Good. Up next is our client, Red Violet. They are trading on NASDAQ under the symbol RDVT. Red Violet is a leading identity intelligence and analytics company that helps organizations verify identities and mitigate fraud and financial crime risk. Presenting on behalf of the company is Camilo Ramirez, Senior Vice President of Finance and Head of Investor Relations. Thank you, Erol. Thank you, everyone, for joining me. As Erol said, my name is Camilo Ramirez, SVP of Finance and Investor Relations. I had asked Erol not to schedule the first meeting of the day in hopes that everyone has had their coffee, ready to engage. I like to keep these very informal. Feel free to ask questions. I feel like they are much more productive in that manner, as opposed to just me lecturing to you guys up here. But yeah, so today, I will go over what we do. I will give you a little bit of management history, our business model, a couple use cases, and run through our financials and so forth. Excuse me. With that, so Red Violet, what do we do? We like to say we are all things identity. I will not really flip through a lot of these slides, but I will just stop on a couple. Like I said, it is much more conversational. I will just leave it on our company journey for the time being. So, all things identity, what does that mean? We say we are applicable to every transaction that occurs in the U.S. We want to understand the individual on that other side of that transaction. With that, management has been together for, call it, about three decades. In the late 1990s, they started a company called Accurint, ultimately sold that to Reed Elsevier's LexisNexis. I apologize. The product name is Accurint, company name was Seisint. Sold that off to Reed Elsevier's LexisNexis for about $750 million. Non-competes expired, got back together, started TLO. Ultimately, one of the founding members ended up passing away. His name was Hank Asher. He is credited to creating this space, what we like to call data fusion, right? Aggregating those disparate databases and creating that profile on that adult individual within the U.S. He ended up passing away abruptly. They were still going through the product development phase. They had not commercialized just yet. Ultimately, sold that to TransUnion for just under $200 million. Non-competes expired again. As you can imagine, technology changed drastically from the early 2000s to call it around 2014. There were a couple bidders on that second iteration that lost out, approached management team, "Hey, do you guys have another go in it? If so, how would you guys do it differently?" So, a couple of differentiating aspects was with the technology. You had the early innings of large language models. Everyone likes to think, hey, AI, this new feature today, right? New technology, but it is all based off those large language models that has been around for a number of years. We like to say our platform has been AI- embedded from the very beginning, a s opposed to our legacy platforms, they are built out in the data warehouse room, so they cannot scale up or scale down depending on peak productivity hours. I will jump into a couple use cases, kind of explain what we do. We aggregate those disparate databases. We say we have a 75-year longitudinal identity graph on the adult U.S. population. As you transact through society, you are graduating from college, purchasing your first home, purchasing a vehicle, getting a cell phone, and so forth, y ou are leaving that digital transaction within society, and we want all that information, aggregate that information, bring it in-house, create that profile on that individual. With that, we like to say we serve five verticals. Those five verticals, I will start with the easiest to understand. It is going to be collections. In collections, let us say you have a debt buyer. They buy a million records from Capital One. They need to understand right party contact information. Is anyone deceased? Because they are not going to waste their time on that. Has anyone filed for bankruptcy? Because they cannot call and collect on that, or they get fined, call it $10,000 per call. So, they will batch over that information over to us. We will append the information, send it right back. And how we win in that space? I t is typically the waterfall effect, right? With their files, they will go to their tier one provider. They will send those million records. There will be some fallout, call it 15%. They will send that 15% to their tier two provider. It has been scrubbed over. There will be some amount of hits that were unsuccessful. We will come in third tier. We say, "Hey, we have high confidence in our data asset." We will come in tier three on that data that has been scrubbed over twice and ultimately have a high hit rate. So, we will move up the tiers from that perspective, right? Because they want the highest hit rate at tier one, so they can have economies of scale, get the best pricing there. Second industry, we will go to investigative. In investigative, that is going to be like law enforcement, private investigators. On the law enforcement side, couple years ago, about two years ago now, we brought over Jonathan McDonald. He was basically credited with building the public sector revenue at TransUnion from zero to where it is at today. We built a team around Jonathan McDonald, and we verticalized that team from what we call Fed and SLED. Federal, go-get and then state, local, educational, and law enforcement on the SLED side. I will give you a couple use cases on there. Your most obvious one is going to be law enforcement, right? They need to understand the criminal actor, and there are a couple differentiating