Welcome to the Southwest Ideas Conference, presented by Three Part Advisors. Up next is one of our clients, Red Violet, trading on NASDAQ under the symbol RDVT. The management team has built and sold businesses that are the core identity verification technology for TransUnion and LexisNexis. They recently reported a strong third quarter, where they saw record revenue of $19.1 million and $7.2 million in cash flow. Presenting on behalf of the company is Camilo Ramirez, Senior Vice President of Finance and Head of Investor Relations. Camilo?
Sorry about that. Thank you, Errol. Thank you, everyone, for joining me today. Today, I'll go over the management team, what we've done in the past, go over their background, what we do, give you a little high-level highlight of the business model, give you a couple of use cases, and feel free to ask any questions if you want to make it more interactive. I won't really flip through the deck today. I'll probably show a couple of pages, but I would prefer to make it more interactive. I think you'll get more value from that perspective. The team's been together for about two decades. In the late 1990s, they started a company called Seisint, also in the identity space. We do all things identity.
At a high level, we aggregate all disparate databases, call it liens, judgments, any PII information on individual credit header data, mobile IDs, IP addresses, you name it, we most likely have it, so we aggregate that data and create a 360-view profile on every adult individual in the U.S., and then we sell it back out to a couple of different industries. Today, we service about five different industries, as we call it out publicly, so we'll start with financial services and corporate risk. In that scenario, financial service is going to be like KYC applications, know your customers, and then the easiest one to understand is going to be collections. Debt collector buys a million records from Capital One. They go out, they get that file, send it to us.
We'll scrub it for right-party contact information, deceased, bankruptcies, and so forth, and we'll send it right back to them through a batch process. We service the investigative vertical. Investigative is going to be like law enforcement, private investigators, so we're having a big push into the law enforcement public sector this year and then really accelerating into it next year as well, and then lastly, well, two more. Lastly is going to be real estate, so we service real estate in two fashions. One, on our IDI side of the business, it's going to be propensity to sell, right? So like real estate marketing. Give me a list of everyone that's over 65 that lives in a two-story home and so forth, and then on the FOREWARN side, it's more of a safety solution, so essentially, let's say you're a real estate agent.
You get a call from an individual, "I want to see this $3 million home. I'm only in town for one day." You know nothing about this individual. It's essentially app-based product. Once they call you, their phone number comes up, does a reverse phone search. They're saying, "Hey, I'm going to show up in a Mercedes S-Class." You don't see that S-Class registered to them. But what you do see is, so they were just recently released from prison with a violent background. So you're most likely not going to see that individual, right? So it's a safety product. And we go towards the associations as opposed to the individual. So today, we service about 500 associations in totality. There's about 1,100. So we're making really good progress in that space. We're the only proactive safety solution out in the real estate space, for lack of a better term.
Most of the solutions out there are like find the body, right? So panic buttons, you're getting attacked, you hit that button, and you hope law enforcement arrives in time from that perspective. And then let me go back to a little bit of history. So in the late 1990s, they started a company called Seisint, right? Technology was very different at that point in time. Ultimately, they ended up selling to Reed Elsevier's LexisNexis for about $750 million. Team got back together. Once non-compete expired, they started a company called TLO. They were building that through the R&D phase. And then the individual that was backing the second go-around, he ended up passing away abruptly. So they had to sell the company prematurely. At that point in time, they were doing about $25 million in run rate, losing about $1-$2 million a month.
Ultimately, TransUnion ended up winning that bid and purchased it. There were a couple of individuals that were in that bidding process as well that lost out. They approached the management team, "Hey, if you guys get a third go at this, what would you guys do differently?" Technology has changed very differently. So the second one was built in the early 2000s. And then this go-around, call it around 2014, they started it. So as you can imagine, you have the cloud now, right, as opposed to building data warehouses and so forth. So we built it in the cloud this time around. And then we used AI and machine learning. The previous iterations were more static connections. So if you see Camilo and Errol at one point, always present them when you're doing a search on that individual. And it's not always appropriate, right?
As opposed to ours, it's not a static connection, so you're not always going to have that presentation depending on that scenario. And then also a couple of differentiators from that perspective. Early on in this iteration, we made a purchase company called Fluent. They're a performance marketing-based company. What they do is they have all these website properties. They're saying, "Hey, win an iPhone, win Super Bowl tickets," and that kind of stuff. So it's not you and I that are interacting with that website. It's going to be your underbanked, underserved. So your 18- to 24-year-old population, still living at home, frequent movers, not purchasing cars, homes, and that nature. So we're gathering that data. That's our proprietary data. We ingest it and then combine it with the publicly available data, regulated data, and so forth.
