside of the bank credit union or insurance company's own systems. So you hand them this piece of software and then they go off and run it on. Okay, this is making sense to me, but it all stays inside. It never breaks the firewall containment. That's right. The indexer is completely inside their infrastructure. The on prem model is on prem, completely in their infrastructure. So they really have that peace of mind and sense of security when using advocates, the product. Yeah. Now, when I was prepping for this chat because I love to dig into how people actually run their service, you have a section in the Advocates OS, LLM model, part of the site, discussing how you can run it on a handful of H100 GPUs or I think also A 100s. Yes. Yep. Reading this, talking mostly to technology founders, getting access to a small allocation of GPUs is not an impossibility. Yep. And the know how to set them up and properly use them also not an impossibility. When I think to my childhood credit union, which I great lollipops, by the way, love that. So, we still remember that. I remember like, I remember where was my hometown for some reason. I don't think they have the juice to get a GPU rack going. So my concern is this sounds awesome, but it also sounds like something that only applies to the largest banks, credit unions and insurance companies. Because to me, small people, I probably just don't have the gear for it. Is that right or am I being too pessimistic in behind the times? Alex, this is why I love you because your questions are right on. So your answer is correct, but I want you to inverse it. So if you're a big banks thing, Chase or Wells Fargo, you fill in the blank, right? Those guys have the money, the talent, the infrastructure to go ahead and then get the best, you know, infrastructure that the money can buy. But if you're a credit union or a mid to your bank, you don't have the talent or the infrastructure to do it. That's why they need a solution like Abacus, right? So even if they, you know, figure it out a way to do it, it's not a simple plug-and-play press button that deploys to their infrastructure and they're good to go, right? There's a whole bunch of maintenance and compliance things around that. So you're absolutely spot on about, you know, there is a lift, a technological lift to get people there. And that's why, you know, a lot of the folks mid to your credit unions and banks that need that support, that's why they turn to Abacus. What is the deposit base of a mid to your credit union? I have no idea what the tick marks are on the access we're describing here. Yeah. Another fantastic question. So I would say the sweet spot is between a billion and three billion dollars in total assets, which if you're at JPMorgan Wells Fargo, it probably sounds adorable, right? Maybe a little baby bank. Yeah. But, but those are the folks that really they have the money, but they, but they also, you know, they have the infrastructure, you know, to think about doing something like this, but they don't have necessarily the technological capability or the staff or human capital. Go ahead and deploy it. The net interest margin on the safe three billion, that's called it, it's credit union. So it's a little bit lower because they're better to you than banks are, call it two percent. What is that 60 60 60 million a year? So they probably have like a million dollar a year IT budget give or take. So that does put a cap on your ACV for that type of client, but it still look very attractive business. So how hard is it to land those mid tier credit unions and banks and bring them into the abacus fold or sorry, the the Abby fold? Yeah. So it's a good question. And this is where if you haven't already Alex, you're going to be like, well, this guy's insane. So I learned early on that, you know, if you're a customer of these banks, you know, because people always ask me, David, how did you get these clients? How did you get your clients? Is it very hard industry? Did you open your accounts at all of them? You are there you go. So I'm probably on some, I'm probably on some government lists, right? The being watched by the feds because I probably have the most open bank accounts of any person in the US. So I would open a bank account deposit of the five dollars and I email the CEO and I'd say, hey, I'm a customer and I promise you, if you're found you're listening, that tactic works like 95% of the time. If you email them and say, your vendor, you almost never get a response because they get those emails all day long. But if you say I'm a customer, it's like a 95% response rate. So that's how I would, you know, sort of get my foot in the door, which I know sounds insane and it is insane. I don't recommend it for everyone. But, you know, that's kind of my hacky way of going about it. And then the second way Alex that we get them to, you know, use advocates is, you know, I tell them and I'm going somewhere with this. I don't want them to use advocates unless they absolutely love it. Right. I want them to completely love the product. And I want it to do everything that we've told them it does. So we give them three months, money back guarantee, right? Let them try the product. You know, it's not something I want them to use if it's not something that they absolutely love to use. And it's, you know, we take this as a matter of personal pride, company pride, you know, we want our products to be tremendously useful in, in a, of, tremendous quality, tremendous quality. So, you know, that's how you know, we're able to sell ourselves. It's not just, hey, you know, we're customer of yours or we have a strong interest that you're doing well. But, you know, our product is here to help you and we're going to guarantee it does that. From a business perspective, I can kind of see this two ways. On one hand, if you offer a three month money back guarantee, people will take a greater risk on trying you out because the bar entry is effectively lowered. Right. And also you by giving them this out effectively, say, we're going to let all of our problem customers go away immediately. And if we're not a fit, we're not going to try to fight for retention here because why bother? On the other hand, a lot of companies don't like to let revenue go away. So, I guess for founders out there, this sounds like a short term risk, long term benefit set up. Is that fair? Exactly right. So, what I like to tell all of the out of the folks, everyone that we work with and all of our employees is the answer is never know. It's just not now. Yeah. We are here to serve our clients and our customers and that's our focus is making sure we're delivering a product that it's really what they want. And while it seems like an inherent risk, gosh, you're going to give away three months for free. You know, I think that's the ultimate test. If your product is what you said it is and it's a quality piece of software, I don't think you have anything to worry about giving it three months. 