Well, good morning, everybody, and thank you for attending the Midwest IDEAS Conference, hosted by Three Part Advisors. Up next, we have Red Violet Inc., traded under Nasdaq symbol RDVT. On behalf of the company, we have Derek Dubner, CEO, Dan McLaughlin, CFO. Just want to let everybody know that both of these guys have a really strong history with this type of business. They're a very seasoned management team. They've started two similar companies before this. Some of you may have heard of TransUnion. So Derek and Dan? Good morning. Thank you all for attending. We appreciate it, so this management team, why don't we talk a little bit about our history, as he just referred to, and what we do and what we are now doing. This is the third company we've created as a team together. The management team has been together for the better part of 25 years. It all started back around 1998 when members of this team got together and built our first company, and what we do is, we believe, rather uniquely, build a technology platform that can assimilate massive amounts of data. Think of billions and billions of data points, both structured, so think of from a provider such as a credit bureau, very structured data, to very unstructured data, maybe to a very select niche government source. And these platforms can take that data in, fuse that data together and assimilate that data and create a comprehensive picture of the U.S. consumer. And we brought our first product out back around 2000, 2001, built off of this platform. That product was called Accurint. Accurint for accurate intelligence, and that product was brought out to law enforcement and government for investigations and insurance for anti-fraud activities. It was quite a game changer. As you can imagine, these were pre-Google days, so the ability for a law enforcement officer to access a system online, type in my information, and get a 20- or 30-page dossier on me brought a six-month investigation down to a subsecond investigation. Think of originally public record data, so the footprint that we leave in society, bankruptcies, liens, judgments, criminal histories, marriages, divorces, businesses we form, assets we acquire, and where we divest those assets to, but then the fusion of this data, creating connections that are otherwise unattainable between people, businesses, assets, and these interrelationships that I'm speaking about. Ultimately, we opened up these products, this product, Accurint, for general risk mitigation, identity verification, due diligence. Certainly there's an extraordinary secular tailwind today in these areas as everything has moved online. But we have been in this business for the last two decades, as I've mentioned, and it's always been about understanding counterparty risk. We say the solutions we build are applicable to every single transaction here in the U.S., because there isn't a transaction you'd enter into without understanding who was on the other side of that transaction. That product was very successful. Ultimately, we sold the company that had that product to Reed Elsevier's LexisNexis in 2004. Some five years later, 2009, the team got back together. What we watched during that time period was rather extraordinary in the way of the volume and velocity of data that came about. As we all know, mobile, e-commerce, social media changed the landscape around data around the U.S. consumer. We also watched technology evolve significantly, as it always does, and we watched as Reed Elsevier's LexisNexis take our technology or our platform, if you will, to power many, many, many solutions across their own enterprise. We do build these platforms to be enterprise-adopted, and that's exactly what we would expect, so we got back together again in 2009 to create, I guess you'd say, the next better mousetrap, and so we set out to build our platform once again. Important about this model, it's a fixed cost model, so understand that our costs of goods, our fixed costs, are building this platform and acquiring our data to build this holistic view of the U.S. consumer. Originally, the first two companies were always constrained to a large mainframe architecture. So think of thousands of servers in a big data room, raised floors and teams around that. As you can imagine, the first two years of this fixed cost model is very capital intensive, with revenue way out in the future. And then we flip to commercialization when the depth and breadth of our data is ready, the platform's capabilities are ready, and we go to market. So the other fixed cost, other than the platform, is data. And, it starts with us licensing data from some of the parties out there that we have built multi-decade relationships with. And, the key part of that licensure of data is we want all of the data in-house. We want to bring it into our environment because that's where we run the analytics, our core competency across data. It's not just interesting to know that Derek Dubner has a bankruptcy record. Anybody with a little bit of work can go find that bankruptcy record. But what is interesting is perhaps a Fortune 1000 company monitoring an employee in their finance department who spends $1 million a month with a vendor, and our system's alerting that that vendor has a brother-in-law of that purchaser as an officer of the company. So as you can see, there are multiple connections from purchaser's company, individual buying, to vendor company, back down to officer. And that would be a sort of a risk signal, if you will, that we would be alerting to. When we go out and we license this data, we might go to a credit bureau for, not their Credit Reporting Act data, but more or less credit header data along the top, just general name, address, date of birth type data. And we would also go to them for other data assets, that we might find useful in building our products, and we would license that data from the credit bureau. Think of a five-year term contract. Again, the key is unlimited use of the data, where we bring the data