So we're winning a lot of business from that side as well. And that's proprietary to us. High level of the business model. As you can imagine, there's a ton of data out there. We're buying data. These data contracts are long-term in nature, usually five years. And so we like to say our cost of revenue is fixed. So every additional dollar is nearly 100% contribution margin from that perspective. Excuse me. And we always get the question, so how about what if someone says, "Hey, I'm going to hike up the price upon renewal"? So a couple of examples there. We had a renewal occur maybe three years ago now for one of our largest data providers. And it renewed flat from that perspective, right? We're multi-source on every data point.
We want to get as many socials, as many names, dates of birth, and so forth for every individual for confirmatory reasons. And we also want that wrong transposed social as well. So if someone's searching that transposed social, your name's going to come up as well since you're tagged with it. And that also gives us some security upon renewal. So if someone says, "Hey, we're going to hike you up 50%," we're like, "Okay, we can't accept that." So we'll just bring our second-tier data source up and transition that first tier out and then go out and get additional conforming data sources. So today, we get data from two of the top three credit bureaus out there. You can deduce who we don't get data from. And then also a number of niche players for specific data sets from that perspective.
And then a couple of examples on the revenue side. So how do customers interact with our website, right? Let's just flip a couple of slides here so you can see our history as well. So first off, there's three ways to access the system. Online, once you're credentialized, you can log in, essentially like a Google search. You have a name, date of birth, or any combination thereof, and then you'll get results back from that perspective. Second's going to be API, computer to computer, right? So we serve about seven of the top 10 identity players in the space. Without naming any customers, it's the likes of Proof, Jumio, Ekata, now owned by Mastercard and so forth. Everyone has a different way of doing that identity verification, but they don't own any of the data.
They have to transactionally reach out to us to complete that individual, that identity verification from that side. So like an example, let's say you're logging into Bank of America, Wells Fargo, so forth, using iPhone. You're going to be using facial recognition, right? So what's happening behind the scenes is actually it's saying, "Hey, this mobile ID belongs to Camilo. Who's on the other? What PII information do you have? Camilo, does it match?" Yeah, sorry, grant access. If not, potentially push them to a couple of security questions and so forth. And then I'll give you another use case. We named this customer early on, Innovative. They're in the background screening space. We won it from TransUnion shortly after we went from R&D to sales phase. That was about six years ago. Ultimately, Innovative was purchased by a company called Appriss, and then Appriss was purchased by Equifax.
What does Innovative do? Let's say you're a Walmart, right? You have an applicant coming to your website. What they're doing is, "Hey, listing their four addresses and submitting that." So Walmart's going to send that to Innovative, and Innovative's going to come to us for completeness, "Hey, is this information accurate and complete for this applicant?" We'll say, "It is accurate, but it's incomplete. They left off a fifth address." And that's usually where the criminal history lies, and they can take that back. So yeah, so Innovative, their contract was up for renewal when they were purchased by Equifax. As you can imagine, Equifax has all the data in the world, way more data than we do. They reached out to us, said, "Hey, can we get a three-month extension?" And of course, we understand you're trying to execute on synergies.
They had that extension, came back for another extension, and came back for a third extension. And ultimately, they signed a long-term agreement with us. They said, "Hey, we can't produce the same type of lift that you guys are getting on that data. So we're just going to sign a long-term contract with you guys." Even though they have all the individual data points, it's not the data that you have. It's what you do with that data. And we believe we do that exceptionally better than the competition. So our main competitors are going to be the legacy two businesses that management created. So it's going to be Accurate, which was product under Seisint, ultimately purchased by Reed Elsevier's LexisNexis. They're the 800-pound gorilla in this space. They have about 400,000 customers. They're a big player in the public sector vertical. And then you have TransUnion.
They have about 50,000-60,000 customers. So when we're calling a customer, it's not, "Hey, here's this widget. Here's what it does." It's, "Hey, who do you use today for your identity verification? Is this TLO or Seisint?" "Hey, we created those two platforms, and here's the next generation. So here's a free trial, right?" It doesn't cost us anything to give us a free trial because our data is fixed. So we want to get that customer to start testing because once they start testing, they'll see that ROI on that investment. And we're more than likely winning that business once in the testing phase from that perspective. And then let's just jump down the page now. I guess typical customer contracts, right? So typical customer contract is going to be a 12-month contract with auto renewal. About 80% of our revenue is contractual.
