All righty, we'll get started. Good morning, everybody. Thank you for attending the Southwest Ideas Conference. I'm pleased to introduce Red Violet, traded on NASDAQ under symbol RDVT, a company that specializes in transforming vast and disparate data sets into actionable identity intelligence. Their cloud-native core platform, powered by proprietary data assets and analytics, enables organizations to verify identities, detect fraud, manage risk, and meet compliance demands. Joining us from the company today is Camilo Ramirez, SVP of Finance and Investor Relations. Thank you, Errol. Let me just adjust this. All right, thank you, everyone, for joining bright and early. First presentation of the day. I'll start off with a little bit of business background, business model, give you guys a couple of use cases, but hopefully, let's make it interactive. Feel free to ask questions. The more questions, the better. What do we do, Red Violet? I'm sure you landed on our website. Got a vague idea of what we potentially do, but at our core, we're all things identity. We understand basically the whole U.S. population. I'll back up a little bit, give you some management history first. Management's been together for about almost three decades now. In the late 1990s, they started a company called SizenT, also in the identity space. Ultimately, sold that off to Reed Elsevier, LexisNexis, for about $750 million. Non-compete's expired. Team got back together, started a company called TransUnion. The individual that was personally funding that iteration ended up passing away unexpectedly. Therefore, the team sold it prematurely. Revenue run rate at that time was about $25 million, but they were burning about $1-$2 million a month in cash. Ultimately, TransUnion ended up picking up that iteration just for under $200 million once he assumed debt and so forth. Third time go. Team got back together. A group of individuals approached them. If you have another go at it, would you do it again? How would you do it differently? As you can imagine, technologies changed drastically from the late 1990s, early 2000s, and so forth. Team got back together. Third iteration was in the cloud. We're cloud-native, so we can scale up, scale down during peak productivity times, which is a main differentiator with one of the two legacy companies, our main competitors. Essentially, they are in data warehouse rooms, right? As they have to update their technology, they have to update, do a full refresh on their tech stack. At times, they struggle to scale up during peak productivity hours. Today we aggregate disparate databases, call it liens, judgment, credit header data, anything and everything on every adult individual, mobile IDs, IP addresses, create that 360-view profile on that individual, and then sell it back out. I'll run through a couple of use cases. We say that we serve five verticals. I'll start off with collections. It's easiest to understand. You have Capital One. They sell off that debt. You have a million records that debt collector. They need to understand right-party contact information. Has this individual filed for bankruptcy? Because debt collector can't call on that. They'll batch over that information and will provide the requested data points. Usually, that's done through a batch process. You can consume our data through three different fashions. One's going to be online. Once you're credentialized, essentially, once you're logged in, it's pretty much a Google search. You have a name, date of birth, social, any unique identifying information, put it in, and you continue to zoom into those types of individuals that meet that criteria. Secondly is going to be batch, like I just gave you with the collections use case. Lastly is going to be an API connection, computer to computer. Second vertical we serve is financial and corporate risk. That's going to be, for example, background screening. A good example there for background screening. I'll go back a number of years because we disclosed this name early on. Company called Innovative. We won it from TransUnion. They were doing around $30,000 a month with us. Ultimately, a number of years later, they grew that business, at least from a usage perspective from us, well into the six figures. They were purchased by Aperis, and then ultimately, Aperis was purchased by Equifax. In that scenario, how do we play in the background screening space? Let's say you're Walmart. Excuse me. Walmart has an applicant. They list five addresses. They'll list those five addresses. It will go back to the Innovative. Innovative will call out to us, say, "Hey, is this data accurate and complete?" We'll say it's accurate, but it's incomplete because they left off that sixth address. That's usually where the criminal history lies. They'll go out, pull that criminal record, and provide that information back to Walmart so they can do their hiring decision. Yeah, when Equifax made the purchase of Aperis, we understood they have way more data than us, as you can imagine, much larger balance sheet. They were looking to execute on synergies on that acquisition, right? They can cut out that data cost. That Innovative contract with us was up for renewal. That was a multi-year contract. They approached us to, "Hey, can we get a three-month extension on that contract?" and said, "We understand. We're