Red Violet, Inc. — Call Transcript 2025

Here again for day two of the Ideas Conference. The next company we have for you is Red Violet. They're a leading solutions provider in the ID verification sector. With us today, we've got the Vice President of Investor Relations, Camilo Ramirez. Camilo? Thank you, Steven. Appreciate it. I'll give you guys a bit of a background about what we do, management background, and then we'll go over our use cases, exactly what we do. Let's make it conversational, so feel free to ask any questions. Stop me if you need clarification on anything, but we'll go from there. I'll quickly flip through the deck. I'll highlight a couple of pages, but preferences for conversation. What do we do? We do all things identity. High level, we aggregate disparate databases, call it liens, judgments, credit header data, anything and everything on every adult individual in the U.S. We aggregate that data and then sell it back out to different industries. We serve five different verticals: collections, financial and corporate risk, investigative, real estate, and what we call emerging markets. I'll dive deeper into that in a little bit, but let me take a step back and give you a little history about management. In the late 1990s, the management team built a business called Sizen in the same space, data fusion, identity verification. As you can imagine, technology then was very different. We built it in data rooms. There were ad hoc connections. You see Camilo and Steven always present them in an investigation as opposed to a more dynamic presentation where the Camilo result can be applicable only to the situation and don't present Steven. Ultimately, that was sold off to Reed Elsevier's LexisNexis for about $750 million. Non-competes expired. Team got back together. They started a company called TLO. It was being personally funded by one of the founders. He ended up passing away abruptly through the development phase. Revenue run rate at that time was about $25 million. They were losing anywhere from $1 million-$2 million a month. Ultimately, that was sold off to TransUnion prematurely, right? They were still going through the R&D phase. Non-competes expired. Team got back together, call it in 2014. I'll show you guys a little roadmap of the history. They got back together in 2014. As you can imagine, technology has changed drastically from the late 1990s-2014. We had AWS, so this iteration is cloud native. It was built in the cloud from the ground up. That's a big differentiator from the competition, the Sizen product and the TLO product that management built. You have LLM models, AI, so it's able to learn upon itself as you feed that data. It's making those connections, building new data points to go out and clear transactions. What do we do? I'll give you a couple of use cases. We'll start with collections. It's the easiest one. Let's say you buy debt from Capital One. You have a million records. You want to go out and you buy a million records. You want to go out and validate that information. I'm getting kind of an echo in the back. You buy those million records. You go out. You want to validate the information, right party contact information. Have they filed for a bankruptcy? We'll pend that information, send it right back. We go to real estate. We serve real estate in two different manners. On our IDI side, it's going to be more of a marketing play. I want a list of individuals that are 16 and above that live in a two-story home, propensity to sell. Under our brand Forewarn, which is basically just a skin and application that was layered over our core platform, that's sold directly towards the real estate community. We go after associations. That is going to be, let's say, you're a real estate agent. You get a phone call. I want to see this $3 million home. I'm only in town till tomorrow. My name's John Smith, purposely using a very generic name. I'm going to show up in a Mercedes S-Class. What it does when that phone call comes in, it does a reverse phone number search and it comes up, brings up their background. You don't see John Smith. You see a different name. You don't see that Mercedes S-Class. What you do see is this individual just got out of prison for sexual assault. You're not going to show that home. What we noticed was that there was a lot of crime committed against realtors. This is more of a safety product for the realtors. We go after the real estate associations. Today we serve about 500 plus real estate associations. There's, call it about 1,100 associations in the U.S. The associations, what they do, they buy this product for their members. It's a member benefit for the users. If you're part of, let's call it down by us, Palm Beach Real Estate Group, the state of Florida purchased Forewarn for all the associations underneath it. They'll just sign up. It's free use, unlimited use, and that's a SaaS product. If you're an individual coming on board, not under an association, that's going to run you $20-$25 a month. Because we sell directly to the associations, economies of scale, those prices are reduced drastically. On the Florida, Florida is one of the largest associations in the U.S. That's going to be a couple of dollars per member. They have about 400,000 members. It just depends on the scale of the association. Next, we have financial and corporate risk. Under financial corporate risk, we have background screening. I'll give you a couple of use cases there. Let's say you're a Walmart. You have an applicant. They're applying for a job. They enter four addresses. I'll disclose this name because we disclosed it publicly early on. They'll go to a company called Innovative. Innovative is the background screener. Innovative will call out to us, is this information accurate and complete? We'll say it's accurate, but it's incomplete. They left off the fifth address, and that's usually where the criminal record lies. Innovative will go pull that criminal record at that fifth address and ultimately send that information back to Walmart so they can make their hiring decision. A use case on Innovative. We won that business early on from TransUnion. They're doing low five figures a month. Ultimately, they grew up into well into the six figures. They were sold to Aperis, another identity player. Ultimately, Aperis was purchased by Equifax. As you can imagine, Equifax has all the data in the world, right? They have all that information, but they don't have an aggregation platform. They have the individual data points. At that time, during that acquisition, the Innovative contract was up for renewal. Equifax reached out, hey, can we get an extension on this contract, three-month extension? We said, of course, we understand. We understand you're trying to execute on synergies. We don't think you're going to have the same throughput or lift on data, but we're here to help if you need anything. They came back, asked for a couple more extensions. Ultimately, they signed a multi-year agreement. They couldn't recreate the data quality or the throughput on their side, even though they have all the individual data points. We