We have a lot of SaaS-like characteristics, but we're not 100% SaaS. So mainly a customer will come on board, whether they can forecast their business or not. If they can't, they're usually going to use this transactional. They're going to pay a higher per-click rate from that perspective, right? They're not minimum committing to anything to us. Or they'll say, "Hey, here's a minimum commitment. Call it for $5,000. You'll get 10,000 searches." Once you reach that 10,001, it's going to go into overage, and it's usually a higher price point. So we'll use that as an upsell opportunity. We see a customer that's continuously using 120%-130% of their contractual obligation. So we'll reach out to them, "Hey, let it work with us, and we'll give you a better price point, increase that minimum commitment." So that gives us a lot of visibility into the future, right?
We run the business on a month-to-month basis from a revenue perspective, so we're always trying to beat that high watermark, and as Errol stated, we had a record quarter. Last quarter, we had a really good year overall, so Q1, we were calling around 20% up. Q2, around 30%. And then Q3, we just reported a 20% increase with a record quarter top-line revenue. A lot of times we get the questions like, "Hey, what does your margin profile look like?" In the past, we've given guidance clarity on what does this business look at at $100 million. So from a gross profit margin perspective, 80% gross profit margins on the Adjusted EBITDA line, about 40% Adjusted EBITDA margins, so today, we're at that 80% mark. We're not at that 100 million mark yet, and we're approaching that 40% Adjusted EBITDA margin.
So we're spinning off a lot of cash. We're GAAP profitable as well. And then I guess I'll touch base on a couple of things we're excited for 2025. So 2025 and even this year. So about two years ago, we made a big investment into a headcount on the technology side. We had a couple of items on the roadmap that we wanted to accelerate. We went out and hired those individuals for a couple of verticals that we're really excited about. The three verticals that we really focused on and are focusing on for 2025 is going to be background screening, right? With the Innovative purchase by Appriss, we knew we were potentially going to lose a customer. Ultimately, we didn't. And we decided, "Hey, we're going to go compete direct with the reseller, right?" And get end-user rates as opposed to reseller rates from that perspective.
We built out a couple of products specifically for that, hired an individual to lead that vertical, and then we've been fine-tuning that space. We used our customer base to help us build the product as well. "Hey, from a beta testing perspective, here's what we're doing. What do you guys think about it?" And it was very interactive. They came back, "It'd be really great if we can do this and X, Y, and Z." And we'd evaluate internally, and then we'll put it on the product roadmap. And ultimately, we made the call, "Hey, this is really good. This is a lot better than the competition. And so let's go to market." And that's where we're at today, starting to go to market. We just made a really good hire. The individual's named Dean. He actually came from Innovative, ultimately.
He was at teen early on, knows the data space very well, what type of data to go get, where you're going to get lift. Because as you can imagine, there's a lot of data, and there's a lot of data that's not useful out there. So he just came on board probably a month ago. So we're excited about that hire. Secondly, marketing services. If we can know everything about an individual from a risk perspective, then we can know everything about your best customer. It's the whole customer lifecycle, not just a marketing list. So we'll start off with, "Hey, here's a list of a marketing list that emulates your best customer." We'll send that out to the customer. As they're doing their onboarding, they need to do that identity verification.
So we'll help out with prefill applications, making sure that individual is who they say they are, updating their CRM with third-party contact information, and ultimately all the way down to the sales aspect. So they're purchasing a large ticket item. As they log in, there's this address. We'll let them know, "Hey, this address has been seen multiple times over the last couple of weeks associated with fraud." So potentially put that through your fraud cycle before you actually send out that product. And you lose out. You get a chargeback. You sent out the product, so you're out those funds. So helping mitigate risk from that perspective on the customer, all on the marketing services side. And then third, which is what we're really excited, we made a number of investments this year, is going to be the public sector.
As you can imagine, those contracts are relatively large. You guys can do a quick Google search on RFPs as it relates to LexisNexis, Red Violet, Seisint, and so forth. And there's a product out there called CLEAR by Thomson Reuters. It's a legacy product. It's not a sexy applications, but they have a big foothold in the public sector as a whole. So we brought on Jonathan McDonald. He helped drive revenue at TransUnion early on from zero to where they're at today in the public sector. He came on board. We made a number of hires underneath them. And when he came on, he's like, "Hey, most of our wins are going to be in 2025. Don't expect much in 2024." It's a much longer sales cycle in the public sector, budgets, procurement, and so forth.