ways you can interact with our platform. One of them is going to be like our mobile application. There was an accident or some crime committed. A witness says, "Hey, it was a red F-150. I have a partial plate. I have two letters." Within our mobile app, you can basically just drop a pin, do a mile radius on that, and start entering Ford F-150. It will drop pins. Red, it will remove those pins that do not match. Here is your partial plate, and you are left with about, call it three pins, right, that match all that criteria. So, you can go investigate. On the law enforcement side, today, we are powering about 1,000 law enforcement agencies within the U.S. To scope out the space, there are about 15,000- 17,000 law enforcement agencies, depending on what source you cite. Derek, our CEO, just on our earnings call, announced that we won the largest law enforcement agency in the U.S. That is a well into six figures contract. It was a really exciting win. We won that actually from the competition. We came in, priced it. The competitor basically came back at the last hour, said, "Hey, we will reduce price for you. We will compete on price and beat Red Violet's IDI product." Ultimately, the law enforcement agency said, "Hey, we truly do not care about price. What we care about is accuracy and completeness." Ultimately, they signed that contract with us, and it is a multi-year contract as well. We are really excited they are proving out our data quality as opposed to, hey, we are not going to go compete on price, essentially. A couple other unique use cases on the SLED side are going to be homestead exemption fraud, right? You have individuals saying, "Hey, this is my primary home residence. I live in South Florida," and truly, they do not live in South Florida for the majority of the year. We are validating that that is their primary residence, or even on the educational side, it is going to be a residency verification. A lot of times, they will say, "Hey, my son lives with his grandparents who is in the better school district." You have overcrowding in certain schools, and then other schools, they are underperforming. Their attendance rates are down essentially because all those kids are going to the better school districts. These school districts will do address verification. Hey, is this child truly supposed to be at this school? If not, then push them back to the appropriate school and so forth to balance out that attendance across school district and they are not losing budget dollars for specific schools and having to close them down. Or even veteran benefits, something like states want to understand their inbound and outbound population of veteran for benefit purposes, because they get budget dollars based off of the usage there. So, reaching out to those inbound veteran individuals, educating on the services the state provides and so forth. On the federal side, we say SLED has been performing very well, been performing ahead of schedule from what we have laid out with Jonathan on the fed slide. We always get the question, "Hey, how is your federal business doing?" Today, it has fallen behind of that expected schedule, but I believe it is part of we did not appreciate that long procurement cycle. Some of these contracts are multimillion-dollar, multi-year contracts as well. You can take a look at some of those RFPs that are made public. What we have been seeing, some of those nationwide initiatives have been pushed down to the state level as well, so w e have been winning those state contracts as a whole. On the fed side, what we have seen, they are basically just pushing down the can, " Hey, we have this RFP. It had an end date. They have extended that end date," a nd so forth. But if you take a look at our pipeline today versus two years ago, that pipeline has grown orders of magnitude larger than we have in the past. And some of the contracts that we are getting in front, we would never been able to get in front of those contracts without the appropriate individuals. Some of these do not even go through public RFP. It is who they know, and essentially, it is an outreach program. They will reach out to certain vendors, say, "Hey, here is the RFP, bid on it," and so forth. We are really excited about the pipeline on the federal side as a whole. Third industry, I will go to financial and corporate risk. On the financial and corporate risk side, that is going to be like KYC, know your customer, background screening. So, background screening, good use case there that compares us to competition. Early on, we won a customer called Innovative from TransUnion. What they do, let us say you are Walmart, y ou have an applicant come in, they list four addresses. Walmart will send that to Innovative. Innovative will call out to us, say, "Is this data accurate and complete?" We will say, "It is accurate, but it is incomplete. They left off that fifth address." And that is usually where the criminal history lies. So, they will pull that criminal history, send that information back to Walmart. Ultimately, Innovative was purchased by Appriss. Appriss was then purchased by Equifax. As you can imagine, Equifax has way more data than us, significantly larger balance sheet. That Innovative contract was up for renewal. They came, said, "Hey, can we get a three-month extension?" I said, "We understand. We are here to help. We understand you are trying to execute on synergies. If there is anything we can do, let us know." So they asked for a couple more three-month extensions. Ultimately, they signed the long-term agreement because they could not produce the same amount of lift on their data as they were gleaning from our data asset. As you can imagine, we get