2025 is going. to be the year that voice AI really breaks out. Voice AI is going to change everything. And this technology is perfect for building a chatbot or automating customer calls. And basically creating these next-gen voice experiences. But building with these tools is so difficult. It's just a bear. It's going to take your developers months to build and test anything even basic, unless you use VAPI. VAPI, this is the fastest and easiest way to integrate voice into your products. It's just the most simple API you've ever used. It helps you build, test, and deploy voice agents in just minutes. And you can create ultra-realistic AI voices and customizable personalities. And like that part, all with real-time response speeds. You know what'd be great? Maybe I just demo it. Listen to this. It seems like VAPI has figured out how to be very low latency. Maybe you could talk a little bit about why that's important. For sure. Low latency is crucial for a smooth user experience. It makes conversations feel more natural and engaging. 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So I know it's adorable because we have nine paying enterprise clients, so that sounds adorable. But for us, we've never had a conversion, not go well. Nine enterprise customers for a company of the age of advocates because it's only a couple of years old, right? That's right. That's right. Yeah. So stop talking yourself down. That's fantastic. There's unicorns out there right now who are like, we would kill for nine customers. So I think you're doing fantastic. Now, what is the ACV here? We're talking about, you know, fencer, we're talking about self-hosting, talking about data fine tuning. There might be some handholding there. I'm presuming this is not $20 a month. No. So, you know, what's our pricing schedule look like? How do we price our product? Right? We have both a monthly subscription and one-time installation. Now, it based on asset. So our monthly fee is for anywhere from 10 to 15,000 per month. And then the one-time installation is 250 to 350,000. It's a one-time payment and it's usually amortized over the like contract. Average contract is roughly three years. Okay. So I'm looking at about a hundred a year for the spread out installation cost and that I'm paying some number of bips off AUM for the rest of it. That's right. That's right. So we usually get the installation fee. It depends on the client. Sometimes it's paid up front. Sometimes it's amortized. But, you know, I would say 15,000 is a surprisingly more common than I mentioned that range. And we normally it's inch toward the 15,000 per month. How much capital would you need to raise to be able to just wave the installation fee and just go straight for it and just go faster and land even more enterprise accounts by getting rid of that that roadblock? Everyone has to give this question. So the installation fee is actually okay. Tell me why. I want you to think of the monthly the monthly fee as mom in the setup. One time installation fee is dad. Dad's grumpy and he has really he's really strict and mom is really nice and wants to give you everything she can. Right. Okay. If you play those off of each other, it allows us to be flexible with our pricing and allows us to negotiate. Right. So if you have two the numbers, one's a monthly fee and the other is a really high number seems, you know, very large. And all of a sudden you bring that number way down all of a sudden the customer, right. These are tremendous amount of value and a good will toward them as a client, right. So it's a very strategic value proposition to have both if you just have one number. Hey, this is our monthly fee. I only have one number to negotiate with. But if you have two, you can play them off each other and we typically do. So everyone says that to me, Hey, why the installation fee that's really high. My answer to that is it gives us a leverage in negotiating power when we're talking to clients and it allows us to, you know, meet their level of budgetary occupations if that makes sense. Yeah. And besides you care much more about the monthly than the one time exactly. One time is a sweetener for you guys. There you go, Alex. So it's it really we're about the monthly. So the one time gives us flexibility in our pricing in a way that we wouldn't have otherwise. Honestly, I mean, like pigs get fat hogs get slaughtered. But like if you can, if you can go ahead and get paid twice, how's that? I mean, boy, I, I'm not going to stare gross profit in the face and say no to it. But I just realized something interesting about your 15k month price point. Because this is this is on-prem. Yeah. You're not eating enormous public and for cloud costs. So your cogs must be bought, your cogs must be low and your gross margin must be just ludicrously lovely. Exactly right. So we're not paying an open AI or anything on a per query basis, which is why our fees can be fixed, right? We can say, okay, that the contract 10,000 or 12,000 or 15,000 a month, they banks and credit unions like that. They do not like annuities that are variable. They will shut that down every time. They want a fixed price. Yeah. And so they can ask 10,000 queries a month or a million queries a month. It doesn't matter, right? The price never changes. So we can undercut a lot of our competition on price. First of all, when it comes to these sorts of Asians and AI deployments. And then secondly, as you mentioned, the one time fee that we're charging, because everything is on-prem, everything is built in house. So we can really price it in a way that makes sense for the client. Has anyone ever come to you and said, listen, we want you. We want to be on-prem. We don't know how to set up our own racks. You were like God and the screwdriver out and put the GPUs together for someone else to run the model on their own metal. Yeah. It's funny. You mentioned that. So a lot of credit unions and banks, maybe even the one you mentioned top of the call from your hometown, they actually have a server room, right? Actually in the building, right? And so we've actually gone to physical server rooms before, spent a few days helping them install advocates. So maybe not quite a screwdriver, but we have that in the room and the vicinity of the actual servers that run the computational data for the bank of the credit unions. So we're not afraid to be there on site if we have to. I'm now friendly googling credit unions, Corvallis Oregon trying to figure out which one it was. Yeah, but it's the one over by my old dentist. You know the place, right? You've been there. Of course. Yeah. Yeah. 100%. I want to drill down on some of the the tech things and get away from the money side for a minute. Response control, hallucination control. Clearly when you're talking about regular industries, can't be sped in our BS, how did you guys actually go about combating hallucinations? Because I think that most models have made progress here. I think there's a good trajectory, but I wouldn't say it's something that's been resolved. And so I'm kind of curious, what was your approach? Yeah. So we have what we call chain of validation, right? And it's a three tiered system, Alex. And that really helps us ensure that abacus is always giving the right answers. So as you know, with LLM, hallucinations, big problem. How do we make sure that the answer is that an LLM gives is accurate and grounded in real facts and data? Our chain of validation has three steps. The first is triangulated retrieval, right? So abacus will look up. So say you ask a question like, how much is a leak? Right? And abacus gives the answer. A late fee is $25. You will look to cross reference three different sources of information to validate that a late fee is actually $25. So she'll look at their online website, make sure that the fee listed on the website is $25. You will look at their truth and lending disclosure to make sure the disclosure says $25. And then she'll look at their internal policy documents