here to help you. We understand you're trying to execute on synergies and so forth. If you need us, we're here to help." They came back, asked a couple other times to do contract extensions, and ultimately, they couldn't produce the same lift on their data as they're gathering from us, even though they have all the individual data points, right? That name, that date of birth, that phone number, that address. They just don't have that aggregation platform where they can make those connections where you see a phone number, and this phone number belongs to Camilo Ramirez. Here's the address for them. What really is our core competency? It's that aggregation platform, how we marry all that data together and high confidence within those data points that that is that individual who we're stating they are. Ultimately, they signed a long-term agreement with us. Today, they're still a customer. They continue to grow with us, which is a testament to our platform as a whole because they just have significantly more data than us. Another good use case in that corporate and financial risk vertical is going to be like, let's say you're a forward mortgage company. You have an individual in your accounts payable department. They're writing a $1 million check to a vendor. As forward, you'd want to understand that that accounts payable individual has a brother-in-law at that vendor that's a director, right? They're writing a $1 million check. It's not fraud, but it's a yellow flag. You would want to understand that relationship. Just risk mitigation. On the third vertical, we can do real estate. Real estate, we serve real estate in two fashions. We have our IDI brand, which applied to the use cases I just gave you, and then we have our Forewarn brand. That is essentially just a skin on our core platform, our IDI core platform. We branded it specifically to go after the real estate associations. Essentially, in that scenario, let's say you're a realtor. You get a phone call. I want to see this $3 million home. I'm only in town until tomorrow. You don't know anything about this individual or their wherewithal or are they able to purchase a $3 million home. They're saying their name is John Smith, purposely a very generic name. You don't see John Smith. When the realtor receives that phone call, the Forewarn app basically just does a reverse phone search based off that phone number, and it pops up that individual's information. They're going to say, "I'm going to show up in a Mercedes S-Class." You don't see it. What you do see is, "Hey, this individual was just released from prison for sexual assault." You're not going to show that home. Very extreme example, but it's a safety solution more than anything. We've had really good success in the real estate space with our Forewarn application. We say there's about 1,100 associations in the U.S. Today, we're about 50% penetrated. There's no other competitive product in the real estate association side, for a lack of a better term. Most of them are find the body. You got panic buttons. Call 911. Items like that. There's no proactive safety solution. We specifically go after the associations as opposed to the individuals. Just the associations will purchase our Forewarn product for a member benefit for those users. On the IDI side, we serve real estate more of a propensity to sell. So, prop tech companies. Give me a list of individuals that are 60 and above that live in a two-story home, items like that. The fourth industry, the fourth vertical that we serve is going to be investigative. Investigative, you're going to have law enforcement, private investigators, repo, use cases like that where they're trying to, on the repo side, they're trying to locate a vehicle. Private investigators are investigating individuals for run the gammon on there. Lastly, we have our emerging markets vertical. For emerging markets, for us, it's verticals where today we don't have a large presence. Just by choice, we haven't invested the dollars for the go-to-market from a Salesforce perspective. A couple of industries in that vertical that could potentially be large contributors in the future are going to be insurance. They want to understand, is this the same John Smith that defrauded us 10, 15 years ago? The industry that we are focusing on today within that vertical is going to be government, so public sector. Public sector is a very interesting space. There's a lot of use cases. As you can imagine, those contracts can be significantly larger. On our last quarterly call, we gave some color. One of the use cases where we won one of the largest toll authorities in the U.S. Down by us, we have Florida SunPass. As you're going through the tolls, if you don't have a SunPass, it's going to do bill by plate. They have a contract with the state, so they can understand some of those plates. If you have an out-of-state plate, they just don't know who that owner is, right, through their internal systems. They contracted with us to understand, "Hey, who's the owner of this vehicle?" Registered owner. What is their mailing address so they can mail that invoice to try to collect on it? Let's say that individual doesn't pay it, it goes through a collection process with the state. That contract can be, once it's fully ramped, it'll be well into the seven figures. Let's say that's one