Hi, everybody. I'm John Hutchison, Executive Director from JP Morgan's Investment Bank, here to introduce Ryan Steelberg, the Co-founder, CEO, and Chairman of Veritone. Veritone was started in 2014, about 11 years ago. Ryan started it with his brother. Ryan has since grown the business to $100 million, IPO'd the business, and overseen the execution for the past 11 years. Ryan has 25 years of experience in executive management across media companies, tech companies, including divisional head divisional leadership at Google. Ryan is based out of Orange County and a native of Orange County, alumni of UCLA. So Ryan, first, why don't you tell us a little bit about your story building Veritone and introduce the audience to yourself?
Thank you. For everybody online, nice to have an opportunity to talk to you today. My journey started, and it's kind of the journey of Veritone, in the mid-1990s. One of the sort of first pioneers to build one of the big first ad tech companies called Adforce, right out of school. Built that up to be, again, one of it was pretty much Adforce and DoubleClick that kind of led and pioneered the way for all kind of ad delivery on the web. Built that business up through its IPO in the late 1990s. We have had a lot of successes building and running, I'll say, tech-enabled advertising technology businesses over the course of my career.
I think the one thing that we learned, and I think it's one of the reasons why in the disciplines of AI, you really see, I think, two big split in threads of where you saw so much innovation come from. One was e-commerce. We'll call the Peter Thiel and Elon Musk path. Then you'll see the ad tech side, primarily Google and that side. There is a reason for that. Why that history is important is, running AI at scale is very challenging. It's a massive data problem. Ad technology and ad serving is a major technology data problem.
You need to be able to, and I'll do the parallel real quick, but if you're going back and you go through the history of going from serving tons of ads on legacy data centers to now migration to the cloud, and then the mobile phone comes out, and then you go from websites to search, what the common thread is tons and tons of data ad requests, billions and billions per hour. You have to figure out to serve the right ad to the right person at the right time. Fundamentally, the leveraging and invoking neural networks, rudimentary AI-based models, was almost a prerequisite to continue to advance when the sheer volume of ad delivery exploded around the world. A whole nother phenomena happened. This was really the onset of the mobile device, where video and audio exploded.
For many, many years, almost everything we were doing originally was very structured. HTML is very structured, meaning think of it as structured data versus unstructured data. Common theme that we're going to be talking about a lot. I'm sure many, many people across this conference are going to be talking about it. The fastest growing data set was unstructured data. Think of it this way. I have somebody who's watching, who's online, and they're no longer just reading a written article and looking at a few pictures, but they're spending 90% of the time full screen watching a video. If you're Google and others and us ad tech guys, that's both an opportunity, but very scary. Where am I going to serve ads, right? I f it's t here became a strong interest leveraging, again, rudimentary cognitive-based AI. This goes back almost 20 years.
We need to start figuring out what's inside this content. We need to do it at scale, number one. Number two is we need to start having a much better understanding of contextual-based targeting, right? So i 'm not just serving an ad and trying to build an inference model about who you are when you're logging on, but also I need to figure out and do the parallel in between who you are and what you're looking at, what your interests are. Very copy, i t was very challenging. Legacy algorithmic processes and models were not working. That is where you start to really see the emergence and investment heavy in AI. Obviously, Google, when I sold my last successful business to Google in 2008, I was tasked primarily to go after the unstructured data market.
How could they start to look at these huge corpuses of audio and video, and how could we start to figure out how to monetize that at scale? So that was really the kernel of Veritone. Veritone as an idea that, frankly, we were thinking about going back to right after we sold DMARC Broadcasting to Google back in the day. Our thesis was this, and it holds true today, and it has been validated over and over, is, wow, there is a lot of unstructured data being created, and this is the tip of the iceberg. Just to put it in perspective, 80%+ of all new data created every single day is unstructured. Messy. Think of it as noise and harmonics off an energy grid. Think of it as audio and video being produced.