So ultimately, we've been seeing some really good wins from him this year. As you can imagine, any good salesperson is going to sandbag it a little bit when he's talking to us. But we're really excited about that on that team as a whole. We're having really good execution on the law enforcement side. So one of the key differentiators on the law enforcement side, let's say you're doing an investigation, you talk to a witness, they say, "Hey, there's a red Ford F-150, and I got two letters of the license plate, X and Y," right? So basically, it's a geo map. You go into the app, you put in that information, red F-150, and it'll drop pins within a five-mile radius, 10-mile radius, which you can pick. So it'll drop those pins that match those locations.
You enter the couple of letters that he has, and it removes the pins that don't match it. So it takes these couple-hour investigations down to seconds, right? Because it's just dropping those pins, and then you can go through your investigations. Or you can even say, "Hey, give me everyone that has a sexual assault case or something like that on their record," and it'll drop those pins everywhere, and then you can start deducing from that perspective as well, so it's very interactive, and we've been winning a lot from that perspective, so the example I would like to give is, "Hey, there's a law enforcement going out, executing a warrant. They arrive at the home. They know the individual that lives there, who they're going after, but then they see five other cars. They don't know anything about those five other cars.
They have to call back home office saying, "Hey, these other vehicles are here. Can you let me know who's here? Are they violent? What type of background do they have?" But with the mobile application, they can do quickly, run the license plate, and get that background right in the field. So they're really excited about that. So we're having really good penetration there. Our sales team was just at one of the larger law enforcement conferences that had really good turnout from that perspective. And that's a big vertical for us next year, as you can imagine. Those contracts are relatively large. I'll pause there, see if there's any questions, and then I can keep going. No questions? All right. So I'll go through a couple more use cases.
I think that's going to be the best-case scenario, just so you guys can understand what we do, the diversity, right? I'll give a FOREWARN on the FOREWARN side. So I gave the real estate agent use case. So we're also starting to expand into other verticals, right? We're beta testing. We get that question, right? Because you have a stated market. You have 1,100 associations. Call it about 1.1-1.2 million real estate agents. So what's next, right? So we're starting to beta test on the small business side. Let's use women small business owners, right? They're hiring individuals, meeting people off-site after hours. They know nothing about that individual. So building upon that safety aspect. So basically, hey, they're going to meet an individual after hours.
They can quickly do a reverse phone search on them and see if they have a violent background or anything, just so they feel safe from that perspective. And then also, excuse me, on the home healthcare side. So essentially, you have these companies sending individuals out to homes. You know who you're going to help, right? But you don't know who else lives in there. Is there a grandchild with a violent background? So essentially, the organization top-down can do that search and say, "Hey, okay, no one else lives there. We're pretty comfortable. We can send one individual to the site." Or if there is someone with something that they don't like from that perspective, violent background or anything from that side. And we won't be presenting that background. It's going to be more of red, yellow, or green, right? So green, you're good.
Just send that one individual. Yellow, you'll probably investigate. Red, maybe you won't send it out, or you'll maybe send out two individuals to that location just for a safety perspective. So going after the corporate side, enterprise as a whole. And then on the IDI side, I gave you guys the mobile authentication example. On the fraud and risk side, so let's say you're a Ford Motor Company, accounts payable. Someone's going out writing a $2 million check every month to one of your vendors. Ford would want to know if that individual's an accounts payable, their brother-in-law owns that company, right? It's not saying that they're doing any fraud, but it's, "Hey, let's be aware of this, right, and make sure we review it a little more." So send that signal, "Hey, there's a relative at that vendor.
Potential, you might want to look into it as a whole from that side." And then we always get the question on, "What does growth look like? Guidance and so forth." So today, we don't give any guidance. We're here from a long-term perspective. We don't want to go after the business quarter to quarter from a whole. So this year, we've been saying we'll be growing about 20% year over year. We've been executing on that. Early in the year, we received coverage from B. Riley, our first analyst. As you can imagine, it's tough to get research out there if you're not doing any transactions and so forth. Our volume's not, it's a little lower, but then we traded. We can account for 75% of our shareholders out there through management, executive board members, and then large shareholders as a whole. So there's been strong holders, ultimately.