of 50, right? Other states have state toll authorities, and they have local county tolls as well. What really excited us about this is, "Hey, we can now have that use case as we go after other states." The operating company that's managing the toll authority for the state also manages other states as well. It's a really nice inroad to potentially win additional states through that operating company that's managing the toll authority within one of the states. Another interesting use case in the public sector is going to be, let's say it's going to be address verification. You have an individual, their son lives with them, but they're going to say, "Hey, my son's daughter is living actually with the grandparents because the grandparents live in a better school district." What we're starting to see is a lot of school districts are starting to do address verification. They want to verify that the child's parents and so forth do truly live within that school district, or should they be going to another school district? Because that school that's losing out on that attendance, they're potentially losing out on budget dollars. They want to bring those children back so they can retain those budget dollars. Just a very edge case, even homestead exemptions where they're doing address verification, understanding this is your primary residence and so forth for homestead exemption fraud and so forth. I'll start moving down the P&Ls. A couple of examples. All our data is fixed costs. We like to say we have a step-fixed cost model. If we decide to bring in a new data set, that's going to step up slightly, but we're fully funded on our data side. If we do bring on a new data set, it's going to be immaterial to the cost as a whole. I'll just flip a couple of charts here. Yeah, every additional dollar that we bring on board is nearly 100% contribution margin. Outside of cost of revenue, we have our selling. Most of the variability there is going to be around headcount. Call it last quarter, we had just under 250 employees. I think year over year, we grew about 25 heads. Outside of sales and marketing, your other variability is going to be commissions and then dollars we spend. I would say about now 18-24 months ago, we hired an SVP of Marketing. He came over from TransUnion proper. He was running their risk mitigation marketing department there. Prior to that, we had no real presence outbound from a marketing perspective. He started getting us to the right conferences, right places, and we're starting to see the fruit of that labor. We're starting to get a lot more inbound. As we make outbound calls from a sales perspective, it's no longer, "Hey, who's Red Violet?" It's, "Oh, we understand that either you're powering one of the identity verification platforms or you created the other two iterations." We say we power about seven of the top 10 identity platforms. We like to call them orchestration platforms. They don't own any of the data, but they have that unique technology that's front-facing to the retail customer where I'll give a good use case. Let's say you're using Zelle. It's going to be mobile authentication. You put in a phone number, you're going to Zelle someone money. You're saying the name's going to be John Smith. You enter that number, and it'll ask you, "Hey, I actually see Joe Smith. Is this the person that you want to send the money to?" It is doing a reverse phone search. That's one of the orchestration platforms that's powering that technology on the front-facing. Other orchestration platforms, without naming any customers, are going to be Proof, Jumio, Ekata, now owned by Mastercard. Each player has their own way of doing that identity verification, but they all have to call out for that PII information. Either we're powering them or one of our two legacy platforms are powering them. Once you move into G&A, most of the variability there is going to be around our engineering infrastructure team. Again, headcount. Outside of that, we like to commit long-term contracts for all our vendors, make it a very predictable P&L and balance sheet. Some of those hours for the engineering are capitalized. If you're looking at basically how we look at it internally, we take our adjusted EBITDA and then add back any internally developed software that hits our cash flow. That's any hours that are worked on improving the platform for the engineering and infrastructure individuals. I'll pause there and see if there's any questions. In like your Forewarn example, is there any IRR that they can generate that you can then use to sell to other places? Yeah, it's a good example there. When we go head-to-head, we'll see with TransUnion. We'll use something as simple as phone numbers, right? Usually, when these companies are testing, they'll test us and the two other competitors. A good example is like, "Hey, TransUnion gave us 10, 15 phone numbers, and you only gave us three." We'll say, "Go ahead, start calling down that list." We have a high confidence that you'll get someone to pick up on that first or second number. Most of those numbers are going to be disconnected. It's just going to be inefficiencies in your process. That's a very basic example, but it's just the quality of data there. Also, being able to understand outside. In the toll example, they need to understand plates from other states as