When I say unstructured, it's hard for rudimentary machines to understand that data without actually doing a lot of work to the data first. You have to actually, it's like mining ore. Again, if you can figure out how to mine ore effectively, there's going to be tremendous value there. So, Veritone was the idea, and it's a play on words, means truth in the signal, is we believe we could create something very meaningful and impactful that if we could build a system that could ingest huge amounts of unstructured data, primarily audio and video, that'll be a common theme throughout this talk. I could leverage both cognitive science and obviously today, future, I'll say more advanced AI models, large language models, et cetera.
Back then when we started this, we were really having to deal with AI-based models we were creating ourselves that were, I would say, more rudimentary cognitive models. These are transcription, translation, object detection, face detection. In no way are we minimizing those, but when you are dealing with tens of thousands of hours a minute, and our volume today, we process well over 100,000 hours of audio and video every single day for our customers. The volume is astronomical. Fundamentally, you have to be able to, A, be able to figure out how to ingest and store and index all this huge amounts of unstructured data before we can even do anything with it. Number two is, do we have the right cost-effective AI models that can extract enough of intelligent data from it so we can act upon it?
Here's the best way, and I'll speed up. We don't need to go through the rest of the history. This is now 2012. We have this idea. The original thesis, the original idea of Veritone was purely for media and entertainment. How can we help extend this technology base to the big media companies, the big advertising companies out there to extend? Even before we sort of launched our first prototype, which our platform is called aiWARE, which is a key point in our technology stack, we knew that this was much bigger than just tracking advertising and facilitating advertising. I think what I'll do is I'll give you two examples end to end of a customer to sort of explain exactly what we do in the commercial sense.
Obviously, over the years, now we've expanded, which we're very excited about, into the public sector and other areas where there also is a huge need for understanding, leveraging unstructured data. Excuse me. ESPN, kind of a prototypical customer of ours on the media and entertainment side. We have mostly large enterprise customers as our accounts. They range, again, on the commercial side from dozens and hundreds of media and entertainment customers from movie studios to broadcasters, audio groups, and video-based groups. Let's take ESPN and Disney. For ESPN, who's been a customer now for about seven years, they keep renewing and they keep expanding. We are their kind of primary AI back-end system. What we do for them is we, every day, ingest every piece of content that they produce, obviously starting with a lot of their historical archive.
That includes everything you hear over audio, podcasting, their video, the actual linear broadcast programming, and all the components that go into that. Think of this huge amount of tonnage that comes into it. ESPN's job, and they do create some of their own singular shows, but for the most part, they are an aggregator. They're having to collect hundreds of thousands of different sports clips around the world, be able to ingest and understand those quickly. Ultimately, you and I, we see the very end product, which is SportsCenter that comes out every four. That takes a lot of work. Now obviously, with many different platforms for distribution, mobile, social, et cetera, their job, their speed, and the time that they have to ingest, index, organize, package, and re-push out is incredibly high, and it's very expensive.
When we first started working with them, they had well over 500 interns literally manually labeling content, trying to collect sources. Obviously, I think we've completely automated that whole process for them over the years. Now the new world is all that content's being ingested into an instance of aiWARE for ESPN, whereas their system of record, we're ingesting all that content, we're indexing it in near real time. Think of it as just layers, tonnage. Visually think of it as creating a huge data lake first, and then applying the right AI models to extract that value that has multiple layers. So you can, you don't want to trivialize part of the first result. You got a question?
Yeah, yeah. And so from a viewer experience standpoint, that's when they call up a clip from 10 years ago, that's because it got tagged by Veritone, and they're able to pull it up quickly.
One of many. The use cases I'll get to. Before you get to the plethora of different use cases, you have to go through the ingestion, index, organize. It's like creating the Google search corpus before anybody even starts to search for it, right. But yes, now once you've done this full indexing, the opportunities, and I'll really focus on three main personas, the end user, where is this value being derived from, really falls into research, programming, advertising, and sponsorship. Obviously, you're going all the way to B2B2C, the end consumer, obviously, is the final beneficiary of this. But now you can start doing really amazing things in near real time is pull me up all footage where Tiger Woods is on screen, his face. There's a Nike logo in the background.