in-house. We are, we believe, among a very few select group of parties that can demonstrate the security and infrastructure necessary to give comfort that we can bring that data in-house. And, really, that lends the fixed cost model. You know, we pay for this fixed fee over this five-year contract, and as we onboard customers and as they access our systems and look up a subject over and over and over, for whatever purpose it is, really today, every dollar is 100% contribution margin. With this model, generally, due to the evolution of our businesses, the prior two businesses that I've just spoken about, gross profit margins in excess of 90%. You'll see with us, just due to the evolution of our business, this business here today, we have driven gross profit margins from low 60s% to now about 80%. And so, you know, it, it's a wonderful model with extraordinary operational leverage. So after we build the platform and we acquire data assets and we release our products, then we have, quote, "flipped to commercialization." And ultimately, we sold that second company to TransUnion in 2013, where the CEO, a newly appointed CEO of TransUnion, then backed by Goldman and Advent, a private company, acquired our second company. He had just left LexisNexis Risk Solutions, where he was the CEO there, and wanted to acquire this next-generation platform that we built and stated unequivocally that he's taking our technology, our platform, to, their large enterprise customers. So we were approached by some investors from the prior companies to see if we wanted to get the team back together and build another company, 2013, 2014. And we sat down, and we said, "Well, what would we build? How would we be different? What is the competitive differentiation against the legacy two products and platforms that we built?" And once again, we saw an enormous opportunity. Hindsight is 20/20. Who wouldn't want the opportunity to build something again, solving for perhaps, any of the difficulties that you encountered prior? We have always had customers that plug into us via API connection, computer to computer, and they might run millions of identity verification transactions in a sub-second fashion. So if we experience some latency in the system at those prior two companies, how would we solve for that? How would we solve for better accuracy of data? Where would we be? Would we be fighting against, these legacy companies that are much larger than us, with the same customer base, or who would the customer base be, and what do we look like ten years from now? And there was enormous opportunities to get back into the marketplace. First, we would solve for that latency and scalability problem by building this platform from the ground up in the cloud. As we all know, the cloud is a very big deal. The cloud has changed technology dramatically, and we built this third-generation platform from the ground up in the cloud, and it is demonstrating the scalability that the legacy platforms have never demonstrated, and we're seeing wins with our customers who are understanding that and running massive transactions through us. How would we compete against much larger companies in data? They have all the data in the world. They have all the money they need. So how would we win on data? We wouldn't be selling or giving access to more data. We're going to give access to better data, so we need to win on accuracy. And we knew that the customer base we've always served was complaining at that time, "I'm paying for more data that I don't need. It's inefficient, it's getting in my workflow, it's slowing us down." So how do we function? We wanted to solve for that. Machine learning. We built those first two platforms in 1998 and 2009. Machine learning was more a term in academia, and today, obviously, game changer. The very same architect, by the way, on this team that built all three platforms, built our third platform here and said, "We will build this one with machine learning, and we will therefore be that much better in what we build." Machine learning. We looked at the use cases of these products, which the users of these products, just to give you an idea, as I've mentioned, a few, law enforcement and government for investigations. Banks now, since 2004, in the Patriot Act, Know Your Customer for every account opening. Retail, for point-of-sale verification. When somebody transacts online, are they who they say they are? Law enforcement and government also for benefits. Somebody filing a claim at the government site, for some type of benefit, there needs to be identity verification, and so we are part of that process around identity verification. Law firms, understanding litigants, counterparties, jurors, et cetera. There really are many, many, many use cases and many users of these systems, from the very small, simple use cases, a small collections firm to find a debtor or an asset, a bail bondsman, a process server, as I mentioned, a small law firm, all the way up to large enterprise. Jumping ahead a little bit as to where we are in our evolution, and we will talk about that a bit. We are most proud today, given where we sit, that we are powering seven of the ten leading identity verification platforms out there. These are private companies for the most part. They've raised capital in 2021, with high growth rates and large TAMs, and they are running verifications for the private sector and the public sector. What is unique about us is that when somebody goes and transact in a private sector or files for that government claim, the government or the private sector may be contracting with one of these identity verification platforms. And we don't really name customers, but the cohort of these identity verification platforms are names you may have heard of: Socure and Trulioo and ID.me, Jumio, Ekata, now owned by Mastercard, Prove. These are identity orchestration layers or identity verification providers who each perform identity verification in their own unique way. One might use a mobile device ID, one might