A lot of times, they can't make anything on a transaction, and they can't make anything on trading volume as well. But we do have really good relationships with large names out there. If you go back and take a look at who we've spoken, what conferences we've been at. We have a really good relationship with RBC, Stifel, and so forth. The analysts are tracking us and so forth, and it'll be a matter of time, essentially. B. Riley was the first one to step up and say, "Hey, think about us when you do do a transaction." With that, on the M&A side, we get the question, "What are you guys doing? Organic M&A? What are you looking at?" Today, we're growing the business organically. We have about 8,700 customers, just over it.
Again, the competition, TransUnion, they have about 40,000-50,000, 50,000-60,000 customers. On the Red Violet versus LexisNexis side, they have about 400,000 customers. So we're just scratching that surface. Early on, we're going after those small and medium-sized businesses so we can reach profitability. And then about, call it 18 months ago, we made that transition to larger enterprise customers and starting to execute on that. On the M&A side, if we do look for anything, it's going to be one of three items: a unique data asset, something that we can bring in-house, proprietary, control that data flow as well, and control the cost. A couple of years ago, the valuations were just outrageous. We're glad we didn't do any of those transactions. TransUnion, they did a couple of transactions. They're starting to lick their wounds. They paid a high multiple for that.
And then third is, sorry, second is a buy versus build. So we made that decision, a build decision with the background screening side, right? So we're seeing, "Hey, what end product is out there? Should we just build it internally or go out and purchase it?" So we're always looking at those unique technologies or an adjacent technology. For example, an adjacent technology is going to be biometrics, right? Doing verification on fingerprints and so forth. So we're always looking for unique opportunities in the space. We're being presented constantly. The identity space is very hot. So if you look at comps, a number of years ago, you have Ping Identity that was purchased by Thoma Bravo, ForgeRock, and so forth. So that's a really good example of what that profile looks like if you're going to compare us.
If you're comparing us today, it's tough because usually most of the companies are in the private sector. Again, we're powering about seven of the top 10 identity players. Most of them are going to be private. And then our comps are buried within much larger organizations. Again, TransUnion, Red Violet, Seisint, LexisNexis, and so forth. And then for next year, we're saying, "Hey, we're going to continue this re-accelerated growth profile for 2025 and beyond." So we're saying, "Hey, we're going to continue that 20% target for 2025, even with the higher comparable for next year as well." Again, I'll pause to see if there's any questions, comments.
In May, your stock was selling around $17. Yesterday, it was up $35, almost doubled in a short time. And I guess I'm trying to balance your request that I talk about the future, but if you could help us understand.
What occurred then. Yeah. So the question was, "Hey, early in the year, the stock price was trading under $20 today." We'll call it we're around mid-30s. So early on, I'll give you a little background as well and lead up to the story of what occurred earlier in the year. One of the early investors, they invested in the company, a multi-billionaire down in South Florida. Doesn't have any kids or any heirs. He's pledged all his wealth. Ultimately, he donated his shares to the Greater Miami Jewish Federation. Initially, I call it it was about 2.5 million. We made a number of introductions a couple of years ago. Ultimately, one firm bought a million shares from them. And then as we started trading up, they were sellers around the $20 mark. So we usually had a cap.
Every time it would hover above $20, they'll be out there selling pretty sloppily. It just wasn't a clean sale process on their end. There'll just be market orders out there. We'd get calls, "Hey, do you know who's selling 20,000 shares, 50,000 shares?" We don't know, right? And more than likely, it was them. So ultimately, with the help of Three Part Advisors as well, we made a number of introductions earlier in the year. And then a number of parties were able to buy the complete block of Miami Jewish Federation. So we didn't have that cap anymore on our stock. There wasn't an active seller every time we hit $20. And then we reported earnings. We re-accelerated our growth. We reported 20% year-over-year growth in Q1, 30% in Q2, and then now in Q3, 20%.
So we're starting to execute that, and then we don't have that overhang as well.
Thank you.
In the back.
Said you weren't clear, and I went down to Radical. So I was thinking about the airport and the concert venue, but also TSA, [inaudible]
Yeah. So the question was the individual saw CLEAR at the airport what type of use cases, three-letter agencies at the, for example, TSA, right? So we don't name any customers. We have one of a really nice government contract in that space. We've gone to the three-letter agency, "Hey, we want to press release this." And as you can imagine, "Hey, you can't give out any contract details. You can't say this." So we're going back and forth.