have a really good relationship there, multi-year contract. We can go into investigative vertical. That's going to be, for example, law enforcement. You get pulled over by law enforcement. Very easy. Our system or one of our legacy products that we sold off. They'll run a plate, do an investigation. How we differentiate in that space is we have a very neat, mobile application where, let me just flip to another side. A neat application. Let's say you had a witness. I saw this red F-150 in this intersection. It was a hit and run, something of that nature, crime committed. The investigator can come in, basically do a geofence, drop a pin within that circumference of that area and say, give me all estate, public sector. Public sector, we're really excited about the public sector space. We made a hire, call it just over a year now, Jonathan McDonald. He led the public sector at TransUnion, built it up from zero to where it is today at TransUnion. We built up a team around him as well, call it around 15 individuals-20 individuals. Public sector, as you can imagine, your typical three-letter agencies doing investigations on individuals. You have ICE doing investigations as well. Those are all opportunities we can win. Even outside of that, where there's niche use cases, for example, we just won one of the largest toll authorities in the U.S., without naming specifically which one. Down by us in South Florida, we have what they call the SunPass program for the Florida Turnpike. They can do bill by plate. Let's say you're running through the toll, you don't have this SunPass to automatically pay for it. It's going to run, it's going to take a picture of the plate. Then they have to do a search based off that plate to understand who's the owner of this vehicle, registration, search, and then they have to figure out the address, send out a bill to that vehicle owner. If that owner doesn't pay that bill, it goes through a collection process and they have to go through right party contact information. We won this contract, just call it maybe a quarter ago. We disclosed it on our last earnings call. That's going to be well into the seven figures once that's up and running. That's one of 50, call it. Some other niche use cases are like homestead exemptions. Are these individuals truly living in that home that they're claiming a homestead exemption? Doing an address verification, homeowner verification, and so forth, or even something as niche as, is this child truly living in this public school boundary, right? As you can imagine, there's a lot of situations where parents say, hey, my child's living with the grandparents so they can go to this A-rated school in a public school system, but in reality, they're not. We have school districts reaching out for that type of data, address verifications essentially to validate that those children truly live in that area and they are eligible to attend that school. Those are some of those niche use cases in the public sector that we're really excited about. Those five verticals, we say revenue is pretty evenly distributed, call it 20% across the board. I'll pause there to see if you guys have any questions. If not, then I'll jump into the financial model. We like to say our data is fixed cost in nature. We go out, do long-term agreements with the data providers. As you can imagine, some of those are going to be credit bureaus, niche data aggregators that are getting data from different municipalities, aggregating that information, we'll consume it. We do long-term contracts on that, and it's fixed cost for unlimited use. Every additional dollar we're bringing in today is nearly 100% contribution margin. Our last quarter on the gross profit line, we reported just over 80% gross profit. We're starting to approach 40% adjusted EBITDA margins. If you take a look at our financials, our disclosures in the 10K, you'll see that one data provider accounts for 40% of our data costs. That is not just one data asset, its multiple data assets. We have a really good relationship with that credit bureau. Ultimately, we just renewed that contract for another five years here a couple of months ago with minimal incremental increase. The renewal prior to that was flat. We always get the question, hey, if you see these, if the credit bureaus are seeing the type of margins you guys are having, why wouldn't they just escalate the price on you? One, we're multi-sourced on every data point. If that does occur, we'll just say, hey, we're not going to accept that, and then we'll just bring our tier two data provider up to tier one and then continue to move on. We can give those data points back. If those data points go back, we don't lose the learnings, those connections between brother, son, family connections, those learnings. Those are all proprietary to us. Those learnings stay with us and data points go back, and we just bring up our tier two. The last couple of renewals, we have a really good relationship, and we renewed pretty much flat from that in our last renewal. No concern there. Are you dependent upon your apps, and do you have some special technology that's not able to do so? Sorry. No, no, no. No patents, right? We don't have any patents. It's the technology that we have that aggregates that information and links those data points together and continues to learn from that information. A good example that I like to give is, in certain industries, they have what they call the waterfall effect. You'll go into an industry and try to gain a customer and they're going to, we will go in and say, hey, let us come in at tier three on your waterfall. What that means is they're sending a million records to, let's say, TransUnion. TransUnion is going to have a certain hit rate, call it 60%-70%. Some of that's going to fall out. They don't have information. They'll send it to a second provider, Reed Elsevier LexisNexis Accurint product. They'll be able to hit some amount on that fallout as well. We'll say, hey, let us come in third. We know we have high confidence in our data assets and we'll get a high hit rate on that. We'll come in even though it's been scrubbed twice already and we'll get a high confidence hit rate. That's how we'll start moving up the waterfall as well. If you have a high hit rate, the customers want you to be first in waterfall because you get economies of scale. As you go down the waterfall, it gets more expensive for them. As you can imagine, they're pushing less, less data through you. It's what we do with that data and how we aggregate that data, as opposed to just hustling or just reselling data points, right? Because a bankruptcy is just a bankruptcy. It doesn't tell you anything. As you're transacting in the commerce today, you're leaving a footprint every single day. Banks for like know your customer or loan decisions can't rely on data from yesterday or the day before. They have to have the most recent data. They want to understand, did you just file for a bankruptcy? Did something occur? Was there a lien or judgment placed on your property or anything of that nature? You need the most current