use location of that device. For example, for a state's legal sports betting, understanding somebody's identity and in that state. So many, many use cases, but in the end, they need to understand, is it John Smith behind that device or behind that transaction, or is it fraud, and so these orchestration platforms are hitting our systems to clear that transaction, and another differentiator that we built this time around is that when we were building our data assets around the consumer, it wasn't just about public records, the footprint that I said leaving in society, but we also wanted to understand more about the younger cohort, the 18 to 24-year-old. These are the up-and-comers in transacting in society, and they're often underrepresented and underbanked at the credit reports or, credit bureaus, excuse me, and in any other transactions. They move frequently, they change their cell phones, their IP addresses change. So we wanted the ability to be able to clear those transactions, for whatever it may be that they're applying for. So early on, we made a couple of acquisitions while building out this company. 2014, we made a very small acquisition when we all got back together, a small company called Interactive Data. It was only a $3 million-a-year revenue business, but it was serving accounts receivable management or collections, if you will, which is always a customer base of these type of systems. They had a terrific security and infrastructure in place with contracts with some of these data providers that we would have to forge. So that accelerated our path. We set out, and we built our technology team in Seattle due to a very deep talent pool out in Seattle for our development team, and we set out to build our platform around 2014, 2015. At the time, one of our funders, one of our investors, suggested that we reverse into a public company, an investment he had, and thought, "Wouldn't it be great if you built a multi-billion-dollar company as a public company this time, as opposed to being private?" Well, we knew we'd be a very unattractive public company for the first two years due to the spend and due to the, you know, lack of commercialization. However, it was wonderful to have restricted stock units to attract talent, as I mentioned: the Seattle technology team and other areas of our organization, and then back to what I was saying: we made some acquisitions while building what would be Red Violet in the marketing arena. Two reasons for that. One, we were going to bring our products this time not only to risk management, but also to marketing services, and by that, I don't necessarily mean marketing or mail pieces, but in the marketing life cycle, as you know, the entire customer life cycle is identity verification. When somebody signs up and they're a new customer, are they who they say they are? Now, I've seen them again, come back and purchase something. Simple use case, why is that address where they're drop shipping this large screen TV, an address never associated with that individual? Red flag. Or why is that address now in the last 24 hours associated with 25 individuals? Another red flag. Fraud, red flag. So, you know, so we acquired. We wanted to bring our products and our solutions into the marketing service arena, where the TAM is much larger, and so, and also sort of a differentiator at the time of where we had brought these original products, so we made some acquisitions in the marketing space for that entree into marketing, but also because these marketing companies were engaging with the eighteen, 20-24 year-old consumer. That is the type of cohort of consumer that engages with these type of companies, and so we found a treasure trove of data there to bring into our system, our infrastructure, in order to clear these future transactions for that cohort, so we made those acquisitions. We reverse merged in 2015 into a company on the NYSE MKT, and we moved to the Nasdaq under a holding company called Cogint, C-O-G-I-N-T, for cognitive intelligence. Under that holding company now was IDI, which is the small company we bought originally in that accounts receivable management and now all general risk management space, and a marketing company we acquired. So two separate subsidiaries. As Dan, our CFO, and I went out to tell our story a bit, we were looking to institutionalize the base. We were looking for institutional investors that understood a long-term view, as we have, of what we are building and how dynamic and incredible this model is. What we encountered was confusion. We encountered institutions saying, "I love the big data and analytics space. I love the model you have, the 80% plus gross profit margins," or, "I know marketing, and I know this space," and so therefore it was getting confused, so we knew that when what would be Red Violet was ready to stand on its own, it would need to be an individual company and in 2018 we were ready. So we took $20 million from that company, which would get us to cash flow positive, and Red Violet spun off. Today's company is a brand new initial listing from 2018. The marketing company is still trading out there on the Nasdaq with no affiliation, so when we set out to tell our story again, we actually said, "We're not even going to do earnings calls in the beginning. We're not going to do any investor relations. We're not going to do any non-deal roadshows. Let's just stay focused on our business. We have a long-term view, and let's build a very healthy foundational business and demonstrate the leverage of this model." And we did that, and we started to do that in the early innings. We were very fortunate that some top institutions reached out to us. We hit their radar screen, and they took some positions. And so what you've seen since that time, 2018 or so, is very typical of our roadmap. The early adopters of our products are small to medium-sized enterprises. So I've named a few of those: small collection firms, small sheriff's offices, small law firms, bail bonds, process server, repossession. Again, it