data from two of the top three credit bureaus. You can assume which one we do not get data from based off our history. So, t here is potentially a vendor and customer relationship in this. So, we are consuming that data, aggregating, assimilating, creating it, and creating that identity graph, and then selling it back out to them, and we have expanded that relationship as well, powering some of their other products and solutions. Even though they have those individual data assets, I think that speaks volumes of what we do with the data. It is not quantity, it is what you can glean from those connections within all the data points. A bankruptcy on its own is just a bankruptcy. There is nothing sexy about it. You cannot glean much information. It is that footprint you leave through society. Fourth industry is going to be emerging markets. So, emerging markets for us is basically industries that we do not have a large footprint in today. You will have insurance in there. We gave a little bit of color that we hired an individual to run our insurance vertical. Historically, insurance has been a large revenue contributor to these businesses. Then, lastly, the last one is going to be real estate. I left real estate last just because we have launched a new market for the FOREWARN side. So, real estate, we serve in two fashions. On the IDI side, we serve it more for propensity to sell, so we power those prop tech companies. Give me a list of everyone that is 80 and above that lives in a two-story home. On the FOREWARN side, historically, it has been in the real estate side, going after associations, so it is a safety product. Let us say you are a real estate agent, you receive a phone call. "I am only in town for a couple of days. I want to see this $3 million home." You know nothing about that individual. They say their name is John Smith, purposely a very generic name. They say, "I am going to show up in a Mercedes-Benz S-Class." Sorry, if your name is John Smith and you drive an S-Class, you are going to be my use case today. Ultimately, what you do see is you do not see John Smith. You do not see that S-Class. You see, "Hey, this individual was just released from prison for sexual assault" or some extreme case from that perspective. What we saw, there was a lot of crime committed against real estate agents. They are going to empty homes. They do not know the individual. They have to pick up all the phone calls that they are receiving. We started going after associations so they can provide it as a member benefit for their real estate members. Today, we have over 600 real estate associations. There is about 1,100, 1,200 associations in the U.S., so we sit just over 50% penetration on the association side. About a month ago, we announced, "Hey, we are taking that FOREWARN safety application to the home healthcare space." On the home healthcare space, there is about 4 million home healthcare providers, 12,000 Medicare-certified practices. In that scenario, you understand who you are providing services to, right? You get an order from the hospital. It comes into your patient management system. These are the services you are going to provide to the individual. You will send someone out. But you do not know who else lives in that home, so there is a household risk. Is it an adult child with a violent background? Has there been crimes committed in the past? So, you do not understand the safety environment of that individual you are sending into the home. What we saw going through conferences, I was speaking with Josh Tilleson, he runs our FOREWARN department. What he gleaned, a lot of these practice directors, they previously were home healthcare providers but got out of providing healthcare services and just managing because there was a lot of uncomfortable situations that they were getting into within these homes. So, how can we provide this safety solution to those individuals? As they come in through their patient management system, being able to, "Hey, do that screening on that house. Is there a risk associated with this household? Yes. Maybe I am going to send two individuals on that first visit to assess the situation, or I am not going to send anyone at all." Right? "I am going to pass on that." What we see, there is a couple providers there. There is called [Solar Protect], [Home Safety], but they are very reactionary. The crime is already committed. It is, "Hey, hit a button. Something is occurring." That crime is already committed. A good example I use, I had to get physical therapy for my shoulder about a year ago. The lady, she provided physical therapy services. She came to our home, and obviously, based off of what we do, I picked her brain. I was like, "Hey, this is what our FOREWARN application does. How do you feel safe going into people's homes?" Typically, she said she only does females. She only came to our home because she had provided services to my wife post one of her pregnancies, and she said, "If I had a decent interaction with the husband, then, I'll come and provide services as well." When she goes to a home, she lets her husband know, "I'm going to be at this location for 30, an hour," whatever the time frame is. If she doesn't communicate with her husband within that time frame, he will call her, and if she doesn't pick up, then he calls law enforcement. In that scenario, the crime's already committed, right? The danger's already occurred. You don't have that safety aspect anymore. This is just providing that proactive safety solution for that space. We're really excited about it. We just attended one of the larger conferences about a month ago. We have a couple other conferences where we've been asked to sponsor