information. What about the international market? Today, we're not in the international space. We have a lot of go-get in the U.S. Today we have about 9,500 customers. The Accurint product has about 400,000 customers, and then the TLO product has anywhere from 60,000 to 70,000 customers. We're just scratching the surface. We're powering seven of the top 10 identity players, to the likes of, call it, Prove, Jumio, Econo, now owned by Mastercard. They all have their own niche way of doing identity verification, how to clear a transaction. Some of them are going to be mobile authentication. Let's say you're logging into Bank of America, Wells Fargo, using facial recognition. What's actually happening behind the scenes is saying, hey, this mobile ID belongs to Camilo Ramirez as a match to the bank side. Here's the PII information. Yes, grant access. If not, ask security questions of that nature. Or they're going to be doing document verification, take a selfie, load a picture of your ID, and they're going to do the identity verification on that physical document. It's still pulling PII information behind the scenes. Those providers have the technology, the front-facing solution, but they don't have the data in-house. Every time they're clearing a transaction, they have to call out to someone like us or one of our competitors. I think there was a question over here. Can you talk about the switching costs for your system? It's a good question. Is it difficult if someone's there to know? No, so it's use case dependent, right? If someone's coming on board using our online platform, there's no use cost, no switching costs. They're just going through our credentialing process. We're validating that they have a valid use case, and then they can start using our system. That's how we win business too, right? Because they're not coming in, dropping a team, building out a whole platform. On the API side, our API is very flexible. We like to say it's very customizable, and behind the scenes, it's just turning on and turning off levers depending on what they need. Usually they're going to have teams on their side and they're up and running between 24 and 48 hours. A good example there on switching costs and ease of use on the API side: a number of years ago, we had a customer, they were doing well into the six figures a month. They were an identity verification, mobile, they do mobile authentication. What they're doing is just the example I gave. They were raising capital. TransUnion ended up leading that capital raise. The TransUnion CEO gave them a call and said, hey, for us to close this transaction, you're going to have to stop using Red Violet's IDI data and move all your consumption to us. The customer gave us a call, told us what was going on. We said, we understand, we would do the same if we needed the capital. We're here if you need anything. We saw that revenue drop off pretty much immediately. Within a couple of months, we started to pick back up. What came out of it was that once they switched to the TransUnion product, they basically couldn't handle the throughput and the latency on the platform, right? Because they're building a data room as opposed to the cloud. During peak productivity hours, their customers were calling them and complaining, hey, I sent this call through. I'm not, I'm getting false positives or I'm not even getting a response. Ultimately, that business came back to us pretty shortly after. The CEO gave us a call. They said, hey, TransUnion said they can build us a custom API. Just give us a month or two. They said that you guys built us a custom API. We reminded them, hey, you guys were up and running within 48 hours. There's nothing custom about it. To this day, years later, they're continuing to grow with us, even though they have that relationship with TransUnion. Would it be fair to say that you've got to stay ahead of the technology? That's a fair statement. Yeah. We're continuing to build that moat around our platform. You have TransUnion, you have Reed Elsevier LexisNexis. They both committed, I believe TransUnion committed about $200 million-$250 million a number of years ago to get their platform into the cloud. We know they're not going to get that platform. We like to give the example, if you take a 757, you want to make it much more efficient, fuel efficient, you're not going to just take parts off, put parts on. You're going to start from the ground up. Once you start removing a couple of pieces from here and here, there's going to be downstream impacts that they're not going to be able to correct within the cloud. They have to start from the bottom up. We're continuing to build that moat, one through the technology we're using, the throughput, the latency, and then also just from product features as well. Something as simple as, when you're doing a search on the individual, understanding is this relative, is this relative of that individual, is it its mother, father, sibling, cousin, or anything of that nature? We get that granular based off of certain algorithms we have. We're able to tell you what type of relative that is as opposed to the competition. They're just going to tell you it's an associate. They don't have that level of granularity. I think I was going through the financial model. Fixed cost model, nearly every additional dollar is 100% contribution margin. Once you get below the gross profit margin, we like to contract everything long-term contracts to any of our vendors. Most of our variability is going to be headcount, and that's going to be related to sales and commissions. We verticalize all our teams into subject matter experts. We have a real estate team, law enforcement team, and so forth. There's a couple of verticals we're really excited about for the coming years. One, I alluded to public sector. As you can imagine, that's a very large TAM. With everything that occurred earlier in the year with DOGE, that's a tailwind for us. It worked out perfectly. We weren't deeply penetrated into the public sector market, so we didn't lose contracts. Historically, you have to wait until those RFPs come up. A lot of those contracts were cut, and they're coming back to market for RFP. They cut too much. Now we have the opportunity to bid on those RFPs that potentially we wouldn't have had that opportunity for a number of years. It's no longer just go with incumbent. Everyone wants to show that they're executing on synergies, reducing costs, and so forth, and having better technology. We're having really good success there on the public sector team submitting those RFPs. These are a much longer sales cycle, as you can imagine. Some of those are going to be year-plus sales cycles, and then there's ramp-up period as well because it follows the budget. On the federal side, budget renewals are around September. On the SLED, state, local, and education, that's usually around the July timeframe for the state and local budget renewals. The RFPs usually follow that as well. Those contracts can be material, as you can imagine. We've seen contracts, they're going to be the whale contracts, $10 million, $15 million