makes sense. If you have as competitive a product as out there and you're at a good price point, they understand it's a simple use case. Those are your early adopters, but then we were ready to go to larger enterprise. It was time. Breaking into public sector is a big endeavor. These legacy companies that we've sold to in the past are entrenched. They've been there for the better part of 20 years, and it's also a long process to deal in government, as we all know. In the private sector, it was time to move to larger enterprise, which, of course, is a longer sales cycle. In the last two years, with the benefit of being cash flow positive and GAAP profitable, which is a wonderful place to be, especially with what small and micro-cap have endured over the last eighteen months, we've been able to invest in our business and our people, and we've brought on some key thought leaders in various areas of our organization to do exactly that, break into larger enterprise. Some of those exciting opportunities, we brought a fellow over from Acxiom to run our marketing services journey, if you will, the things we've talked about. During that eighteen months, we brought in a bunch of new data, a healthy amount of new data, to integrate into our platforms around the consumer demographics and likeness, likes, and the like, and interests, which has gotten us ready to enter the marketplace and the marketing services journey. We also power background screening support, as you can imagine. In that scenario, somebody applies for a job, and the company they apply to needs to understand who they are for background screening, and they might contract with another party for that service. We are very much behind the scenes, solely in the support realm, not in the decisioning realm, but we will provide information around John Smith. Here are two aliases John Smith left off. Here is the address history of John Smith. Here is a county where there's a criminal record, and that county's been left off the application for obvious reasons. So we've had great success there, and as opposed to being behind the scenes, which this company, up to date, has been very much behind the scenes, in many areas that I've spoken about, we are finding ourselves where the customers are approaching us and saying, "I understand you're powering various platforms out there for various use cases, but I'm also trying to solve for X within my organization, which is a complex problem." And so we're very excited by that because we have this dynamic platform that's very extensible to solve for many, many use cases, resolving entity. Entity meaning people and businesses and assets, as I've said. We believe we are rather special in entity resolution, and we also believe that we have created this core consumer identity graph that is key for any organization to access and find out an individual for identity verification and risk mitigation. The beauty of this is sort of think of this flywheel, where this core consumer identity is inside, and you can enter our platform or our matrix, if you will, through any area, through a property, through a phone number, through a vehicle, whatever it may be, to access data for whatever your use case may be. Another example we're very excited about in serving law enforcement. We have been focused the last eighteen months on bringing on law enforcement users for their investigations. We have introduced some new functionality that is rather fantastic, that they're really enjoying in a geospatial search, just to give you an idea of some of the capabilities of the system. Law enforcement would log on. Law enforcement understands that there's been a crime in Jupiter, Florida, and all they know, it's a black Ford F-150, so they can literally put Jupiter, Florida. They can pull whatever radius on a map they want, five, ten, fifteen, fifty miles, and run a search on those Ford F-150s, and we'll drop pins. They also know there's a tag with X and A in it, and of course, those pins will drop down to basically one to five, and any other detail they can provide will give them what they want. These type of searches or new ways of accessing the system, other than just putting in a Social Security number, or a name, or a relative's name, or whatever it may be, are really dynamic, really incredible, and we've had great success adding law enforcement users over the last 18 months. We're very focused there, so we are very excited about where we are as a business. There are three ways to access our systems. One is online, an online application. We only sell business to business, so when we credential a business in order to give them access, are they who they say they are? Do they want sensitive information? We'll even send out a third-party firm to visit their location and make sure they are who they say they are, and that that person did apply for an account. We'll open that account up, and they can access it online, so with. They'll get credentials with multifactor authentication and access our system, and they can run whatever searches they may need to do. Think of a collection firm with 50 seats, and they will contract and put those collectors on that. Think of a compliance department at a bank. Think of a law firm or a legal department within a large retailer. You name it, there are many, many use cases, and they can access online. Batch is another way to access our systems. The most easily understandable use case there would be a debt buyer who might buy a lot of bad debts from a large financial services company, and they'll have millions of consumers, and they'll need to understand more about those consumers. So they will give us a batch file. They will transfer that electronically, and through an automated process, we will cleanse that file for them. Most importantly, we'll let them know if somebody's just filed a bankruptcy or has a bankruptcy, because then it's against the law for them to contact that individual in order to collect. But then we will identify deceased to get it out of