to say that shows that the late fee is $25. When all three of the-- And she here is abby, the age. That's right. That's right. Abby the agent. And so if all three of those sources match, the data passes the first step in chain of validation, which is triangulated retrieval. Then there's a second step in chain of validation, which is claim decomposition engine, right? So we take the logical structure of a claim and we decompose it, and make sure that that's valid with the fact that let's take our example. A late fee costs $25. We're talking about a thing, a late fee. We're talking about a fee. And then we're talking about amount, right? We take the thing, the fee, the amount is the logical claim that we're trying to validate. And we validate that claim against the information. So with an LLN, they give a lot of, very colorful answers, right? Answers that are meant to sound very human-like in quality. We're extracting just the logical claim that's being made. The thing is saying cost this much. Is that true? Customers are allowed to sign into online banking and set up alerts. Are customers allowed to sign in online banking? And once they're in there, are they allowed to set up alerts? That's a logical statement, right? That we're trying to validate. Okay. Why wouldn't you do that before you did the triangulation of the $25 price point for this late fee that we're discussing? Because to me, it sounds like you would want to get to the absolute nuts and bolts of the logical progression and then check that against the facts. Yeah, it's a good question because if you check the logical progression of a statement, right, and it is valid, but the validity. So if I said, for example, all hats. our dogs. That is a valid logical statement. It's not a true logical statement, but it's valid. Right. All right. Everyone knows that CRM isn't just software. It's basically the heartbeat of your business, but it can get ugly quick if your data isn't organized and you're dealing with a messy tech stack. That's why I love HubSpot for startups. It's the all-in-one customer platform so you don't need a frank inside of goals. No. Right now, early stage companies are going to get 75% off and with this one system, you're going to automate marketing and actually converts. Track yourselves pipeline without spreadsheet chaos and you're going to manage your customers like the Amman Hotel, six stars all the way. You're going to get investor-ready analytics that tell your story perfectly in man. When you pull up HubSpot and you get those metrics, you got those analytics. Things are going to go really faster for you as a startup with potential investors. Plus, you're plugged into an amazing community of founders who've already tackled what's ahead. They've been around those sharp turns and they can tell you how to navigate them. HubSpot was built by scrappy founders. I know them and they understand every dollar counts. That's why hundreds, thousands of startups trust HubSpot to scale their businesses. Here's an amazing call to action. So generous from my friends at HubSpot, 75% off. That's right. 75, not 7% off, not 5% off, 75% off, HubSpot for startups. You need to get three months of perplexity AI for free. That's a great pot sweetener. Head to HubSpot.com/darded. We're not just checking the validity of the logical statement. We're also checking the truthfulness of the statement. So the first is we want to make sure that the information going into that logical statement is true. Once we validate that it's true, then we validate the actual logical claim. It is the claim valid. If something were to say a late fee is negative $100, that's not a logical claim. That's not a valid logical claim. We know immediately something's wrong. That's why we do it in that order. Then we have the third piece, which is what we call fact-packing. Every single answer Abby gives comes with the actual document that provided the answer. We are in the document that the answer came from in a full-site patient. So document ID, paragraph level hash. So if that person using Abigail wants to see, okay, Abigail's saying it's $25. I just want to make sure that's right. For my own sanity check, they can click to see the actual physical document. This is where it came from. This is the exact paragraph. So they can go, "Oh, yeah, it's $25. There. I know the answer is right." Is the person here that's checking this the end user or the company itself who's vetting their own system? The end user. So we empower all of the end users, no matter if they're a teller or an executive vice president. If they receive any answer that Abigail's gives and they want to make sure that that answer checks out. It sounds about right. I don't know about that. Is it really $25? I thought we changed it to $35 last month. They can click on the actual answer, right? It will show them, "Oh, yeah. In policy 5, 6, 8," or it talks about late fees in this paragraph. I can see right here Abby highlighted exactly where it came from. Can I just say that if you're a credit union, $35. Late fee, get a new one. That's what is the city bank? Who I hate. Yeah, no credit unions are really good about late fees. I'm just making up numbers here with eight. I know. I'm just really fantastic. Yeah, I know, but I'll agree with you. Late fees can get completely out of control. I'm curious with this three-part system, do you get hallucinations down to zero, down to a diminimous level, or down to a merely acceptable but still improving level? We have a 98% accuracy rate because of this system. Anything that the system flags, it kicks out, right? Because we train on financial data, we know the kinds of questions they're asking. We can be very, very precise. We have 98% accuracy rating. hallucinations are incredibly rare if they happen. We're able to get it down to a level so low that they're not material. That's where the trust comes in. Once clients learn to trust Abby, you know, skies the limit as to what you can do. That's amazing. I'm so happy that we've gone from, attention is all you need to chat GPT to this level of deep vertical integration. Less than a decade. I mean, that's why technology is awesome. I do hate that I feel so incredibly behind every single day because I feel like I'm sticking my head into a fire hose of news. But then you see where the rubber meets the rodent is pretty exciting. This is actually going to make smaller financial institutions more competitive, more efficient, and just more viable. So that brings me joy. David, now about your company, though, how fast are you growing? We've grown tremendously fast. We have more interest than we're able to service right now. So we're really trying to move the needle on our fundraising around and build up some infrastructure here so we can do that. Just it last year alone, we went from 25,000 in ARR to 125, excuse me, MRR, 25,000, MRR to 125,000 in MRR by the end of year. So we have grown incredibly fast so far. When I think about venture capital, I know this is this big in startups, not this big in venture capital, but it often feels like the same thing. A lot of founders need to raise money because they need to build, they need to reach a certain milestone, hire certain people, buy a certain technology or whatever. And so it makes a lot of sense to have risky capital in there. But going from 25,000 to MRR to North of a million ARR in a year, I mean, it feels like you rely on so much more income to play with that you're probably not as capital