well. I think that's where a lot of friction is created as well. They'll have a test file where they'll send to us and then send to the competitors and see. Sometimes you'll get false positives, so they'll follow through with that as well. Because false positives is a bad thing, right? It's going to waste their time and so forth. They'll have a test file and send it to everyone to understand who has the higher accuracy, higher hit rate overall. Okay. I usually get the questions, use of capital. We're generating a ton of cash. Free cash flow last quarter was about $7 million or adjusted EBITDA around $9 million for the quarter. As you can imagine, we're going to continue to generate a lot of cash as we continue to grow that top line. Again, it's nearly 100% contribution down the P&L. A couple of uses of capital. I think you may have potentially, if you're following the story, you may have seen that we filed a shelf yesterday, $150 million shelf. The shelf is strictly for optionality. Just have a tool in our toolkit. We've been generating cash. We have a long organic runway. We have been seeing a lot of additional companies come to the forefront where they have a unique technology, unique data asset, or just a front-facing solution where it's going to be a buyer versus build decision. We want to be able to move swiftly if that does arise. A couple of things that we look for from an M&A perspective is data. Do they have a unique data asset where we can start owning that data as opposed to leasing it? Even though we do long-term contracts, we would rather own that data, have it in-house in perpetuity. Then we kind of control that data as well. Because most of the time they're selling to us because they're aggregators and they're going to be selling to TransUnion, Red Violet, LexisNexis, and so forth. Is it an adjacent technology that would be complementary to our platform, for example, like biometrics, iris scanning, and so forth, or a buy versus build where they have a front-facing solution where we can go out, purchase this company, take out that data cost because they don't own that data. We'll get synergies there. Also get lift from the revenue perspective where we're no longer powering that front-facing solution. Instead of getting wholesale rates, we can go to the end user and get retail rates. We'll get additional lift there. Additional uses of capital. We do have a share repurchase program. Last quarter, we increased that share repurchase program as well. We did issue a one-time dividend earlier in the year. Big 10% customers? I'm sorry. No, no 10% customers. Today, we have about 9,800 customers in the scope out the space. Red Violet, Camilo's LexisNexis, they have about 400,000 customers. TransUnion with their TLO product, they have about 50,000-60,000 customers. We're just scratching the surface from that perspective. Outside of those two players, there are small niche players. Like if you need real estate data, you can go to a real estate provider. We are the main three aggregators that have that 360-degree profile. There are a couple of other players, but the data quality is subpar. Sometimes we'll see customers go to lower-tier quality players just for cost savings, and they'll usually revert back to us a couple of months later because once they start putting that data through their workflow, they just have inefficiencies back to your IRR question. We directly compete against TransUnion with their TLO product. They just rebranded to the True Platform. They made an acquisition of True Narrative a number of years, a couple of years ago. They're trying to combine those platforms. True Narrative was in the cloud. TransUnion, I would say three years, maybe three, yeah, three years, four years ago, they stated they were going to bring their platform into the cloud. They were going to invest $100 million. A couple of quarters later, they came out and said, "We're going to invest another $50 million." We don't believe they're going to get that platform into the cloud. Once you start removing pieces, it's like a game of Jenga, right? It's going to stand up for some time, but once you start moving significantly more pieces to move it in the cloud, it's just going to, we don't see it being possible. One of the reasons is because those platforms are built with proprietary code language. Even our platform today, it's written in what we call iron. In this iteration, it was a conscious effort that when we hire individuals, instead of having them learn this proprietary language, we can have them write in C++, Python, and so forth. That will basically move up into the language iron that we have, and they can code out. We don't have this long ramp of educating that individual on how to code in the iron language as opposed to the previous two iterations. It was all proprietary languages. When they're hiring you tech individuals, first six months, they're just trying to understand, "How do I code to this?" Right? You also have that lag. That's one of the reasons we don't believe they're going to be able to get into the cloud efficiently. They've been trying to say that, "Hey, we are in the cloud because of the True Narrative acquisitions," but what we've seen, it's still a very fragmented experience. Like you're getting an invoice for the TLO product, you're getting an invoice for the True Narrative