and so forth and have really good interaction with different home healthcare providers. We'll attack it from the organization standpoint, that patient management system. As always, we'll have that inbound as well. We're excited about that home healthcare space. As we gain traction there, there's other face-to-face engagement industries as well, right? So, we'll start expanding that safety aspect to other industries as well. I'll pause there. That was a lot of information. See if there's any questions. If not, then I'll jump into like how contracts are structured, data costs, and so forth. Can you talk about your technology stack and what gives you a certain edge? Because you're competing against very large companies, some which, [audio distortion]. What is different about this [audio distortion]? Good question. Yes, s o the competitive advantages question, right? The main competitors are going to be Reed Elsevier's LexisNexis product, and then, TransUnion TLO product as well. Those, given on the timeframe they were created, they're all built in data warehouse rooms, as opposed to today, we were cloud native first. In one of the scenarios, I'll give examples of advantages of the platform as opposed to just me lecturing you. In one of the scenarios, there was a mobile authenticator. Basically, they're doing calls in. They're trying to clear an identity. Let's say, you're logging to Bank of America, Wells Fargo using facial recognition on your phone. What it's doing behind the scenes is saying, "Hey, this mobile ID belongs to Camilo. Here's the PII associated with Camilo. Does it match the bank? Yes. Grant access." In that scenario, there was a company doing a capital raise. TransUnion ended up leading that raise. TransUnion told its customers, "Hey, you have to move all your volume from Red Violet's IDI platform to our platform, or we won't close the deal." They let us know. We said, "Okay, we understand. We're here to help. If you ever need anything, let us know." We'd do the same if we were going to raise capital. Ultimately, we saw that revenue fall off. They were doing well into the six figures. We saw it fall off. Within a couple of months, that revenue came back. What had happened was their end user were pushing calls in, and ultimately, they were either getting false positives or not getting responses back, so that throughput wasn't available. It was just falling off. That's a testament to being cloud native. As customers are hitting our platform, we can scale up our data stack to support that additional throughput as opposed to the competition. They have an infrastructure, they're confined to that. Also, that also plays into all the AI initiatives going forward. So, how do you leverage these new technologies, new approaches to simulate and gather data when you're on an old legacy platform, right? We're able to leverage the cloud native first platform and then bring in new AI ways of gathering data. A couple examples of that is, let's say, obituary data, right? There's data out there where they're going to list son, father, cousin, second cousins, right? You want all those data points. You want to bring those in so you can build that family tree, per se, within that identity graph and understand it. You have AI going out, reading that long form data, processing it, and assimilating it into our platform. Or even like RICO complaints. You've known two bad actors are always associated. There's a RICO complaint that lists three more. So, reading that RICO complaint and being able to simulate those connections and building out those profiles, and e ven, so it gives you a scope on size, right? Today, we have just over 10,000 customers. Reed Elsevier's LexisNexis, excuse me, has about 400,000 customers. On the TransUnion side, they have about 60,000, 70,000 customers. We're only in the early innings from that perspective. We like to say we have a $10 billion TAM. Call it, we have one very small portion of that today. What prevents me from, if I, presumably, if I had access to all the raw data [audio distortion]? Yep. [audio distortion] It's a good question. [audio distortion] Yeah. So, a good AI defensibility question, right? It's not just a data question, it's what data do you go out and get. It's having that institutional knowledge on what data assets actually matter. In that Equifax example, they have significantly more data, but they can't clear that identity how we're clearing it, right? We have significantly less data than they do. It's what you do with that data and how you simulate it. So, early on, all three iterations were built with proprietary languages. So, this iteration was in identity resolution, so we call it IRON. Basically, if you go out there, a nd one of the reasons we used IRON is because C++ JavaScript doesn't have the computational power to be able to consume this data, simulate it, s o we've used this proprietary language so we can go out, gather that data, assimilate it, and create these profiles. Then, another competitive advantage on that standpoint as well is, historically, in the past two iterations, if there was a new data scientist coming on board, they had to spend their first six months understanding how to code this language, and then they can go out and try to start adding value. Today, we learned from our past experiences and said, "Hey, we don't want these data scientists to just burn six months trying to understand how to code. Let them code in whatever language they want, and then it'll just drop down into IRON, and they can hit the ground running as opposed to spending their time learning a language." It's also a data stack, right? How do you keep this data safe? It's not like you and I can go