constrained as your average Joe in the startup game. So why is it the right move for you guys to go out and try to raise more money versus just self funding because it feels like you have a fork in the road here, if you will. You're right. When I started the business, I started the business and I wanted to run it like a real business. We're a cashful positive. We don't have a burden rate because that's, I'm from Chicago, I'm from the Midwest. I'm not one of these fancy San Francisco boys. So I started it the old fashioned way, my business and worked at it slowly as we've made progress. To me, making money was the definition of, okay, now I have a business, we have net income. So you're right. We do have capital, we do have money coming in the door. So why venture capital? And the answer to Alex is as much as I hate to say this, to win in this space, you have to be first, right? The company that these credit unions are banks, they're insurance companies adopt first is going to win because once they're in there, they're very hesitant to change, right? Some of these banks and unions have systems from the 80s and they don't want to change it because it works. So the same thing is going to be true of whatever AI system they adopt. So we need to get there first. To get there first, we need a large and fugitive cash so that we can move very, very quickly. Make sure that we're capturing as much real estate as possible, you know, as the AI space is building out and companies are starting to adopt this technology. And so that's why the venture capital coming in. So the gates open, you're running across the field to get the best spots at the concert and you want to run even faster to beat all the nerds out. So you raise money, okay, I'm here for that. But if you do end up raising someone going out there and then going back to profitability, well, that would make my Chicago roots very happy to hear because I love net income. Those are my two favorite words in the English language. All right, David, before we go, one, what's the website and two, what is a role that you're hiring for? You're having a hard time landing the candidate. Yeah, so go avicist.co is the website. And then a role that we're hiring right now, sales, sales, sales sales and we need people that have expertise in that industry. So banking insurance credit unions. So those are the roles we're really looking to to fill right now. So if there's anyone out there listening that's interested, please let me know and go to the website and hit contact us. But we're looking for people with that industry experience. And that sort of sales point of view or disposition. So all my cool sales people out there that know those industries, let me know if you're interested. All right, well, David, thank you so much. And when you, I don't know, whatever your next milestone is, two millionaire or whatever it is, I'd love to have you back on to learn more about it because companies that grow as fast as yours are are onto something. So in the meantime, good luck. Enjoy the Chicago summer. It'll be tank top season for a solid three months before it freezes again. Well, thank you, Alex. Thank you very much. I told you that was going to be fun. But let's take it one step further. Let's go talk to it. Synthesia, not synpesia. Come on, get it right people. And let's learn more about AI generated avatars training, videos, video models and all that good stuff. Let's go. We have been talking about AI so much on the show I'm sure you want us to be quiet about it. Who wants to see more text generated by robot? Well, good news today. We are going to stay on AI, but we're going to move away from the written word and instead focus a little bit more on video. Now regular viewers of the show will recall that some time ago, I heard tell of a new product called Synthesia and I wanted to play with it. So I made a video brought it to the show showed it off the Jason. Well, today we are bringing the company on the show to talk about what they're building because I have added them to the twist 500 partially because they raised a big round earlier this year, but partially because they're growing very, very quickly. And we'll get to that. So please welcome to the show. It's Victor, Riverbelly, the CEO and co-founder of Synthesia. Hey man, good to be here. Thank you for being here. After folks who are watching the video, it's actually quite late over in London where he is. He's been very generous with his time. But first, ballast, start right there building an AI company in the UK. I've been told Victor that if you're not building within three blocks of one part of San Francisco, you're doomed and yet you're absolutely not. So what's it like to build an AI company over in Europe? There's pros and cons. I think if you weigh up everything at once, it's probably still better to build an asset for the US. But there's a lot of benefits actually for building in Europe. One of them I think is actually talent, right? So the product rise of talent in the Bay Area is extremely high compared to the rest of the world. And it's not just about the cost, actually, I think there's also a loyalty element to this, which has two sides to it. In Bay Area, most people are building their own personal stock portfolio, and they're very quick to jump ship if you miss a couple of quarters, right? In the year of the start of a different way you relate to your work, it's less transactional in sense. People care about the mission, the company they work in. They don't cast much about stock options, where it definitely has its downsides as well. But my general sense is that people list jump in Europe, and I think that doesn't cost money official if you're the father of a company, right? When we start the company back in 2017, the capital market is a lot neilier as global as they are today. I think that has kind of equalized today more or less. I don't think it's particularly harder to waste money in Europe than in the US. And you're less close to the ecosystem. I think that matters a lot if you're building your customers or other tech companies. And that's one of the things that we are. There's not really a thing for us. There's not even a top five industry for us. But obviously, if you're building a developer tooling, you probably want to be in the area that's just proximity to other tech companies who are all spending your customers is much better. But I think building in Europe, building in the UK is getting better and better and better. There's also the benefit of a company like us, one of the companies in Europe that do really well. If you're a great European engineer, there isn't that much choice. Right? There's like a thousand cool startups, a lot of money, and do very interesting things. I'm pros and cons, but overall, I think Europe and the UK is actually become a pretty good place to build. I don't tell anybody, but I've actually scooted the UK back into the EU mentally. I'm just preparing for a post-Brexit feature. So we'll see how long that takes. But I think we can see the writing on the wall. Okay, Victor, what I did there was actually not start with what the hell you're building. So it's a synthesis. In my understanding is a tool that I can use to create essentially AI avatars in videos. These are aimed not at the consumer use case. I'm not making a 30 second clip of Gandalf walking around with Frodo, but instead I'm creating video materials for a corporate environment. So one, how close was that? And two, narrow it