product, and so forth. Any other questions? We got 12 minutes. I can keep talking or we can make it interactive. You've got a lot of things on your plate. What are the one or two that reason investors in the machine? Yeah. Yeah. No, good question. We're really excited about two verticals right now: background screening and public sector. About 18 months ago, I'll start with public sector first. About 18 months ago, we hired an individual, Jonathan MacDonald. He came over from, he basically led the TransUnion public sector vertical from zero to where it's at today. We built a team around him, called it about 20 individuals. We verticalized his team into Fed and SLED. Fed, they're going to go after those federal contracts, three-letter agency. SLED is going to be state, local, and educational. Jonathan MacDonald laid out a roadmap. He said, "Year one, it's going to be building the team, building the foundation. 2025 is making our presence known, and 2026 is where you'll start seeing revenue contribution. I would say 24 months ago, 24 plus months ago, we gave color that, "Hey, we're going to grow this business 20% top line for the next three years." 2024, we grew 20%. 2025, we've been on track on that 20% mark. 2026, we're confident that we'll continue to trend towards that 20% mark as well. That was pre-Jonathan MacDonald being onboarded. We always get the question, "When you gave that color, is it with the public sector fully executing?" The answer is no, because we didn't have Jonathan MacDonald at that time, and we didn't have a large presence in the public sector for anything. Everything that occurred with Doge was perfect. It was a perfect storm for us, right? Because we didn't have a presence. We saw a lot of contracts just get slashed. As we've seen, some of those contracts are coming back for RFP, and they wouldn't have been up for RFP for another three, four years. Now we're getting a shot at those contracts much sooner than anticipated as well. Our pipeline has grown dramatically over the last 18 months in the public sector space. Those contract values vary, right? They can be small state Department of Revenue contracts. If you land one of those large three-letter agencies, those can be multi-million dollar contracts a year. They are real needle movers for us. We're really excited with what Jonathan MacDonald has done so far, and we're excited to see what comes for next year on the public sector side. The other vertical I mentioned was background screening. When Equifax purchased Aperis, we made the conscious effort, "Hey, let's go directly to the end user as opposed to powering these individuals." In that space, for whatever better or worse, the space doesn't like dealing with Equifax for whatever reason. We gave color in our last call where we won one of the largest payroll processing companies in the U.S. Without naming the name, it's going to be the likes of Paylocity. Next, we have their own ERP payroll system, ADP, and so forth. That contract, it's a six-figure contract, multi-year contract. Their minimum commitment, we'll start seeing that contractual obligation starts in April of 2026. Once they're fully ramped, we anticipate that being a seven-figure contract once they're fully ramped. These transitions, it's not an on-and-off switch. They'll start bringing over their lower-tier customers, make sure the flow is working correctly. They are proving out that, "Hey, everything is valid. Everything's working as expected." They'll start moving up their customer stream from that side. We're excited with what we're seeing in the background screening space as well. That pipeline has increased as well. We made a hire, again, from Equifax ultimately to lead the go-to-market strat, the data and how we approach the background screening space. We're excited with what he's brought. He's a lot of knowledge there. On our roadmap, we have other verticals that we want to penetrate, right? We don't want to be a mile wide and inch deep. As we penetrate different verticals, we'll invest. If it's gaining traction, we'll continue to invest in that headcount, that go-to-market strategy within that vertical. We'll start expanding that. We have the data to serve a lot of these verticals. It's just how do we focus, how do we stay focused and manage that roadmap as opposed to being a mile wide and inch deep? We get the question a lot as you enter, for example, insurance. Historically, insurance has been a large revenue contributor to these organizations, but we have a small presence in insurance, if any. Not by lack of having the data, it's just we haven't been focused. As we start penetrating other verticals, it's not that we're going to go out and have to materially change our data costs. We already have that data. It's more of a go-to-market strategy, potentially adding a small data asset. It's going to be immaterial to the whole cost. That also excites us, right? As we start executing on that, I always get the AI question as well. How does AI impact our business? Right? Because they say, "Hey, why can't you just build a model in AI and do identity verification? You can do it in ChatGPT." In the KYC banking industry, they have to have high confidence when they're opening accounts. We'll leverage AI for our benefit in a couple from an operational and also a tech stack perspective. We have