the inefficient workflow, updated information, updated contact information to make it a better process. And lastly, API connection, computer to computer, as I've talked about. That really is our bread and butter, and where we really, really, really shine against the competition. We have these out-of-the-box APIs, but they're rather customizable for us, very simply, and it's very important for our customer because customers have their own requirements around API functionality, and we can get them up and running within 24-48 hours, where they're driving tons of volume, heavy volume through our systems, whatever their use case may be. We have two brands today, generally known out there in the market. The first is idi CORE, and that really is our general purpose online investigative system, where you can access online batch and API. It's well known there, idiCORE. The business overall, let me just step back, is 80% contractual revenue, 20% transactional revenue. The contractual revenue is very often made up of some minimum usage within the contract at some price point, and then overage above that at a higher price point, and it's very important to us to build goodwill with our customer. Again, a differentiation. We want to know right away from the prospect, what are they trying to solve for? Because this matrix over our platform is so dynamic, we can literally turn dials, suppress certain information if it's federally regulated and using it for marketing, open up certain information where maybe a bank needs to know your customer. It's very important for identity verification. Maybe turn off certain data where they don't want certain users to be able to see that data, or we perceive that certain users within the organization doesn't need to see certain data. So it's a very dynamic platform, one for many, many use cases. And so, that would be sort of the contractual. We want to understand what do they want to use it for? Once we set them up, And the beauty of that is the customer often says, as I mentioned, "The competition is giving me a menu of products, and product A is much more data than I need, and I'm overpaying for it. Product B doesn't solve for what I need, but I'm told that's what I get." We don't want to do that. We want to be within what do they need, and if it's somewhere between A and B, we will price for that. They love that. It's a wonderful relationship, and then right out of the gate, we're monitoring how the transactions are going, and we're checking in very regularly, not only when the contract renews at the one-year anniversary, and we talk to the customer, and we say, "We see how you're using it. You're doing wonderfully, but you're hitting your overages. Now, if you up your minimum, your price point will come down, therefore, more efficient for you, better for us in the way of visibility, great relationship," and they love that, and so the beauty of that is we continue to build those relationships, and we're excited by that, and often with larger customers, they will start with smaller volumes, of course. They're not going to shift all of their volumes over to a new entrant, but they're going to start with smaller volumes, and then as they see the proof points, they're going to up that volume then. So that's where that relationship really comes in very strong. So we've talked about the three, idiCORE being the core product, and then another brand, we introduced several years ago. We were looking at the real estate industry, and it's a rather unique industry in the way of risk. It is one of the few industries where somebody in the real estate world is demonstrating success by showing up in a very expensive car. They're dressed very nicely, and they show up at a house after somebody calls them and says, "I'm in town for two hours, and I'd like to see this $3 million house. I'll see you there." There are very few transactions in the world where we would want to walk into an environment where we know nothing about an individual. So when that number comes in, we've introduced a product called FOREWARN. And FOREWARN is an app-based solution powered by the very same platform that powers idiCORE, and FOREWARN is identity verification on your phone for a safer engagement. So when that number comes in, you can immediately say that, "Yes, it is confirming John Smith, as they say. John Smith tells me I'm going to show up in a BMW 3 Series. Maybe that checks out, but John Smith has a criminal record and was just released from prison." So it may be that I wouldn't go show that property, or it might simply be, it's not that clear, but I'm going to call back to the office and have another agent meet me at the property. We've had incredible testimonials from agents around the safety of this device, creating safety for them, and that they would never walk into a home again without understanding who just contacted them. We don't sell that individually, necessarily to realtors. However, if they call us, we're happy to sell that to them for, you know, $20 a month, for example. But we sell that to real estate associations, and we are now over 450 real estate associations who appreciate the value of this product to deliver that product for a member benefit for their members. And so FOREWARN is a fantastic product. It is 100% contractual, very SaaS-like, while the rest of our business is rather SaaS-like. It's not necessarily recurring, but it's reoccurring because even the transactional business that we do, once we are ingrained in your workflow and they appreciate what it's doing for their business, it's highly reoccurring. Can I open up to any questions? Sure. You know, Palantir, I feel like a lot of people invest in them. They don't, but I can't ever figure out what the heck they do, or maybe a competitor with you guys or no? I guess technically they could be a competitor because what they're doing is entity resolution around data for government or the private sector. You know, government, for example, has very siloed data assets. They have multiple three-letter