down for me. Not bad, but I think a lot of people have this perception that we're the AI avatar company and that's definitely the way we started. That's started, but that's kind of how we initially went to market and hit part market feet full of five years ago in 2020. I think by now we're much more than that avatars is a feature in our platform, but really what we are is an AI video platform for the enterprise. We cover the entire lifecycle of the video. We hope you create the content which starts with the AI models. The avatar is right which replace the need for a camera, but it's also a fully fledged video editor, sort of like using camera powerpoint. It's a modern collaborative platform that can support enterprise with thousands of people creating things simultaneously, organizing themselves around that. It's a content management system for translation, updating, versioning. It's a publishing platform where our own AI video player that's built to show the videos you make in Tunisia and deliver you insights and analytics back. So really it's about the entire value chain. It's not just about creating clips of avatars, even though that's where we started. The right of the use cases we're not targeting, entertainment, without targeting, and advertisements really. And the best way of thinking about Tunisia and the market to serve today is people today want to watch and listen to content that don't want to read that much anymore, right? In our private lives, everyone I think will agree with that. We're recording a podcast with a video right now and most of them prefer to consume it for making it this way. And that's because we're free choice. That's what we do. But when we go to work, right, we don't have free choice most of the time. You have to basically consume a lot of text, a lot of emails, a lot of slides. And that's just a much worse way of communicating to your customers and please your partners. That could be anything product marketing, to customer support, to internal trainings. It's a much worse format if you're using text. If you're communicating with those efficiency, you need video. And what we've built is a platform where enterprises can essentially use our tooling to communicate in the most effective way, which is with video. But at the speed and scale of text, right? You don't have to use cameras. You don't have to use ad, it has to be accurate. It's going to be great at video editing. It also comes out the box in a PowerPoint style experience. And the markets we serve five years ago, we launched the first iteration of the platform. It was very much internal focused, learning and development, workout training, or introduce cases. Because as the other time, voice technology gets better and better, you unlock more and more 10. So today it's 52% of all videos generated that are external faces, which is like customers for. Oh, so over half of the product marketing. It is over half now, right? And a lot of that is like product marketing, for example. But it's not like the fancy meta ad that tracks you initially. It's the mid funnel content, right? It's like you're interested in a product, you go to the website and you want to learn how does my product compete against a competitor that can help you video instead of a long page of text. You're a consumer and you're trying to take out a mortgage used to be faced with a long page of text that very few consumers can sit down and comprehend. Now you can watch a five minute video instead. So it's not like the top level awareness stage of the funnel. It's much more in the kind of information sharing how to style of content that we found on the issue. Now, when I showed the demo, we used a more well of a woman sitting on a couch wearing a striped shirt. For example, that's kind of the thing you have on your front page. Can I use an easier to make a video or an AI digital twin of myself or am I always going to be working with a model that you guys have provided it and also I presume tuned and tweaked to fit your voice algorithms? Now, so you can make your own avatar, very popular feature on the platform one of those popular ones. It's super simple. You can use your webcam. It takes five minutes. You basically just, you know, recite a script that we give you. You can also, if you want higher quality, you can go into a studio. You can record it with a camera. Essentially, like the quality you input to the system is the quality that you get out. And I mean, these technologies are magical now, right? I mean, if you try killing your voice at some part in time, I have it yet because I think my voice is very annoying and I wouldn't want to listen to it more. So it actually hasn't occurred to me. But I hope that with that, yeah. Yeah. You can speak, you know, we can speak 30 languages at the click of a button. And it is pretty magical to listen to your own, like actual tone of voice, be in Spanish German Italian, whatever language you want to speak. That actually, that is freaking cool. And I presume because we're talking about multiple languages here that the service itself does support much more than just English. Okay. We support 140 different languages, which is also a key part of our value proposition, especially for global companies, right? We need to communicate across borders all the time. So let's talk just a little bit about training data because you guys recently announced a deal with shutter stock to ingest some of their visual information to help build out your expressed two model. But when I'm thinking about how to nail an Irish accent, I mean, is there a repository? You can go out there and like train against you to collect all that yourself. How did you go about supporting that many different languages and speaking styles? So that particular part is separate from the deal with the shutter stock, right? And if we start to count the video side of things, which is really where shutter stock is, is kind of relevant there. Basically, what's happening in the world of AI is probably no, right? The models get bigger and bigger and bigger and bigger and they get more and more generalized pooled. Elements are kind of like the first iteration of this where we built something which was essentially high generalized pool. I could just do a lot of different things. It wasn't like a model that was like really good at doing that just one thing. In the world of a video and avatars, if you look at what the product is capable of today, it's essentially people talking to the camera and the quality has gotten like really high. It's kind of edging on looking like a real video essentially, but there's a lot of more things you want to avatars do. Avatars that can laugh, avatars that can cry, avatars that really use their body language in the correct manner. So when you look at me speak right now, I use my hands, right? There's a beat to what I'm saying. Avatars, I'm not there yet. There will be with these new flagship models. And essentially what you want is our AI models that centers on human speaking. We want them to see lots of different scenarios, lots of different types of people interacting in many, many different ways. That requires lots of training data, right? So we both spent a lot of money on procuring our own data sets working with actors around the world and studios, 3D data and so on. But it's also really helpful to see kind of a glimpse of the world in shutter stock, which has like a lot of content on their platform. So much. Okay, so it sounds like this is