All right. We're going to go ahead and get started. Good morning, everyone. Thank you for coming to the H.C. Wainwright 28th Annual Global Investment Conference. My name is David Chomiak. I am an Investment Banking Analyst on our ECM team here at Wainwright. Today with me, we have Ryan Steelberg with Veritone, Inc. I want to give him as much time as possible to talk to you guys, so I'm going to open up the floor to him now. Thank you very much. Good to be here. Ryan Steelberg, Chairman, CEO. The presentation I'm going to do today is I'm going to walk through. Actually, a little different. I'm going to go through some customer deep dive profiles. Talk a little bit over the business, and then at the end here, we'll give you some updates on our finance. Talk a little bit about reaffirming our guide, which we did from Q2. Obviously, we're sitting here in the middle of September, and if we got time again, answer a few questions. Thank you. Veritone, we started the business in 2014. After we left Google from an ad tech career for going back a few decades, we really saw the need for AI being applied to unstructured data. Back then, we were primarily needing to understand what was inside audio and video at Google to try to frankly understand what's the best way for us to monetize audio and video assets for selling advertising space. But we saw and felt that there was something bigger around the horizon. In 2014, obviously, this is before the large language models came to market. This is the days of early transcription, bespoke AI-based models. But we had a vision, we had a passion that if we could create a solution called aiWARE, which is our core technology stack, if we could ingest petabytes and petabytes of audio video data, and we could index that with high degree of accuracy and precision, it could open up a tremendous amount of interesting use cases and monetization opportunities. That's where we started. The business, Veritone, which means a little play on the words truth in the signal, Veritas and tone. We've been around now and servicing thousands of customers with our solutions, both in the U.S. and around the world. Our business really focuses on two main verticals, commercial, which is for us, commercial in a dominant position in media, entertainment, sports, and news, and more recently, public sector. That would be law enforcement agencies, the Department of Justice, the Department of War. I'm going to walk you through customer journeys on a couple of those type of customers who are using our technologies and solutions today. For us, from day one, it's been all about data. Every single day we wake up and the amount of unstructured data that we are producing as a society continues to increase. Whether it's us as citizens and individuals with our phones, whether it's municipalities with cameras all over the streets, citizen upload, body cameras, dash cams, we are producing a tremendous amount of unstructured data. Unstructured like audio and video is messy, meaning machines and other things can't readily understand it, let alone use it as a utility value, potential monetary value. We've set out on a mission, and we've been successful in solving that equation. Our solution is called aiWARE, and we've applied it now to thousands and thousands of customers, which I'm going to go through in detail. Our customers are some of the biggest brands you know. I'm going to walk you through them. I'm going to talk about, for example, the US Open Tennis Association just wrapped up. Groups like Disney, CBS News, again, the Department of Justice, the Home Office in the U.K. Those are kind of the caliber of the customers that we sell to and service every single day. We like to say as our solutions are now mission-critical, they are not novel or optional. They are germane and instrumental to their normal and their consistent operations. I go back to what is our core technology stack. aiWARE is our stack. It is a full end-to-end software and data stack for AI. That includes data ingestion. That includes the orchestration and onboarding of bespoke and different AI models. We have our own models. We have on our platform, over 60 of our models are actually proprietary, trained, and tuned AI models that Veritone developed. We also have a complete application and API stack, which means we are not just processing data and delivering JSON data payloads back to our customers. They're actually using the end applications that Veritone develops every single day. At any point, we have tens of thousands of active end users that use our end applications every single day around the world, and I'll walk you through a few examples. As I mentioned, two biggest areas of go-to-market for us are commercial, really defined for us as media, entertainment, sports, and news. Then I'm going to go later and talk about what we're doing in the government and public sector. In the commercials world, what we do for almost everybody is we help them understand what are their data assets, what are their data assets that they are trying to figure out how to better monetize or get better utility value from. We help them build an instance of aiWARE, whether that's going to be run in their own tenant or we're hosted it in a tenant, a shared like a hotel or a tenant for them, and we help ingest all their data assets. We help orchestrate and tune models to understand those datasets, and then they license end applications from us that turns that into very discreet and tangible value. Again, that could be if you watch SportsCenter, for those who are sports fans, every night at the end of SportsCenter, you'll see Veritone in sort of the credits. We help ESPN and other sport organizations sift through petabytes and petabytes of audio and video every single month and help them understand what's the greatest highlights I can pull together in near real-time. Can I create personalized clips that I can push out and distribute through social media in near real time? We are not just creating better search, but we're creating better utility for distribution and monetization for some of the largest media entertainment customers that you know. Movie studios, live sporting events, the Masters Golf Tournament, CBS News. The list goes on and on and on. Here's just a couple examples, and two customer client profiles. Obviously, we're in New York. We just wrapped up the US Open from the US Tennis Association. Veritone was there on a hybrid environment. Our solution, the full stack of AI, was both on-prem and readily available in the cloud for an instance of AWS deployed in AWS for the USTA. What do we do for