In an aperture of five minutes, they're talking about redemption from a car accident to winning the U.S. Open. By the way, what I just described, up until the exciting inclusion of large language models into our stack, it was still efficient, but it was like Boolean, your classic search. Now, obviously, you can do it organically. What I just described seems like, wow, that's amazing. It's like truly a search engine for the unbelievable amount of corpus of audio and video. You're right. What's come and been derived from that is now it's been seamlessly integrated into their workflow. They know why which programming is working and what's not. They've now done the correlation to Nielsen ratings. They've now done all the integrations working with us to first-party data for consumption. So they know what's working and what's not working.
They know that that host that they signed a $100 million contract with five years ago, his ratings are declining. You can actually have that layer of understanding in that data to, in effect, to prove and support your decisions when you're trying to figure out new programming ideas. The advertising side just is probably the thing that helps fund this whole project is because so much advertising is now, as we all experience, particularly in sports, is embedded inside the content, we now have complete visibility and resolution of the efficacy of those ads. Meaning, obviously, we're in Boston right now. The Celtics somehow won last night. I'm from LA, so I can do a little pun there. If you watch that game, you're seeing advertising embedded all throughout the broadcast. It's not a commercial break anymore. It's literally organic, right.
When there's a timeout, we all heard it. They're saying this timeout is sponsored by Geico, and there's a Geico logo in the background. Because of AI now, that's indexed. We can now understand how valuable and how effective embedded organic advertising is, native-based ads, in contrast or in correlation with a classic commercial break. That's just another big example. What we're finding is so exciting is you know ads, once you've created this huge corpus of intelligence from this unstructured data, every company is now taken in some different direction. We work with CNBC, CNN, the Disney's. Almost every audio broadcaster out there has been clients for years. I think we've created a great, sustainable, and growing business in the media and entertainment and commercial space.
I'll just give you a metric, and then I'll open it up to a few questions before we go over to public sector. Scale-wise, on behalf of our customers, we indexed and processed almost 60 million hours of audio and video last year. That's like almost 11 PB of data. It's huge, massive scale. So much scale that we are platform agnostic. We run all of aiWARE and our payloads on AWS, on Azure. We're completely platform agnostic. That has multiple benefits, but also by the nature of how we've designed this platform, our customers, let's say, who are big Azure or AWS customers, they can actually knock off or use this platform against their commitments and their credits. Anyway, that's kind of the backstory how we got here. It started with an ad tech vision, expanded.
We are now, I'd say, if you, who is Veritone? We are the experts on large-scale audio and video indexing and understanding with AI. Period. I don't think there's any company that's better than audio and video understanding and leveraging the opportunities for that than Veritone is. That is really who we are to our DNA.
Great. Thanks, Ryan. Bringing things up to speed in terms of a business update. Over the past few years, Veritone's gone through a transition, culminating in the sale of the legacy non-software business, Veritone One. Can you tell us about that evolution just in the past couple of years? Is Veritone through the transition to becoming the pure-play AI company?
We went through, I'd say we started out as pure AI, pure play, and then we went through a journey, and we're kind of coming back to our roots as the markets matured. I've done this. This is my sixth company. Timing is everything at times. When we first launched this, we took this company public for some various reasons, very early, when we were doing, frankly, like $10 million of revenue because we could, right. We were able to raise a tremendous amount of money. My vision when we first, and our vision when we first launched Veritone was we really didn't want to build end solutions or applications. We wanted to build this large infrastructure called aiWARE, publisher APIs, and just wait for the business to flow in.