very, very hard to do well. So you're using both data from other people getting your own data from actors and voice, voice and motion capture and so forth. How much better will express to the model I believe you're currently training right now be compared to what I can currently see via the Synthesia website? It'll be a lot better. I think what we're getting to now is maybe helpful just to outline how you become Avatars based in an AI video in general. So I think this technology started five years ago with the first ones to launch the popular. Back in the launch, the first it's raised off them. They're pretty crap to be honest, right? The voices sounded like you're kind of talking to a GPS and the video was kind of still to it, but it was kind of like good enough for some use cases. And we were some of the first to discover what the use case were and then we're definitely not super bowl ads at all, right? But for internal training videos with the alternative was asking people to read 10 pages of PDF documents. These sort of prebi Avatars action was a much better experience, right? Now what has happened since then is that quality has gotten better and better and better. But we're still in the real of what I would call educational how to very kind of you to the Terry and practical content, right? It's about like me delivering some information to you in the most of the way. It's not yet about storytelling. Storytelling to me is making you feel something, making you laugh, making you sad, making you happy, right? That's what a real actor does. That's what you look like a great app, right? It makes you feel something. That's what you see in entertainment content. And I think with these new models, we'll actually begin to cross into that because what's going to happen is that the Avatars can begin to perform their lines like a real actor. When I speak to you right now, my voice kind of slows down and speeds up. I emphasize a specific word. I use my hands to underline something. That's how we speak as humans. That's what's most natural to us. And when we can teach these avatars to do that, we are going to be able to create content that's going to be even closer to the real thing. And it's going to have all the emotions that are next-precipable. You would have from a real actor. So I think with these new models, I think we'll break through into storytelling. We'll begin to see the first iterations of creating content that's not just meant to be like in form, but also kind of like entertain storytelling and make you feel something. And I think that's going to be a pretty interesting watershed moment. We've seen that in other modalities, something like just if you just stick with just the voice part. And essentially before a couple of years ago, voice technology was only used for assistance in your phone and for GPS, right? You would never listen to an audio book that was done with an AI voice. It was just way too bad. It was terrible. Now, now you have companies that basically take real books internally. And it's an audio book and it's a great listening experience because all the emotional activities conveyed. We haven't had that moment for video yet. And I think that will be a pretty big watershed moment that just on up so many use cases. So I think it's pretty exciting. So what I just heard is that when the Express 2 model comes out from Santhesia, we're going to cross over to almost a new generation of AI video quality. It's going to unlock a lot of other use cases both in the corporate world. And I presume also at some point in time in the consumer domain as well. Absolutely. Yeah. I'm going to go off topic here and talk about where I see this going. And I'll bring us back to the corporate world and how you're doing and so forth. But I recently was playing with open AI's voice mode, just chatting with my AI. And we had a talk. I gave it a name. It was kind of weird. I almost felt like I was like crossing some sort of like ethical boundary. We were just talking about like the stock market. But it still felt different in the interest. Anyway, almost like I was talking to a person even though I knew better. If I take that moment of like, oh wow, this is actually really good. And apply it to what you just told me about what you're going to pull off with the Express 2 model video. I mean, how long until I have like my AI is not only persistent in terms of memory and knowing me, but also has like a look to them that I would expect to persist across my digital environments. It feels like to meet your building the real front end for AI here in a way that other people haven't yet. So to me, what you're describing feels like it opens up a canary and explosion of possibilities versus just making middle funnel corporate materials that much more beautiful. For sure. I mean, I think we're so early in all this technology and that's to your point, right? I mean, these things would be real time. They'll be different. They'll be 10 times what it is today. And you will be at some point, right? We have like a eyes that can tell joke, you'll laugh at, which sounds weird. We may even at one point have you coming home after a long day of work and sitting down in your couch. And instead of turning on Netflix to entertain yourself, you may begin talking to an avatar or go through some interactive kind of experience, right? Oh, five to imagine. Five years. Yeah, I mean, maybe even before that, I think, but definitely in five, five, 10 years, you know, I think this is going to be completely normal part of living lives. And it will be, it'll be weird. There'll be lots of challenges. But ultimately, I think what this promises right is that the interactions we have with our computers are going to be much more natural the way our brains are wired, process information, right? I think I'm using a keyboard and a mouse. It's natural to all of us today because we've, you know, we've kind of grew up with it. But everything really is approximately for how we actually prefer to interact with each other in the real world, right? Which is we talk to each other, we see emotions, we use our hands, we show people things. I think we can just, we can get much closer to that. And it's not like you had the same experience, but the first time we tried something like opening it as well, right? It is kind of like a holy shit kind of moment where like you almost cannot believe it's real. But like the first time you use chat, you could see right? And it's truly magical technology. The flip side of that is it's also very interesting how quickly we just get really used to things, right? Like with chat, it's like match. If you took chat, you could see back like 40 years in time, you'd get burned at the state, right? Oh, absolutely. Because like someone invented, it's like insane. But now everyone's like, eh, it can like solve the entire, it can't solve all of my homework in one prompt, right? I have to do in two prompts. That's what I'm talking about. But this is a good trick. It's very quick to, yeah, but I mean, we get used to it quickly because our expectations rise so fast. So the first time that I saw a synthetic video, I thought to myself, this is 90% of what I could want. How dare they not give me a hundred? Which is an insane perspective to have given that, you know, not that long ago, this was, as you said earlier, poor voice, stupid video, kind of crappy, but definitely a great first step progress. But compared to today, I mean, it's mind blowing how much better