Ultimately, we said, "Hey, what can we say?" and we're just waiting for them to say what we can say, and then hopefully we can press release that name, but yes, CLEAR is a good example, right? So all that's identity verification. Part of it, you enter your ID into the machine, takes a picture of you. That's partially document verification as well, so what they're doing is saying, "Hey, here's the details on the ID." That also has a barcode in the back, and there's companies out there that can decode that barcode, one, because they help build that barcode, so they have that proprietary data. So that's another type of data source, and they're verifying. They're reaching out to someone like us saying, "What's the PII information related to that individual? Does it match?" and they're doing that facial recognition as well.
So there's multiple identity plays from that perspective. Yeah, in the public sector, maybe call it maybe two years now, we won a four-letter agency, a large investigative bureau. They were doing an investigation for financial information. They needed data on all adult individuals in the U.S. We went head-to-head with LexisNexis. They were the incumbent. Ultimately, we went through a security process. They really liked what we were doing from a security perspective. And then the way we approach security is in the forefront, right? As you can imagine, we have all that data. So that's always a concern. Early on, we built it with security first. We had the same CIO for all three iterations from that perspective. And then so what they were doing in investigation, then we did a test file. We sent over test transactions.
They compared it to the competition, and they said, "Hey, you performed a lot better, so now price it." That was our first foothold into the public sector. We didn't have any insight on pricing. We assume we priced it not competitively, but we won that contract, and now we have an open PO, a five-year open PO. And recently, they came back and they said, "Hey, we have another investigation," and they can just come over, drop a file, and we don't have to go through that security clearance, that testing phase. So it's really exciting. Once you're in the door on the first one, it opens up that door. And it's, "Hey, you're not at the forefront." No one gets fired for using LexisNexis, TransUnion, right? And you're just going to renew with them. So now, maybe call it two years ago, we had a new hire.
I guess I'm expanding on your question, just giving a couple more examples. We had a new hire on the marketing side. Early on, we had no marketing, right? It was all outbound, not a lot of inbound. He came over from TransUnion. He led all the marketing for their risk side. And then he's getting this at the right conference shows, making that outbound marketing, and we're getting a lot more inbound now. One, because companies are starting to understand the name, see the name more often than not. And so we're able to get to testing. And again, once we're at testing, we usually win from that perspective on data accuracy, quality, and so forth, and even throughput. I'll give one more example on throughput on why we win, right?
So maybe about four years ago, one of our customers was going through a funding round, six-figure customer a month. TransUnion was leading the round. The CEO, Chris Cartwright, reached out to them, "Hey, we'll only participate if you stop using IDI Red Violet." And so ultimately, we're like, "Hey, we agree. I get it. You need the cash, but we're here if you need anything." And we saw that volume drop off for one or two months, and ultimately, that volume picked right back up. What they were seeing on their side was their customers were putting calls in. Calls into the platform were, again, dropped. The throughput wasn't there. They weren't even getting a response back. So the technology side was lacking. TransUnion said, "Hey, they must have built you a custom API." And it was off the shelf. They were up and running.
When they set up with us, they were up and running within 24 hours. So our API is very user-friendly, very customizable. So when someone comes to us with an item they're trying to solution, it's not like, "Hey, that's going to take us a month to build out for you." We're like, "Hey, no, perfect. Just explain what you need, and we'll use the API, and you'll be up and running within 24, 48 hours." So we see a lot of wins from that perspective as well, where our customers are out there bidding on contracts and so forth that they currently don't have the ability to solution, but they know they can come to us, "Hey, here's a unique way of doing that identity verification. Can you help us out?" And we can. And they're able to win business, and they're growing their revenue with us as well.
Any other questions?
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Yeah. So today we have about, sorry, the question was on the topic of M&A and then capital structure and how we would invest in that M&A. So today we have a really clean balance sheet, only common stock, no warrants, no debt. We have about $36 million in cash. That's not enough, right, to do a purchase, right? So when the time comes, when we approach something, it would have to make sense that we're going to have value-add. We wouldn't pay a higher multiple than our multiple. And ultimately, that will most likely be through an equity raise of some sort, or it's going to be a combination, right?
Earn out a stock sale as well, so on a performance basis from that perspective. But yeah, we wouldn't pay a higher multiple. Again, we don't want to dilute ourselves either, and we don't want to dilute our shareholders. Any other questions? All right. So now I'll give you guys back three minutes. I know everyone has a busy day. I sure do. Thank you for joining.