We would index all of Disney's content, and that was going to be more like a Twilio, if you will, back in the day. Back in 2012 through the early days of the founding of the company, the industry was not ready. They were not ready with their data sets. By the way, if the companies or my clients do not really understand their data sets at all, or frankly, a lot of their data sets are not even in proper digital form, I need gas to run my engine. So we had to do a pivot for the first few years, I would say from 2018 through the early 2020s, 2020, 2022, where we had to go build the actual end application. Meaning, instead of ESPN giving them a bunch of unmined ore, we had to do a lot of work.
Meaning what people are buying from us, that's running on aiWARE, but we had to build the end applications. I'll make a rough analogy. We were building Windows. Instead of people building applications on Windows, we initially, because the industry was not quite there yet, had to go build Office, Microsoft Office. That's what we've been selling. The majority of our revenue today, about $60 million of it, is from the end applications built on our stack, which everything I told you, all the ingredients, all the cool AI, what turns into value and discernible ROI for end customers is the application layer. Obviously, I think thankfully we had enough subject matter expertise to make that pivot. The other thing was in our history, just because of our background and other reasons, we were, and we did, raise a lot of money.
Even with our experience and, I'd say, a few failures in the past and a lot of success, we kind of broke the cardinal sin of we started waiting for the market to mature. With shareholders, we started spending money, and we got distracted. We kind of applied our technology stack into a lot of different other areas. We were in the energy grid optimization business for a while, literally trying to optimize solar and battery utilization with our stack. Because we could, we were kind of waiting, and the budgets were really not flowing. Bottom line is that we spent a lot of stuff, got distracted.
Unfortunately, when the true bomb dropped in this industry, which was the release of ChatGPT, and the whole world woke up and said, "Let's go." Thankfully, I had enough of a stable business, which I've just been describing on the commercial side and the emergence of our public safety side. We were not, unfortunately, in a position to sprint and capitalize on it. That was a mistake we made. We spent too much money. We then raised debt. Unfortunately, in my mind, the perfect stack that was ready to go and get us into hyper-growth velocity, I was cleaning house. I was a private equity CEO trying to manage downsizing when I should be investing and going for hyperbolic growth.
That process of me, I'll say, trying to clean up the business and get us back to, frankly, our roots has taken about two years. I do believe we're through the valley. One of those which you just described is we did at one time own a traditional media agency, like an ad agency. It was a big one. It's called Veritone One, and we finally offloaded it at the end of last year. It was powered. What differentiated that advertising agency was our technology, but it was a service business. We weren't selling. The core product was not aiWARE or our applications. It was this third-party services group. That was, I would say, one of the few major strategic moves that I needed to make to get us back to where we are today, where I'm pure play AI.
This is what I do. I ingest unstructured data. I turn it into value. I sell applications. I think we are there, which is exciting.
That's great. Jumping into what I think is the most exciting question today, Veritone Data Refinery was announced in November of 2024 and is an exciting new growth driver for the company, already driving about $10 million in pipeline. With premium data becoming more valuable for training AI models, what we think is going to be a $17 billion market in the next five to seven years. VDR is positioned to deliver a huge amount of growth for the company to help monetize customers' content in whole new way, in a whole new way. Can you tell us a little bit about VDR?
Yeah. In the workflow and ecosystem of AI, training data is lifeblood. Before you can run these models against new data sets, like running transcription and so on, you have to actually build the models. Building these models takes training data and lots of it. It is exponentially growing, the demand for training data. It took a whole nother level when you introduce large language models and now multimodal models, meaning models that can understand and discern audio and video and other things in a common model. There have been a few companies over the years that have emerged to assist in the preparation of training data. It is something that we have been looking at for a few years now. One is a private entity called Scale AI, which you may have heard of.
Just to put in perspective how important preparing data is, the CEO of Scale was actually with the president in the Middle East. Just perspective. You're seeing Coke and you're seeing Jensen and NVIDIA and Elon Musk, but then there's somebody there who prepares the data, literally. It is not just pure technology. It is a mechanical turk, human labeling company right? W hich is a big portion of the revenue. That is how important data is. It is an insatiable amount. You mentioned $17 billion. It could even surpass that. What we looked at was how can Veritone, because we have been now for over a decade ingesting and amassing the largest corpus of clean audio and video, this might be a big opportunity for us. What made us make the decision to open up this organic new line of business was Shutterstock.