to one of the credit bureaus and say, "Hey, give me the entire U.S. population." I want that in-house, right? Because that's a big liability for them. So having that relationship and that confidence, we get audited by our data providers regularly, at least once a year, if not more, depending on the data asset. We're SOC 2 Type 2, PCI Level 1, and so forth, ISO 27001, and whatnot. So, it's how do you keep that data safe? Our tech team, our CIO basically explained it to me. We have our data stack, right, and t here's no internet connection within that data stack. So, it's how do they come in? Someone's making a call in. It'll sit out here. We'll push that information out, and they'll go out. So, we're trying to control that potential of breach, right? Because we have this entire U.S. population. So, we run breach tests. We have external parties trying to breach our platform. We have automated ways of looking for irregular activity. Let's say you have an online account that's pulling in in a fashion that's not natural for a human being, that account will be automatically shut off, go through our compliance, and they'll have to explain what was going on, and then we'll make a decision, do we turn them back on or not? I believe you had a question as well. Yes. [audio distortion] these different [audio distortion]? Yep. In the waterfall effect. Yep. [audio distortion] Yeah. So, moving up tier within the customer base, I'd say about 18 months ago, we had an initiative, "Hey, let's move up tier." Early on, I'd say, call it seven years ago, is how do we get to GAAP profitability? So, it was going after those low-hanging fruits. Small business owners, they're very price-conscious. What we see in the space is the competitors will give you a menu of products and they'll say, "Hey, here's product A, here's product B, and product C." But product A has too little, product B has too much information. But this platform, it was very data industry agnostic, so we can turn on and turn off those levers. So, we're not competing on price, we're just providing them what they actually need. We're not providing them additional information that's not used. The end user is seeing price savings, but we're not discounting our price because we're giving them exactly what they need, because we can turn off those additional levers. So, that's initially how we went to market to get profitability. As we started maturing, we said, "Hey, we're going to move up market." We disclose this figure every year at the end of the year. Last year, we had, I'd call about just over 120 customers that were generating over $100,000 a year. Prior to that, it was around 90. Prior to that, it was around 70. So, we've seen really good escalation there. And it's not just that tier, we're seeing across the board. Internally, we track a million plus, 500+, 100, and so forth. So, we're seeing expansion across all tiers and we're seeing that in our customer adds as well. We're seeing those larger customers, but we're also getting a lot of that inbound on those smaller customers as well. We're getting play in all the respective tiers. And now with AI, we're seeing some of these customers become much more astute because you can access our platform in three different fashions. You can access it through online. "Hey, I need to understand Camilo Ramirez." You'll do a search. You have some known information, you'll get results back. You can batch or you can do an API connection or high volume. We say we power seven of the top 10 identity players today. We call those orchestration platforms. They all have their unique way on how they access [inaudible] identity, but they don't own any of the PII. So, we're powering seven of the top 10 identity players today. Without naming any customers, the likes of Prove, Jumio, Ekata, now owned by Mastercard, ID.me. ID.me plays in the government space, Prove does mobile authentication. Some of them do document verification, take a selfie, take a picture ID, but they still need to understand the PII behind that. We've seen really good success in powering those identity players. So, as they grow, we'll grow as well. And they'll be expanding into different new verticals, right? Ten years ago, Uber was a new thing, maybe a little longer at this point. Uber was a new emerging technology. So, how do they understand doing those background screening on those individuals? Buy now, pay later. Understanding that that John Smith is the John Smith applying for the application or even, let's say DraftKings, right? You know this mobile ID, you need to clear the PII behind it, understand, hey, is this John Smith tagged to this mobile, w ho owns this mobile ID, and is that the appropriate identity, even from geolocation data? Today, we don't have any geolocation data. So, is that cell phone within the area that's legal to gamble, right? So, understanding where that cell phone resides. If there's no other questions, I'll jump into the business model. I think it's important, right? We like to bring in all data assets in-house, right? We do long-term contracts, whether we use that data asset once or a million times, it doesn't cost us anymore. We like to say it's a fixed cost model. A couple of years ago, we got a lot of questions from investors saying, "Hey, what do you guys look at at X number?" We said, "At $100 million, our gross profit margins are going to be around 80%, adjusted EBIT around 40%," right? We got a lot of pushback. Is that truly achievable, right? And it is, because it's our data cost model. If you look at our 10-K, you'll see about 40% of that cost associated with one vendor. It's not just one data asset, it's multiple data assets, and it's just been the relationship