them? Every single camera, every voice that's recorded on a press interview, everything is ingested into aiWARE for the USTA. In near real time, we are indexing and preparing that content for immediate distribution, highlight generation, collaboration, and distribution. We're talking terabytes and terabytes of audio and video being processed and ingest in real time. This is more than just what you see that gets broadcast on CBS, for example. This is every single camera that's running 24 hours across the two-week event at the USTA. Veritone, we are both a technology solution for them as a SaaS customer, but we are also a licensing partner. We help them actually package and sell and license their footage from the US Open to thousands of buyers around the world. It's rare that you see deals that are multi-year. This is actually a five plus year deal that we have with the USTA. One of many customers like this, where again, we're not just selling them technologies, we're actually showing them true ROI and helping them generate millions of dollars of net new dollars each year. The NCAA, another customer. Been a customer now for several years. Everything the NCAA produces and owns, whether that's the big conference events or March Madness that we've all seen, same thing. We help ingest, index, organize, package, and distribute all of the assets that the NCAA owns. We're also a licensing partner. They pay us subscription technology fees, and we help license that content to third parties. If you ever see footage show up in a documentary, a movie, a commercial, Veritone is there helping them, again, not just prepare the content, leveraging AI and helping distribute it, we are helping them monetize it. What I just described for the USTA and NCAA, we have hundreds of customers around the world like that. Think of we are like a hybrid of next generation Getty Images. What Getty, as you know for imagery, Veritone, because of our technology, is like the Getty Images, but for video. Hundreds of customers around the world do that every day. We, right now, manage over almost 30 petabytes of audio and video for our active customers today. That is how we started. It is probably one of our largest, big business segments for the business today. Again, worldwide type of customers. About several years ago, we were approached by DARPA from the U.S. government saying, "Hey, what you guys have done at Scale AI we have not seen before for sports media entertainment, can you do for the public sector?" That was the beginning of our journey. Still on aiWARE, the exact same technology stack, now has been brought into and is servicing state and local law enforcement, the Department of War, federal civ, like Department of Justice, and international governments like the Home Office in the U.K. What are we doing there? aiWARE today historically has been running in multitude of public clouds like AWS, Azure, Google. We have containerized and sort of re-architected the whole stack, and now we are completely Kubernetes based, containerized, and so the entire stack of aiWARE and all the applications now are in FedRAMP in both moderate level and soon to be high level. For everybody in the room here, it is important. That just means we are in compliance that it can service different branches of the federal government with security levels and clearances. We can deploy the entire stack in network isolate environments like the United States Air Force or the Defense Logistics Agency, both customers. What we have done, again, is staying very focused on helping those institutions ingest, index, and understand some either batch files or live streams in near real time, tremendous amounts of diverse in volume of information that is unstructured data. Drone footage, satellite imagery. What you are imagining, Jason Bourne, that is what we are doing for these government institutions. I am just going to give you a couple customer examples again as well. California Highway Patrol. This is not just about us dealing with the intelligence community, but also stuff that is very transactional, California Highway Patrol. They are actually the largest state sworn officer institution, actually, in the world. California Highway Patrol. We help them. You are like, "Well, how do we help police officers?" The biggest problem that our law enforcement agencies deal with today is an exponential growth of digital evidence material. Every citizen uploads body cameras, dash cams, Flock cameras, you name it. We are producing more and more data. The conflicts when we think about sort of capturing DNA evidence or fingerprints of the past, today it is digital evidence. Wiretaps, you name it. All of that has to be ingested, synthesized, indexed, and processed in near real time. So in effect, whether it is we are doing it for redaction purposes, to get it out to the public where it blurs your faces, or it is part of an investigation. Here is what the investors need to know. That same technology stack that services CBS News is the same technology stack deployed in a secure environment that is running hundreds now of state and local law enforcement agencies, the Department of Justice, and multiple branches of the U.S. government. I do not have a slide on it, but the press hit just last week, that we are one of only 12 companies to be awarded almost a billion-dollar framework with the U.K. government for digital forensics software and tools. Think of it for those who know U.S. contracting, like a BPA, a blank purchase agreement. But instead of having 276 companies listed, there is only 12. Who we compete with. Who do you guys compete with? We do not compete with the foundational models of hyperscalers. We typically compete with legacy software groups like Axon. If you know Axon with the body cameras, they have some of their own software. We actually compete against Axon. The difference between Veritone and Axon, you can have any body camera you want, you can have any drone you get to choose. There is no vendor lock-in, which means data is data. We can walk into any institution, we can create the adapter ingestion flow, and we can immediately give them the tools to understand what is happening in their investigations in near real-time. That is just an example. A derivative. We have been doing this for now 10 years. We have obviously ingested, processed tons and tons, millions of hours of audio and video. Then obviously, as the birth of the large language models and multimodality models started to explode, as we are all pretty much aware now, those groups are voracious. They need data to train and tune their models. We