with things are, and I almost feel like I should be more enthused, like I should be more excited today. But instead, tools become wrote very quickly when you actually go about using them. Exactly. And I think a great analogy here, right, is like visual effects and movies of computer games, right? Yes. And then you just get used to this stuff so quickly. It's also interesting, especially when you work with humans, which we do, right? That humans is the hardest thing to synthesize and make AI videos about, right? Because we're so sensitive to even, like, the smallest inconsistency despite, like, when you look at a human, there's, we have so many, like, micro interactions of micro movements that goes on all the time. And we're incredibly good at spotting if something is, like, just a little bit off, much better than if you're just making your video of, like, waves and the sea or something like that, where I think in a lot of AI models now, I came up with love to do that. And but it is a part, it's kind of a movie goal post, right? Which is, which is kind of, like, very fascinating. Okay. Let's bring it back to today. So actually, as this video comes out, we're recording this just a little bit early. You guys are announcing that you've reached 100 million in annual recurring revenue, one Congrats. And two, and this is a stack that actually blew me away more, is that 70% of the Fortune well, 100 are now customers and that's up from 40% about two years ago. You're getting pretty darn close to having every single of the largest companies out there a customer for you. So I'm curious, what can you tell me about what they're using Synthesia for today? Has the use case changed at all? Is it where it was, even though you're going into the large companies in the world or do you want something else out of you? It has definitely changed over the years, you know, and we're very fortunate to have worked with lots of our customers over many years and cutting into, you know, large and larger contracts because more and more people in those companies use Synthesia. And as I can't describe it a bit earlier, right? It still revolves mainly around, like, practical content and utilitarian content. It's about informing someone of something. It's not storytelling ads and those kind of things yet, right? But what we have seen is that a lot of our customers know that started working with us a couple of years ago. Maybe they started with doing, like, internal training and learning. But then since then, like, now they're using us, the customer support, they're using us for product marketing, they're using us for, you know, part-integrations and a whole bunch of other things. So I'm curious about your costs because the technology's awesome. You guys did just raise $180 million series D. I think you said it was a 2.1 billion dollar evaluation. That's a big chunk of capital. So I do you guys have very high compute costs? Is training very expensive or is that money more earmarked for people, marketing and so forth? It's always been very important to us to build a great business, not just in terms of top-line growth, but in terms of unique markets. So it's a really, really solid business, kind of like a marketing perspective, retention metrics and those kind of things, which is something I'm really proud of, almost more proud than the Hognon AR figure. Now that said, of course, we do spend a lot of money on training AR about it, right? It's important for us to stay the leader of the field, have the best models and so on and so forth. But I generally take a very practical approach to how we train models. You know, I don't want to just train models for the sake of training models. There's a lot of companies out there that start with the technology, right? So let's train an AI video model. They can do absolutely anything. If you want an AI video model, do absolutely anything, that's going to be a very big training one. It's going to be very expensive and your model is going to be kind of okayish at a lot of things. Yeah. But not really, really good at one thing. And for some use cases, that's the thing you want, right? And some of them are saying that that's not great. What we're doing though is we're focusing surely on humans to the camera. So that's a much more narrow domain than all the videos in the world. And that also means that we can, we can be smart around like how much training we need to do. We can work with open source models. We can add a lot of our own data, but of course, a ton of algorithms. So even though we do spend a lot of money on AI models, that's not the predominant reason for for for for raising this capital, right? That is pedomic going into head count, that all usual things you'll see in the SaaS company. And but I think it's kind of interesting how the magic profile of AI companies, right? It's like they look very different depending on what kind of model you are in general. It looks like it looks better if you're like workflow driven and use case driven as opposed to being like in the model layer. Yes. Because when you build a workflow on the platform around your model, right? You're not really charging for the model. You're charging for the workflow you're selling to a costless and for us, right? So that means is that to the point of it earlier, I was like, yes, the advantage is the feature in the products, but they are a part of the product, right? People pay us for it's not just avatar videos, they pay us for translation versioning, video editing, collaboration. They pay user video player and then it's sweet that comes with that. And I think that means you can build a different shape of business, of course, like being a foundation model company. And it's amazing like looking at OpenA and from all these guys, but they're going to be great companies. It's going to be very few of those. And I think if you're not going to be one of those like truly big winner takes all kind of companies, I think you want to be very smart about like what models you train and how much money you you pour into that, right? Because if you're pouring, if you sort of a $1 train model, and you still have the best model on the market, you're basically kind of screwed. So we think a lot of, we think backwards from the use case and from the workflows, and when we need to train all models, we do our own models to do that. If we need to integrate other models into our ecosystem to create the best experience for our customers, we'll do that. We're not religious about like having to do everything ourselves, right? And I think that's been a key part of our strategy for many years. Hey, if OpenAI and Thropic, Google, XAI, and Mistral, all want to spend $100 billion making something awesome and then beat each other up on pricing and offer it to me, cool, I'm in. Exactly. I'm happy as a cat because I don't have to lose all that money and I get the best stuff. So I do really appreciate that. Now, we talked about the Fortune 100, clearly biggest companies out there in the world. I'm curious about the next like 5,000. So are you guys seeing smaller, more mid-market companies have a similar appetite for what's enthusiastic offers, essentially, video AI technology? I mean, we have customers that are of all size as a part from just individuals all the way up to the world's absolute biggest companies. And we have a self-service product, we have a premium product, which I think you've interacted with yourself. So all that is of course, all the way important for