that the team has had for multiple decades. So, as we go acquire additional data assets, we'll go to them. Hey, do they have this data? We have economies of scale there. We have a really good relationship. We just renewed that contract a little less than a year ago. It's a five-year contract. It renewed relatively flat because we always get the question, why wouldn't they escalate you? They see the margins you're generating. Why would they keep giving you this price? Ultimately, it's just commodity on their shelf. There's not many companies that they can go out and sell the entire U.S. population to. If they escalate that price, we'll just bring our tier two provider. We're multi-sourced on every data asset. We want as many socials, as many data bursts. We want those transposed socials as well, right? So, we can understand a lot of government entities, they'll fat finger numbers. So, how do you get back to that right identity, even with that transposed social? We want high confidence. If they escalate us, we'll just give those data points back. But all the learnings that we glean, that's proprietary to us, and that's the true benefit. It's those connections, not the data points. Keep going down the P&L with revenue. Most of our contracts are 12-month contracts with auto renewals. They're going to be usage-based. So, we're a usage-based business. There's certain industries that they have seat licenses, like law enforcement. It's still usage- driven, but they contract on a seat basis because of their budgets and so forth, so they need to be very predictable. But the majority of our revenue is usage- based. So, for, call it for $10,000, you'll get 20,000 hits. Once you go over that, then it goes into an overage price. If you're continuously in overage, our team does a really good job in upselling those contracts. Hey, let's get into a larger minimum commitment. We'll give you some cost savings on that overage, and everyone wins. Then, that revenue becomes very predictable. About 80%, just under 80% of our revenue is contractual. We spoke about data costs and then moving on to like sales and G&A. From a sales perspective, most our variability is going to be commission- related and headcount- related. Today, we have about, call it around 60 sales individuals, around 40 inside sales, call it around 20 outside sales. Our sales teams are verticalized based off the industry. So, you'll have a collections team, real estate team, legal team, public sector team, and so forth. Every industry has a unique way of how they conduct the business, how it's sold into. On the G&A side, we even do long-term contracts on a lot of our vendors there, just to make it very predictable. As you're modeling out, it's going to be headcount, and then revenue, adding customers and so forth. Pause if there's any questions. I'm sure as you guys were looking Red Violet up, you probably saw we just did a capital raise. We raised about $115 million about three weeks ago. All equity, no warrants. It was a really successful capital raise. We're oversubscribed orders of magnitude larger than the $100 million. What we saw there, we had a really high hit rate on the engagement there, call it around 80% hit rate from an order place. Why did we raise capital? We have a really initiative in our M&A. From an M&A standpoint, we look for three things: unique data asset that we can bring in, cut out some of our data costs as well, so continue to expand that leverage. Unique technology. Are they acquiring data in a unique way? Are they providing services in a unique way? It becomes a buy versus build decision. Or go an adjacent technology or going downstream, right? We have a customer, they are using our data, and they are providing services to the end user, right? So, we can come in, take out that platform, gain that technology, cut out that data cost, because we can bring in our data cost. Now, instead of getting wholesale rates, we will get retailer rates, so you get lift from there as well. We brought on Stephen Chang from Equifax to lead our Corp Dev function about six months ago. I think if you look at our filings, you will see some acquisition-related costs there. We have been very active in this space, looking at different potential opportunities. We like to say, "Hey, if something doesn't make sense, even though we spent a good amount of time and dollars, if there is a red flag towards the end, we will walk away," and we have done that in the past. What we saw late last year, there was an opportunity, and there was a couple of suitors for that opportunity, and the bankers basically said, "Hey, how are you guys going to fund this?" We had just under a $50 million balance sheet. You are not going to liquidate your entire balance sheet. You are going to have to go raise capital. Can you guys raise capital? So, our offer was discounted because of that, right? With this is, hey, we now have that balance sheet to support these acquisitions. We no longer have to say, "Hey, we are going to offer this, but we are going to go raise capital." We have the powder to go and execute on that. Also, power our organic, right? Continue to invest within the teams, the sales, and go to market. I guess, I will leave you guys, we got two minutes, I will leave you guys with a couple future states. On one of the last calls, we said, "Hey, what does this model look at maturity?" From a maturity standpoint, gross profit margins exceeding that 90% mark, adjusted EBITDA, 60%-65% adjusted EBITDA, right? Because it is that fixed cost model, and we say every additional dollar brought in today is nearly 100% contribution margin. Any last-minute questions? All right. Thank you, everyone. Appreciate it.