contracts with Reed Elsevier LexisNexis. Even if we get a portion of that, that's going to be material to our revenue. Secondly, we're excited about the background screening space. We won one of the large payroll companies out there for their background screening process. They're in the process of onboarding. When Innovatives was purchased by Aperis and ultimately Equifax, we had a buy versus build decision. Do we continue to just service this industry behind the scenes, or do we go front-facing? We made the effort to build out the products. We already had the data, but how do we productize this and go to market with it? We started productizing it, and this year, we're now ready to go to market in a formal fashion. We had a lot of beta testing per se, where we got a lot of feedback from the different background screening players. It'd be really nice to do this if you can do X, Y, and Z. Now we're at the place where the product is fully functioning, should service, I call it, 99% of the use cases out there. Proof in the pudding, we won one of the largest payroll processors. We don't, we typically don't disclose names on the IDI side. We'll disclose names on the Forewarn side. If you look at our press releases, they're mostly all Forewarn related. That's by design because there's going to be real estate agents, hey, our sister association offers this product to their members as a member benefit. Why don't you? It's more of a marketing perspective on the Forewarn side. On the IDI side, as you can imagine, most of our customers don't want individuals knowing how they're doing their identity verification or where they're getting their data from. They're very hesitant on allowing us to press release their name. We also don't want the competition knowing where we're winning. From just a couple KPIs, actually on this slide, from a gross retention number, typically we say these businesses run from 90%-95%. Last quarter, we came in at 97% on the gross retention side. We continue to guide towards that 90% to 95%. We don't publicly disclose net revenue retention, but as you can imagine, we mimic those information solutions companies. Mid-teens, high mid-teens to upper, to 120%, from a net revenue retention perspective. What I understand, the revenue, the retention is just a decline. It's not that revenue. Correct. It's just gross revenue retention. Yeah, we're not accounting for any upsells or anything of that nature in this gross revenue retention. It is very interesting. Correct. Any other questions? Above the flags, could you go, would that be a bus to work or a car? [guess] Yeah. There are two clear. There is Clear at the airport, and then Thomson Reuters has a legacy product called Clear. That legacy product is mainly in the public sector space. It is a very clunky, dated product, but they have a huge presence in the public sector. We are starting to bump up against them. Yes, Clear like at the airport. They are running identity verification. They are using facial recognition. They are also scanning your ID and doing PII information behind that, validating that it is a valid ID, and then they are pulling up your picture as well with your facial recognition. That Clear would be a potential customer of us or one of our competitors because they need that PII information. It is a good question. How does artificial intelligence, whether a threat? Yep. A very valid question. I'll answer it a couple of different ways. We get that all the time, right? Because AI is out there. You can aggregate this, the data. ChatGPT is layered over the internet. Bad data in is bad data out, right? As you can imagine, banks can't clear transactions with ChatGPT. They can say, hey, tell me about John Smith. Who is John Smith? Is this the right individual? How are you leveraging AI today in a couple of different fashions? We have a high confidence data cohort. How do we leverage AI on top of it so we can go out and interact, customers can interact with our platform differently? Today, when you go into our online platform, you're entering a name, date of birth, social, something of that nature, and getting information back. What we're working on today is being more interactive. Tell me everything about Camilo Ramirez or something as simple, which sounds very simple, but it would require a number of searches. Is there a family member of Camilo Ramirez that has a violent criminal history, whatever you want to call it? Today what you'd have to do, you'd have to search Camilo Ramirez, go into each known associate, look at their criminal background, and that's going to take you time because depending on how many relatives, you're going to have to go through each individual relative. You can keep going down the family tree, as opposed to when you're interacting with the platform. You ask it that question and it'll either say yes, this family member, and it'll give you the family member name, has a criminal history, and it'll give you all that information. It's doing all those searches in subseconds. That's how we want to interact. Even on the, let's say, let's say you're a CVS. You want to understand the upward and downward mobility of the population within this intersection. We have all those data points of that population. Basically, their analyst is going to say, hey, typical crime rate in this corner is X, Y, and Z. Here's the upward mobility of individuals and here's the house prices that continue to go up. They're going to make a decision, do we build a CVS on this corner or not? It's aggregating all that data and giving you insight into a population as opposed to just individuals as well. From an operational standpoint, how we're leveraging AI, today, let's say for every, call it, thousand customers we onboard, we're going to have to add one or two additional credentialing individuals, right? How do we automate our internal processes so we can continue to expand on that margin? As we continue to grow, we don't have to add those one or two individuals for every thousand customers. It can be one individual for every 5,000 customers or something of that nature. How do we automate our processes internally so we can continue to extract margin in the future? From a margin perspective, just to note, at maturities, these businesses on the gross profit line, they're generating 90% plus gross profit margins. On the adjusted EBITDA, call it around 60% adjusted EBITDA, just because of that fixed cost nature of the data asset. That's how artificial intelligence helps you, though. Yeah, that's a good question, right? There are barriers to entry. As you can imagine, let's say you want to go out and start a business in this nature. You have the credit bureaus. They have the largest balance sheet, and as you can imagine, they have all the data in-house. When they wanted to get into the market, it was a buy versus build decision. Each iteration, they went out and bought the businesses just because of the amount of information that you have to gather and the type of information that's actually beneficial. A good example, early on, we didn't have business data. If me saying that, you're