us. But to my point earlier, around focus, we're building for the enterprise. And that means we're building for businesses. And that means we make a bunch of trade-offs that makes our product amazing for the enterprise and for bigger businesses. And maybe sometimes means that we're not always like priors that actually need to be like a small business. That said, we work with lots of small businesses, we love the product and we love working with them. And there's going to be, I think, tools like Centhesia. I think the best way of thinking about the ultimate term of something like what we're building right now is basically PowerPoint, right? Every office in the world, office worker in the world today creates PowerPoints. And in 10 years time, they're going to be creating videos instead of interacting videos. They're not going to be creating PowerPoints. I think that's for sure. And so our tool is enterprise-focused in terms of the company strategy. But they could probably in itself can be used by, by literally anyone. And of course, when you have a PLG-driven model like we have where you can go in and try out the product and sign up with credit card and go for it, you get amazing. It's just amazing. You get everyone in right. And everyone who wants to play an album that uses it can use it. It'll be interesting to see if the split between enterprise companies is using it for internal knowledge sharing and smaller companies using it for marketing, external facing creations. If that holds, as the model is improved over time or if it becomes much more blended, I presume it'll become much more the same across the size of customer. But the question I guess then is just how quickly? But I guess things are moving so fast in AI. Maybe, you know, soon, I guess is my answer. Do that. Everything feels like it's about six to 18 months away. I think there'll be different-- Where are we going? It's going nuts. It is going nuts. But I think also, I think what you'll find is if you think of like the total market for this as being any communication touch point. If it's takes the PowerPoint of video ever as the market that we're targeting, right? I think the shape of the different types of markets will be different. So for example, an internal facing communication, you'll have a lot of videos that all be very different because they're all talking about some different topic, right? Which makes sense, right? You're teaching your partner how to do things, teaching your customers how to use the product. There's just so much communication that happens in an enterprise and with where they're kind of close to stakeholders. Now, I just take advertisement for a shot, right? That's part of what we're going to be different from the shape. That's part of what I'm going to be more-- I'm going to spend a lot of time on this one video, and I'm going to make it really good. And then I'm going to iterate 10,000 variations of that video. I'll change out the avatar to be slightly older, slightly younger. I'll change the hook. I'll change the way the product is positioned in there. Because that's a conversion rate optimization game, essentially, which is what you're doing on meta, app work, et cetera. So I think that's going to be more about like, you create a couple of great base ads. And then the system creates many, many, many different durations of that to figure out which ones works the best. And this is actually exactly what has happened today, especially in text, right? With app works today, you don't. When I was a teenager, I did app works. You would have to write all the different app works iterations yourself. What you do with app works today, you can't just give it a theme. It's like, this is my product. It does x, y, c. This is my competitors, whatever. And then app works just automatically generates, I mean, thousands or hundreds of thousands of different variations to figure out which ones performs the best, right? Now, that's not possible video today, because we can't really programmably create video at that level of scale. But that is going to be possible very, very soon, right? So I think it's, it's got very interesting to see the difference between this kind of algorithmic creation of video for conversion rate optimization. And on the other hand, we'll probably have more kind of power pile style, power upon start creation inside companies, where you'll still want to have like an onboarding video for the sales team. You'll have like a specific video explaining how partners would integrate with your platform and the pricing policy and all those different sorts of things. We're just still very early, but there'll be so many different shapes of products. They'll, they'll, they'll, they'll, you know, comprise and this equals to this is mind blowing, because I just realized that eventually, you know, run the tape out far enough. Instead of having text ads that are targeted for my demographics, my age, my location, my education level, whatever, it's going to be like, here's the best video avatar to sell X to Alex. And here's the best video avatar to sell Z to Victor. And I'm really curious what that is for me. Like, I don't know. Is it like an old man? Is it like a woman my age? Like I, I have no idea, but someone's going to math that out and I'm going to get told a lot about myself based on how I'm sold to. And I don't know if I like that. That just feels, is that therapy or is that advertising? Yeah. As I said, I mean, there's going to be, it's going to be weird and it's going to get real, real quick. And there's lots of, you know, ethical considerations and culturally, right? I think there's the fact that these technologies developing so quickly is very exciting. But humans are much slower to change and to understand new technologies in what's happening in the world than technology is developing right now, right? And that definitely creates, that creates some problems and potential challenges that we have to make sure we kind of square out too. All right, well, Victor, an absolute treat. When Express 2 comes out, I want you to come back on the show and show it to me. And I'm curious to see how far things can go. But in the meantime, I'm going to go make your AI avatar tell me more heavy metal facts because I can. What is the URL for people to go check it out themselves? And quickly, what's a role you're struggling to hire for? Right. www.santicia.io. Go in, create a free account, make an Amazon for yourself. Right now we're struggling to hire, I'm hiring across all roles. And we're building an amazing go-to-market team across North America and Europe. And we're building amazing engineering teams in Europe. So if you're in one of those two camps and you want to join a company that's become a hundred billion dollar company one day, then you know where to go. All right, thanks, Victor. I absolutely love talking to founders. It actually just never gets old. I've been doing this for what a decade and a half now. And every time you talk to a new founder, you leave pretty darn energized. We're back tomorrow with our live news team with Jason and Lon. But I do really enjoy getting the chance to sit down with founders, dig a little bit more deeply into what they're working on. And just riff, just learn what's working the market, what's not, where is the capital really flowing? So expect a lot more of these, I have a bunch in the can. We're recording a lot more this week. So there's a lot more twists coming your way. I'll see you tomorrow. Bye.