this data asset, high confidence data asset. Once you layer additional AI, large language models on it, what can you gather from it? Today, all the platforms are very input-driven. "Hey, here's a name, date of birth." We're working on how do we communicate it, make it very conversational. Tell me everything you know about Camilo Ramirez. A good example is going to be, is Camilo Ramirez related to an individual with a violent background? Today, for you to find that out, you'll have to search Camilo and start going all down the known relatives, associates, and click on, "I'm looking at their history." You are doing multiple searches. When do you stop? Like, how far do you go? As opposed with the interactive way, you ask the question, it's going to run all those searches, and it's going to say, "Yes, he is related to an individual," or, "No, he is not." It will tell you exactly, instantaneously, as opposed to doing all those individual searches. Even on how we gather data, let's use obituary data, for example. It's going to be newspapers on websites, long-form data. It's going to print out a very nice family tree, right? Hey, it's going to list cousins, nephews, second cousins, so forth. How do we use AI to go read that long-form data and assimilate it into our platform and add that additional data connection? Even from an automation standpoint operationally, right? As we continue to grow for the next $100 million, how do we not scale our operations? Let's say for every 1,000 customers, you need one credentialing person. How do we scale that back, automate that process? For the next couple of thousand, we don't have to add additional headcount to support that customer base. Continue to extract the margin from that profile. At maturity, these organizations, from a gross profit margins, they're around 90% gross profit margins on the just EBITDA line, around 60%. Today, we're in the low 80s from gross profit margin. Just EBITDA, we're starting to approach that 40% mark. Similar to before they run that background check, right? Because we want to stay out of the actual decision-making, right? Because then you fall into the Fair Credit Reporting Act, and there's a litigation that occurs there, a lot of G&A costs that gets added if you're providing that hiring decision. We don't provide hiring decisions, and we don't provide credit scores to stay out of the Fair Credit Reporting Act. Before they run that background, they want to validate, "Hey, is this data accurate and complete? Is this the individual that they actually say they are?" Before they go have that spend running that full background check. Even for certain industries, you need yearly background screenings on individuals, like employees and so forth. There's multiple industries that need that. Even trucking industries, they need to understand if someone was pulled over for a DUI or anything like that. They'll run background checks periodically. Is that payroll for doing that, not the actual company? It's a service provided to the company. Yep. Yeah. Yeah. Yep. It's a service. Yeah. See the retention of them earlier for us. Generally speaking, what are the contracts? What do you have? Yep. So. Yeah. Generally, contracts are 12-month contracts with auto renewals. Usually, there's a couple of different ways. Essentially, if there's a per-seat license, usually that's for law enforcement, whether an individual has a login, it's all you can eat. They'll minimally commit to 10-seat licenses for $100 each for a year. Once they use more, if there's additional individuals using that platform, they'll go into overage. Usually, when we see customers going to overage, it's a good upsell opportunity because they're usually paying slightly more on that overage usage. Say, "Hey, you've been consistently in overage. Let's move up your minimum commitment and give you some pricing concession." From a usage perspective, we like to say we're an information solutions company. It's a usage-based business with contractual minimums. For 5,000 searches, you're going to pay $2,500 a month. Once you go over those 5,000 searches, you get into overage. About, call it last quarter, 75% of our revenue was contractual. Even the transactional revenue is highly reoccurring for these businesses that are being in business. They either have to use our platform or another platform. This does drive some seasonality in Q4. You have fewer business days, so that transactional revenue, we lose about a week because it's B2B. We lose about a week in November and about two weeks in December. Historically, not accounting for last year, Q4 has usually been seasonally down from a top-line revenue number. Also, on the G&A side, we have company year-end bonuses as well. You'll see that margin profile tick down in Q4 when you guys look back at history. Last year was the first time we bucked that trend. Just on the names of those that are per-seat orientation versus those that have usage? It's going to be industry and use case specific, honestly. Collections is usually per seat. That's the norm for that industry. Law enforcement is typically per seat as well, just because of how that user base uses the platform and what they've been accustomed to. Yeah, most, I would say it's industry and use case specific. That's going to be for our online platform users, not our batch process or API.