like, why wouldn't they have business data, right? It's very integral to understand who owns businesses, but it's a nice to have. It's a very expensive data set, and there's not much lift. It's low ROI on that business data. Early on, we didn't have business data. We'd go out and when we would go out to prospect customers, we'd say, hey, they would love our product. They would say, hey, but you don't have the business data. When you really dug in, they were doing a couple of searches a month on the business data. Ultimately, once our P&L supported purchasing that business data, we layered in that business data. Today we're starting to work on KYB, so Know Your Business. If we can understand everything about a consumer and individual as their sole data point, we can know everything about a business as well. LLCs, who are the true owners of these LLCs going down multiple businesses. Being able to come out in the future with a KYB product as well. There's the moat and then the know-how of what data to acquire. The credit bureaus have to have high confidence in you keeping that data safe, right? As you can imagine, they're not just going to give the whole U.S. population to anyone out on the street. There's a lot of risk associated with that. We're audited by the credit bureaus on a regular basis. We're PCI Level 1, SOC 1, SOC 2 certified, ISO 2700 as well. Safety is at the forefront of our data safety. I believe you had a question. How many sort of separate real estate deals do you have? How many not renew or in the end contract period? The second point question is, would Uber be a particular customer possible? Yeah, that's a potential use case, right? I'll answer that one and go back to your other one. Uber, they're doing background screening on drivers as well. They have that high transactions. They're doing identity verification, make sure that's a valid individual, that valid license and so forth. That's a potential use case. On the real estate side, let me flip to the side of it. Today we service over 575 real estate associations. Again, there's about 1,100 associations in the U.S. From a real estate, from a realtor standpoint, we're servicing just about 350,000 real estate individuals. There's about 1.2 million real estate individuals in the U.S. Of those that are truly in the real estate business, call it about 900,000. Real estate individuals are actually transacting more than one home a year, they just those that have a real estate license just for family and friends and so forth. There's no true competing product in that space. For lack of a better term, most of the competing products are going to be like find the body, so panic buttons once you're attacked and so forth. This is more of a proactive safety solution. Any last minute questions? Sure. No, I appreciate it, guys, for making it interactive. Thank you.

Speaker 4: Here again for day two of the Ideas Conference. The next company we have for you is Red Violet. They're a leading solutions provider in the ID verification sector. With us today, we've got the Vice President of Investor Relations, Camilo Ramirez. Camilo? Here again for day two of the Ideas Conference. here again for day two of the ideas conference The next company we have for you is Red Violet. the next company we have for you is red violet They're a leading solutions provider in the ID verification sector. they're a leading solutions provider in the id verification sector With us today, we've got the Vice President of Investor Relations, Camilo Ramirez. with us today we've got the vice president of investor relations camilo ramirez Camilo? camilo

Speaker 5: Thank you, Steven. Appreciate it. I'll give you guys a bit of a background about what we do, management background, and then we'll go over our use cases, exactly what we do. Let's make it conversational, so feel free to ask any questions. Stop me if you need clarification on anything, but we'll go from there. I'll quickly flip through the deck. I'll highlight a couple of pages, but preferences for conversation. What do we do? We do all things identity. High level, we aggregate disparate databases, call it liens, judgments, credit header data, anything and everything on every adult individual in the U.S. We aggregate that data and then sell it back out to different industries. We serve five different verticals: collections, financial and corporate risk, investigative, real estate, and what we call emerging markets. Thank you, Steven. thank you steven Appreciate it. appreciate it I'll give you guys a bit of a background about what we do, management background, and then we'll go over our use cases, exactly what we do. i'll give you guys a bit of a background about what we do management background and then we'll go over our use cases exactly what we do Let's make it conversational, so feel free to ask any questions. let's make it conversational so feel free to ask any questions Stop me if you need clarification on anything, but we'll go from there. stop me if you need clarification on anything but we'll go from there I'll quickly flip through the deck. i'll quickly flip through the deck I'll highlight a couple of pages, but preferences for conversation. i'll highlight a couple of pages but preferences for conversation What do we do? what do we do We do all things identity. we do all things identity High level, we aggregate disparate databases, call it liens, judgments, credit header data, anything and everything on every adult individual in the U.S. high level we aggregate disparate databases call it liens judgments credit header data anything and everything on every adult individual in the u.s We aggregate that data and then sell it back out to different industries. we aggregate that data and then sell it back out to different industries We serve five different verticals: collections, financial and corporate risk, investigative, real estate, and what we call emerging markets. we serve five different verticals collections financial and corporate risk investigative real estate and what we call emerging markets I'll dive deeper into that in a little bit, but let me take a step back and give you a little history about management. In the late 1990s, the management team built a business called Sizen in the same space, data fusion, identity verification. As you can imagine, technology then was very different. We built it in data rooms. There were ad hoc connections. You see Camilo and Steven always present them in an investigation as opposed to a more dynamic presentation where the Camilo result can be applicable only to the situation and don't present Steven. Ultimately, that was sold off to Reed Elsevier's LexisNexis for about $750 million. Non-competes expired. Team got back together. They started a company called TLO. It was being personally funded by one of the founders. He ended up passing away abruptly through the development phase. I'll dive deeper into that in a little bit, but let me take a step back and give you a little history about management. i'll dive deeper into that in a little bit but let me take a step back and give you a little history about management