Shutterstock, a public company, recently being, I think, taken private and consolidating with Getty. Shutterstock, who is one of the largest stock photo businesses out there, they entered that space, meaning they started to mobilize and license and work with the hyperscalers. These are the Google Geminis. These are the OpenAI AIs. They started to license their imagery to these groups to help them train their models. We were watching this very closely since, obviously, we're sitting on a tremendous amount of audio and video. When we saw their growth, that really went from like zero to over $130 million in just a few years in their data services business.
It was when we started to craft our idea, really in the second quarter of last year was when we said, "Hey, let's really look at the efficacy of this business line expansion for Veritone." Obviously, fast forward a little bit. We designed and took to market VDR, Veritone Data Refinery. We kind of came to market in mid-Q4. It has only been a few months. The reception has been incredible. We are, we think that this is going to be one of the largest lines of business for Veritone for the next several years. We are actively booking business. We are generating material revenue. Contracts are six figures and higher that we are working with, and s pecifically, again, we are working with on both sides of the equation.
We're on one side, if you will, representing and working with our customers who we've had customers for 10 years in some instances, not just large media companies, but I'll just say other entities that have large corpuses of unstructured audio and video, including surveillance video, which we'll talk about public sector in a second. Now we're ingesting. We're preparing that data, ironically, using AI to prepare the data to train other AI. It's a really interesting virtuous cycle. No, we're engaged with all the main buyers. We do think that we thought that this was going to be a few million dollars of contribution. I think it's going to be significantly more than that in calendar year 2025 and beyond. I think we've got some interesting moats around us. I mean, scale is a big one.
I'll give you just a couple of other examples. We're not just limited to facilitating the data sets that we already have under representation. It gives us a huge competitive advantage that we do with groups like the NCAA and others that have these huge libraries. However, we are also being tasked and working with the model development companies, the hyperscalers, who are giving us tasks on their side. They're telling us under contract with them to go find new data sets, help us prepare it. So if you want to think about it, Veritone is the audio and video version of Scale AI. That's a very, very exciting opportunity for us. I've seen lightning in a bottle a few times, and this could be one of them.
Even if it's not lightning, it's going to be producing a lot of revenue ore for us, which is exciting.
That's terrific. Jumping into public sector, you've alluded to several times now. Veritone has made great strides in the public sector with some large wins with the DOD, DOJ. What are some of the problems that you're solving for the public sector, and how are you competing with, how are you taking on competitors who are also trying to dig into the space?
Yep. Reflecting on the commercial side with media entertainment customers, it's interesting. Their core business is leveraging the audio and video they produce and make money in. The core asset that we act upon is their core product offering. Media entertainment customers have triple PhDs, if you will, on the format of audio and video, even before cloud and going back cold storage and tape and et cetera. We've proven there was a huge problem. We've generated a nice business from helping them mobilize and advance for media entertainment customers. Meaning, now imagine you look at almost every other company whose core expertise is not managing their data. It's not their core product offering.
If you look at state and local law enforcement, I'll look at two groups, the Department of Defense and certain agencies like the Air Force or state and local law enforcement. These are entities now that are being bombarded with having to, again, understand and leverage tons and tons of data to run their business. If you are Beverly Hills Police Department, a client of ours, and you are involved in a case or investigation, this is no longer cops going out and talking to people and taking manual notes and stuff. This is now a massive data collection. We know the one we've all celebrated now, and it took years to really understand is DNA. Imagine doing cases now without DNA. By the way, DNA was probably one of the most unstructured things that you could figure out or try to understand.