agencies, and so you'd want to understand they're the same person. We, however, have a platform very similar in that context of entity resolution around siloed data. We have a lot of the data in-house, whereas I believe they are primarily platform over the end user data. For us, we identified very early on that that would be sort of a long-term view of what we could be doing with licensing our platform for those purposes. We have a long history there, where we've heard from an author of a book around this industry that has heard that when our first company was used by the federal government, after 9/11, our system was used to find accomplices to 9/11 and the Beltway sniper. That at that time, there's a common overlap in the sort of the construct or the thinking around these platforms born from Palantir. So kind of a competitor, but generally, our competition today is TransUnion, LexisNexis, and Thomson Reuters, who also have similar products in this area. I would assume your data costs are quite large, acquiring data, probably now. Are you moving towards gathering and your own data, acquiring data from third-party data? Yes, it's a great question. So that is correct. We spend about $10 million a year licensing data. But that said, from the early days of this business, as I said, we wanted to build that 18- to 24-year-old cohort of the database. We made some acquisitions in the marketing space, where we built a very large proprietary data asset, which we own. Now, when you take all that data together with the analytics that we perform over this data with our platform, we create connections, as I mentioned, that example for Fortune 1000, that is otherwise unattainable. So if we license a data set, think of a list of bankruptcies just to put into the algorithms. If that license were to end, we simply give that data set back. And what's important about this is we want as many data sources as possible as confirmatory data around a data point. So a Social Security number, a date of birth, an address, a phone number, all confirmatory. So if a license were to end by our choice or another's, we would simply give that back. When we think about this model, the really simple way to say it is data is carrots and onions off the shelf when we go license it. Now, it's proprietary in that we know who the key vendors are, that we can build the best holistic view of the consumer with a 30 year history. So we know who to go to, but carrots and onions off the shelf, replaceable for many, it's a commodity. It's what we do with the data to create the view of that consumer that is the best beef stew out there. And so really, that's where we want to win in the identity verification. It's not okay that a provider says, "I don't know who John Smith is." No, we want to clear that transaction. Or better yet, we want you to know John Smith is the person who filed a fraud claim with the insurance company ten years ago and burned that company. That is the same John Smith, or that is the 19 year-old that you haven't seen before or others out there haven't seen, but we can tell you, clear that transaction, make commerce available to all, and that 19 year-old shouldn't be penalized for being underbanked at that time, or even the 13 year-old who is underbanked. That person should have access to everything that's out there in commerce. Yes. Could you talk a bit about, given the fixed cost nature of what you have, your plans for scaling up idiCORE for one house, how rapidly could they grow? How, I don't know, will the company be three to five years from now? Sure. Dan, our CFO, would you like to comment on that? Yeah, sure. So, as Derek said earlier, highly scalable model, especially at the gross level. Every incremental dollar today is basically 100% contribution margin. Flowing that all the way down to the bottom line, really the main cost data we talked about, cost of revenue and then personnel costs. And this model normally goes in kind of 3-4-year increments, where we invest in personnel and then leverage that for the next three or four years. We did that in 2022. We brought in about 50 new team members, mostly on the technology side. So now we'll be able to leverage that P&L for the next call it three or four years. And what these models look like at scale, Derek gave you a little bit on the growth level, but what we've kind of given publicly is, this business at a $100 million business, $100 million in revenue, 80+% gross margins, where we're just about there now, 40% adjusted EBITDA margins, and close to 25% free cash flow margins. And so, when we look at this year, our goal is to reaccelerate the business from the top line, to 20+% growth. We've seen that in the first quarter, seeing close to 30% in the second quarter. So we've been able to do that. Even though we're investing in some of these key areas, you still see our incremental margins growing. I mean, if you look at quarter- to- quarter or at the growth level, our incremental margins mostly over 100%. So a lot of leveragability as we continue to scale. And to give you an idea from a competitive standpoint, those competitive products that we create, 800,000 from LexisNexis with that Accurint product, over 400,000 customers today. TLO product, we believe, you know, 60-plus thousand customers. Today, we're just about 8,500 customers, so a lot to go get. Given that bit of information we gave it, what we look like at $100 million, you know, we're very proud that we've put up a third quarter... Excuse me, a second quarter here, 30% revenue growth at with all of those characteristics, 80% gross profit margins, 36% EBITDA margins. So it demonstrates, you know, that's exactly what we can accomplish. Yes? Do you have non-competes with the buyers of your previous two companies? Had non-competes with the buyers of the previous two companies. No longer. Reasons for them to part? We did not in the early days because it was a different type of business that we were building at the time. We had some litigation that was resolved. Thank you.