In the late 1990s, the management team built a business called Sizen in the same space, data fusion, identity verification. in the late 1990s the management team built a business called sizen in the same space data fusion identity verification As you can imagine, technology then was very different. as you can imagine technology then was very different We built it in data rooms. we built it in data rooms There were ad hoc connections. there were ad hoc connections You see Camilo and Steven always present them in an investigation as opposed to a more dynamic presentation where the Camilo result can be applicable only to the situation and don't present Steven. you see camilo and steven always present them in an investigation as opposed to a more dynamic presentation where the camilo result can be applicable only to the situation and don't present steven Ultimately, that was sold off to Reed Elsevier's LexisNexis for about $750 million. ultimately that was sold off to reed elsevier's lexisnexis for about $750 million Non-competes expired. non-competes expired Team got back together. team got back together They started a company called TLO. they started a company called tlo It was being personally funded by one of the founders. it was being personally funded by one of the founders He ended up passing away abruptly through the development phase. he ended up passing away abruptly through the development phase Revenue run rate at that time was about $25 million. They were losing anywhere from $1 million-$2 million a month. Ultimately, that was sold off to TransUnion prematurely, right? They were still going through the R&D phase. Non-competes expired. Team got back together, call it in 2014. I'll show you guys a little roadmap of the history. They got back together in 2014. As you can imagine, technology has changed drastically from the late 1990s-2014. We had AWS, so this iteration is cloud native. It was built in the cloud from the ground up. That's a big differentiator from the competition, the Sizen product and the TLO product that management built. You have LLM models, AI, so it's able to learn upon itself as you feed that data. It's making those connections, building new data points to go out and clear transactions. What do we do? Revenue run rate at that time was about $25 million. revenue run rate at that time was about $25 million They were losing anywhere from $1 million- $2 million a month. they were losing anywhere from $1 million- $2 million a month Ultimately, that was sold off to TransUnion prematurely, right? They were still going through the R&D phase. ultimately that was sold off to transunion prematurely right? they were still going through the r&d phase Non-competes expired. non-competes expired Team got back together, call it in 2014. team got back together call it in 2014 I'll show you guys a little roadmap of the history. i'll show you guys a little roadmap of the history They got back together in 2014. they got back together in 2014 As you can imagine, technology has changed drastically from the late 1990s- 2014. as you can imagine technology has changed drastically from the late 1990s- 2014 We had AWS, so this iteration is cloud native. we had aws so this iteration is cloud native It was built in the cloud from the ground up. it was built in the cloud from the ground up That's a big differentiator from the competition, the Sizen product and the TLO product that management built. that's a big differentiator from the competition the sizen product and the tlo product that management built You have LLM models, AI, so it's able to learn upon itself as you feed that data. you have llm models ai so it's able to learn upon itself as you feed that data It's making those connections, building new data points to go out and clear transactions. it's making those connections building new data points to go out and clear transactions What do we do? what do we do I'll give you a couple of use cases. We'll start with collections. It's the easiest one. Let's say you buy debt from Capital One. You have a million records. You want to go out and you buy a million records. You want to go out and validate that information. I'm getting kind of an echo in the back. You buy those million records. You go out. You want to validate the information, right party contact information. Have they filed for a bankruptcy? We'll pend that information, send it right back. We go to real estate. We serve real estate in two different manners. On our IDI side, it's going to be more of a marketing play. I want a list of individuals that are 16 and above that live in a two-story home, propensity to sell. I'll give you a couple of use cases. i'll give you a couple of use cases We'll start with collections. we'll start with collections It's the easiest one. it's the easiest one Let's say you buy debt from Capital One. let's say you buy debt from capital one You have a million records. you have a million records You want to go out and you buy a million records. you want to go out and you buy a million records You want to go out and validate that information. you want to go out and validate that information I'm getting kind of an echo in the back. i'm getting kind of an echo in the back You buy those million records. you buy those million records You go out. you go out You want to validate the information, right party contact information. you want to validate the information right party contact information Have they filed for a bankruptcy? have they filed for a bankruptcy We'll pend that information, send it right back. we'll pend that information send it right back We go to real estate. we go to real estate We serve real estate in two different manners. we serve real estate in two different manners On our IDI side, it's going to be more of a marketing play. on our idi side it's going to be more of a marketing play I want a list of individuals that are 16 and above that live in a two-story home, propensity to sell. i want a list of individuals that are 16 and above that live in a two-story home propensity to sell Under our brand Forewarn, which is basically just a skin and application that was layered over our core platform, that's sold directly towards the real estate community. We go after associations. That is going to be, let's say, you're a real estate agent. You get a phone call. I want to see this $3 million home. I'm only in town till tomorrow. My name's John Smith, purposely using a very generic name. I'm going to show up in a Mercedes S-Class. What it does when that phone call comes in, it does a reverse phone number search and it comes up, brings up their background. You don't see John Smith. You see a different name. You don't see that Mercedes S-Class. What you do see is this individual just got out of prison for sexual assault. You're not going to show that home. Under our brand Forewarn, which is basically just a skin and application that was layered over our core platform, that's sold directly towards the real estate community. under our brand forewarn which is basically just a skin and application that was layered over our core platform that's sold