all know the foundational transformer models were all built on primarily them ripping off the open text web. That is how most of the models were initially trained. Then they looked at imagery, then they looked at video. World models that you have heard. What they need to grow and get smarter and finer tuned is lots and lots of structured with metadata video. Veritone happens to be archiving and harvesting video for a long time. So about a year ago plus, we introduced a new derivative line of business that is called Veritone Data Refinery, VDR. What that means is we are now under contract with almost every single major hyperscaler and foundational model developer, and we are packaging and preparing and selling them or licensing them data to train their models. Companies like Scale AI, [Micro1, Mercor]. We are now in that business. It is our newer line of business. It is one of our fastest-growing. Who we are selling to are the biggest names you know, Google, NVIDIA, Meta. These are customers now of ours where we are under contract with them to either sell them datasets that we already have, or they are coming to us and saying, "Veritone, we need a million hours of two people speaking in Japanese and we need to run it through aiWARE to normalize it, transcode it, and add the metadata so we can use it to train our models." Example, cannot give all the details, but NVIDIA. NVIDIA is a customer, came to us. They needed thousands of hours of a very specific multi-angle dataset that they needed very quickly. Veritone had almost all this all ready to go. We actually took multiple different sources through aiWARE, our own stack. We normalized it, ran it through our own AI models to clean it up with appropriate metadata, and we licensed this data to NVIDIA. This is happening now with all those other names that I just mentioned before. This is a new line of business. It is very organic, obviously, because again, it is very congruous where if I am working with state and local law enforcement agencies, we have actually done this with security footage. Do not think of us just repackaging NCAA content or footage of Tiger Woods and trying to train their models. We are also using and licensing and packaging datasets of security footage, stuff like that. Again, very exciting new line of business, very congruous to what we have been doing now for years in our core lines of business. We have had to have very little additional OpEx or CapEx investment to activate this new line of business. This is really exciting. Just almost out of the gates, we have a pretty short-term pipeline for VDR, Veritone Data Refinery, of near $100 million. At any one time, we have hundreds of data scientists at these companies who have access to our datasets through a derivative platform we call Veritone Data Marketplace. It actually looks very similar to the application that frankly, the Masters uses. But obviously, it is geared towards data scientists where they can now very quickly analyze, does Veritone have enough of the right datasets with the right metadata in the right structure so we can ingest that and either fine-tune or tonnage build a new foundational from scratch with our datasets? This is a really exciting new line of business for us. How are we going to grow? I will get to a few financial things. I only got about three minutes, so I am going to go fast. But we have thousands of customers. We have got to do a better job of cross-selling. We are now starting to see our ability to cross-sell our same solutions that kind of cross over between public sector and commercial. One key area. Two, sell more things to our existing customers. I have some of the biggest brands and logos customers out there. We need to continue to do a better job of not just trying to find net new logos, is to continue to upsell them with more products and solutions to our existing customer base. Then third is we have a great portfolio pipeline of new applications that we are going to be introducing. We have announced a couple like Assess for our public safety division, but for those paying attention, we will be announcing some exciting new applications, again built on aiWARE, where they are going to be introducing to our customers here shortly. Just a couple things on financials. Again, for those who have been following Veritone for a while, for me, there is a lot of exciting things happening, but it has been a year of a lot of cleanup. We used to have almost $200 million + of debt. Over the last year, while we have been doing all these exciting things, we have been greatly focused on de-leveraging the business. We are down to about $45 million, of a legacy convertible instrument. It is due in November, so for a lot of analysts looking for a future catalyst, we do need to service that. We are working and have been working to try to service or some structure that we have to service in some form or format, the convertible debt. We have done a great job. We got rid of over almost $77 million of term debt in the past. The convert, by the way, was over at 200. A lot of exciting things, but we still got to push through and clean up the balance sheet. The goal here is to try to de-leverage the business as much as we possibly can, as long as we can. It is a lot smaller, so hopefully the numbers are getting a lot of small numbers, but it is something that we still need to take advantage of. Second thing is just get back to growth and push towards profitability. We have already announced this, but in addition to the de-leveraging of the business, we stated clearly that we are trying to reduce by up to 30% our total OpEx structure. That is not easy. But we have been doing a great job. We have announced that we have reduced it by about 12% already on a run- rate basis. And I feel very confident that we will probably get to north of 25 by the first two quarters of next year. To be clear, when you are servicing these big accounts, it is a fine balance between making sure I have the right investments to accelerate growth, but at the same time, continue to get through and de-leverage this business. Because frankly, Veritone is the most undervalued business in the world. If you look at what I have, proprietary tech and my customers, we are going to get through this with a much cleaner balance sheet, which is, again, we deserve it, our shareholders deserve it, and frankly, we are mission critical by some of the biggest companies in the world. So we are excited about the work we have done, but we still got a little bit more cleanup to do. And that is zero seconds left. So that was like 20 seconds. Any questions?