Took years to have any credibility before it even was acceptable in cases. Now, in fact, it's considered as ground truth, ironically, even more than eyewitnesses at times, as you know. So if you are, let's go very practical. There is a homicide or there's a domestic violence incident. Every investigation has to start collecting evidence. The fastest new form of evidence is audio and video, body cameras, dash cams, security cameras. Obviously, we like to say crime travels. Something bad happens, the person flees. We'll call it like Jason Bourne. They're running across it. You have to collect this information. You have to build a case. The Boston bombing, we're here. It took literally thousands and thousands of people. They rented out warehouses and sifted through the stuff manually. Imagine, and that's unlimited budget with the FBI.
Imagine now you have to do that in a case where it's citizen upload video. We have applied Veritone and aiWARE technology very similar to what we do for audio and video for ESPN, but now we're ingesting and integrating and harvesting all those disparate data sets to help accelerate investigations. There are many different layers of this. It's so exciting. Ultimately, we want to help them close cases faster, which is time savings because I don't care where you are and what your position is, but all these budgets are being constrained for law enforcement. There are all these residual applications or use cases that popped up, like FOIA request, Freedom of Information Act. When you see footage show up and it's being redacted. It's not just using our technology to find the bad guy.
It's also protecting, I'll say, the identity of citizens. They have to release that footage to the public. It's called FOIA, Freedom of Information Act, where that information has to get released to the public. We need to protect people. We use our AI to obfuscate their faces and change their voices. That's what you see when those video clips pop up on the news and stuff like that. Public sector is a very, very exciting business for us. What I just described in kind of that micro use case for state and local is the same applications at a much bigger scale, which we're doing with the DOJ and the Department of Defense. We're actively working with the Air Force. We're actively working with the Department of Defense Logistics Agency. It's taken us a very long time to get in this space.
Getting into the Fed and FedRAMP and having your authorization operate takes years. I think successfully we're there. You're going to see 2025 is going to be not just the year of VDR. 2025 is going to be the year where Veritone sort of breaks into a new electron level for public sector growth as well.
Any questions from the audience in our last few minutes here? All right. A couple more from me. You mentioned these great, really exciting growth vectors in public sector and the VDR product. What are a couple of things you wish the market better understood about Veritone in the story?
We've been around for a while. I think it's probably here's the core pillars. One is Veritone probably has the largest number of enterprise core AI customers outside of the hyperscalers. We have thousands of customers that use our core AI platform and applications every single day. We have tens of thousands of end users that use our applications. So our solutions are mission critical to the missions for these commercial businesses as well as the public sector. I would say when you dig in, the more interesting and exciting it is. There's real meat there. The testament and the proof point there is our retention rate is very high, high at the 90% percentile. Many of the companies I mentioned before, the NCAA is, I think, just signed a five plus three year deal. I mean, these huge extensions.
We are here to stay, and we're experts at this. Thankfully, we've done a good job of being, I'd say, strategic and fiscal stewards to get the business back in shape, back to our roots. Here's the second thing I want people to know is through that journey of cleaning up the business, we did not get rid of any of our core assets, not one. Our core lines of business, we kept all of them. I think people are this is really an entry point. I think, frankly, we are probably the most undervalued stock on all Nasdaq, bar none, relative to it. I think that will change very, very quickly. Hopefully, it shouldn't take one big announcement with a new big DOD customer. It shouldn't. Again, we have to prove ourselves. At the end of the day, it's a numbers game.
We should not be a story stock. Again, I just think once we get back into the light, for over 1,000 days, our stock was over $20 going back years. We should be at a different point. I think some were strategic moves we made poorly in the past. We are through that. Now it is just, hey, take a look at Veritone because if you are looking for AI, quote unquote, exposure, this has not been a diamond in the rough. It has been a diamond that has kind of been under a cloth here for a while. It is still there. People are going to see some amazing things this year from us.
That's great. Wonderful. Thank you, Ryan, for joining us today here in Boston at the JP Morgan TMC Conference. Thank you.
Thank you, everybody.