directly towards the real estate community We go after associations. we go after associations That is going to be, let's say, you're a real estate agent. that is going to be let's say you're a real estate agent You get a phone call. you get a phone call I want to see this $3 million home. i want to see this $3 million home I'm only in town till tomorrow. i'm only in town till tomorrow My name's John Smith, purposely using a very generic name. my name's john smith purposely using a very generic name I'm going to show up in a Mercedes S-Class. i'm going to show up in a mercedes s-class What it does when that phone call comes in, it does a reverse phone number search and it comes up, brings up their background. what it does when that phone call comes in it does a reverse phone number search and it comes up brings up their background You don't see John Smith. you don't see john smith You see a different name. you see a different name You don't see that Mercedes S-Class. you don't see that mercedes s-class What you do see is this individual just got out of prison for sexual assault. what you do see is this individual just got out of prison for sexual assault You're not going to show that home. you're not going to show that home What we noticed was that there was a lot of crime committed against realtors. This is more of a safety product for the realtors. We go after the real estate associations. Today we serve about 500 plus real estate associations. There's, call it about 1,100 associations in the U.S. The associations, what they do, they buy this product for their members. It's a member benefit for the users. If you're part of, let's call it down by us, Palm Beach Real Estate Group, the state of Florida purchased Forewarn for all the associations underneath it. They'll just sign up. It's free use, unlimited use, and that's a SaaS product. If you're an individual coming on board, not under an association, that's going to run you $20-$25 a month. Because we sell directly to the associations, economies of scale, those prices are reduced drastically. What we noticed was that there was a lot of crime committed against realtors. what we noticed was that there was a lot of crime committed against realtors This is more of a safety product for the realtors. this is more of a safety product for the realtors We go after the real estate associations. we go after the real estate associations Today we serve about 500 plus real estate associations. today we serve about 500 plus real estate associations There's, call it about 1,100 associations in the U.S. there's call it about 1,100 associations in the u.s The associations, what they do, they buy this product for their members. the associations what they do they buy this product for their members It's a member benefit for the users. it's a member benefit for the users If you're part of, let's call it down by us, Palm Beach Real Estate Group, the state of Florida purchased Forewarn for all the associations underneath it. if you're part of let's call it down by us palm beach real estate group the state of florida purchased forewarn for all the associations underneath it They'll just sign up. they'll just sign up It's free use, unlimited use, and that's a SaaS product. it's free use unlimited use and that's a saas product If you're an individual coming on board, not under an association, that's going to run you $20- $25 a month. if you're an individual coming on board not under an association that's going to run you $20- $25 a month Because we sell directly to the associations, economies of scale, those prices are reduced drastically. because we sell directly to the associations economies of scale those prices are reduced drastically On the Florida, Florida is one of the largest associations in the U.S. That's going to be a couple of dollars per member. They have about 400,000 members. It just depends on the scale of the association. Next, we have financial and corporate risk. Under financial corporate risk, we have background screening. I'll give you a couple of use cases there. Let's say you're a Walmart. You have an applicant. They're applying for a job. They enter four addresses. I'll disclose this name because we disclosed it publicly early on. They'll go to a company called Innovative. Innovative is the background screener. Innovative will call out to us, is this information accurate and complete? We'll say it's accurate, but it's incomplete. They left off the fifth address, and that's usually where the criminal record lies. On the Florida, Florida is one of the largest associations in the U.S. on the florida florida is one of the largest associations in the u.s That's going to be a couple of dollars per member. that's going to be a couple of dollars per member They have about 400,000 members. they have about 400,000 members It just depends on the scale of the association. it just depends on the scale of the association Next, we have financial and corporate risk. next we have financial and corporate risk Under financial corporate risk, we have background screening. under financial corporate risk we have background screening I'll give you a couple of use cases there. i'll give you a couple of use cases there Let's say you're a Walmart. let's say you're a walmart You have an applicant. you have an applicant They're applying for a job. they're applying for a job They enter four addresses. they enter four addresses I'll disclose this name because we disclosed it publicly early on. i'll disclose this name because we disclosed it publicly early on They'll go to a company called Innovative. they'll go to a company called innovative Innovative is the background screener. innovative is the background screener Innovative will call out to us, is this information accurate and complete? innovative will call out to us is this information accurate and complete We'll say it's accurate, but it's incomplete. we'll say it's accurate but it's incomplete They left off the fifth address, and that's usually where the criminal record lies. they left off the fifth address and that's usually where the criminal record lies Innovative will go pull that criminal record at that fifth address and ultimately send that information back to Walmart so they can make their hiring decision. A use case on Innovative. We won that business early on from TransUnion. They're doing low five figures a month. Ultimately, they grew up into well into the six figures. They were sold to Aperis, another identity player. Ultimately, Aperis was purchased by Equifax. As you can imagine, Equifax has all the data in the world, right? They have all that information, but they don't have an aggregation platform. They have the individual data points. At that time, during that acquisition, the Innovative contract was up for renewal. Equifax reached out, hey, can we get an extension on this contract, three-month extension? We said, of course, we understand. We understand you're trying to execute on synergies. Innovative will go pull that criminal record at that fifth address and ultimately send that information back to Walmart so they can make their hiring decision. innovative will go pull that criminal record at that fifth address and ultimately send that information back to walmart so they can make their hiring decision A use case on Innovative. a use case on innovative We won that business early on from TransUnion. we won that business early on from transunion They're doing low five figures a month. they're doing low five figures a month Ultimately, they grew up into well into the six figures. ultimately they grew up into well into the six figures They were sold to Aperis, another identity player. they were sold to aperis another identity player Ultimately, Aperis was purchased by Equifax. ultimately aperis was purchased by equifax As you can imagine, Equifax has all the data in the world, right? as you can imagine equifax has all the data in the world right They have all that information, but they don't have an aggregation platform. they have all that information but they don't have an aggregation platform They have the individual data points. they have the individual data points At that time, during that acquisition, the Innovative contract was up for renewal. at that time during that acquisition the innovative contract was up for renewal Equifax reached out, hey, can we get an extension on this contract, three-month extension? equifax reached out hey can we get an extension on this contract three-month extension We said, of course, we understand. we said of course we understand We understand you're trying to execute on synergies. we understand you're trying to execute on synergies We don't think you're going to have the same throughput or lift on data, but we're here to help if you need anything. They came back, asked for a couple more extensions. Ultimately, they signed a multi-year agreement. They couldn't recreate the data quality or the throughput on their side, even though they have all the individual data points. We have a really good relationship there, multi-year contract. We can go into investigative vertical. That's going to be, for example, law enforcement. You get pulled over by law enforcement. Very easy. Our system or one of our legacy products that we sold off. They'll run a plate, do an investigation. How we differentiate in that space is we have a very neat, mobile application where, let me just flip to another side. A neat application. Let's say you had a witness. I saw this red F-150 in this intersection. We don't think you're going to have the same throughput or lift on data, but we're here to help if you need anything. we don't think you're going to have the same throughput or lift on data but we're here to help if you need anything They came back, asked for a couple more extensions. they came back asked for a couple more extensions Ultimately, they signed a multi-year agreement. ultimately they signed a multi-year agreement They couldn't recreate the data quality or the throughput on their side, even though they have all the individual data points. they couldn't recreate the data quality or the throughput on their side even though they have all the individual data points We have a really good relationship there, multi-year contract. we have a really good relationship there multi-year contract We can go into investigative vertical. we can go into investigative vertical That's going to be, for example, law enforcement. that's going to be for example law enforcement You get pulled over by law enforcement. you get pulled over by law enforcement Very easy. very easy Our system or one of our legacy products that we sold off. our system or one of our legacy products that we sold off They'll run a plate, do an investigation. they'll run a plate do an investigation How we differentiate in that space is we have a very neat, mobile application where, let me just flip to another side. how we differentiate in that space is we have a very neat mobile application where let me just flip to another side A neat application. a neat application Let's say you had a witness. let's say you had a witness I saw this red F-150 in this intersection. i saw this red f-150 in this intersection It was a hit and run, something of that nature, crime committed. The investigator can come in, basically do a geofence, drop a pin within that circumference of that area and say, give me all estate, public sector. Public sector, we're really excited about the public sector space. We made a hire, call it just over a year now, Jonathan McDonald. He led the public sector at TransUnion, built it up from zero to where it is today at TransUnion. We built up a team around him as well, call it around 15 individuals-20 individuals. Public sector, as you can imagine, your typical three-letter agencies doing investigations on individuals. You have ICE doing investigations as well. Those are all opportunities we can win. It was a hit and run, something of that nature, crime committed. it was a hit and run something of that nature crime committed The investigator can come in, basically do a geofence, drop a pin within that circumference of that area and say, give me all e state, public sector. the investigator can come in basically do a geofence drop a pin within that circumference of that area and say give me all e state public sector Public sector, we're really excited about the public sector space. public sector we're really excited about the public sector space We made a hire, call it just over a year now, Jonathan McDonald. we made a hire call it just over a year now jonathan mcdonald He led the public sector at TransUnion, built it up from zero to where it is today at TransUnion. he led the public sector at transunion built it up from zero to where it is today at transunion We built up a team around him as well, call it around 15 individuals- 20 individuals. we built up a team around him as well call it around 15 individuals- 20 individuals Public sector, as you can imagine, your typical three-letter agencies doing investigations on individuals. public sector as you can imagine your typical three-letter agencies doing investigations on individuals You have ICE doing investigations as well. you have ice doing investigations as well Those are all opportunities we can win. those are all opportunities we can win Even outside of that, where there's niche use cases, for example, we just won one of the largest toll authorities in the U.S., without naming specifically which one. Down by us in South Florida, we have what they call the SunPass program for the Florida Turnpike. They can do bill by plate. Let's say you're running through the toll, you don't have this SunPass to automatically pay for it. It's going to run, it's going to take a picture of the plate. Then they have to do a search based off that plate to understand who's the owner of this vehicle, registration, search, and then they have to figure out the address, send out a bill to that vehicle owner. If that owner doesn't pay that bill, it goes through a collection process and they have to go through right party contact information. Even outside of that, where there's niche use cases, for example, we just won one of the largest toll authorities in the U.S., without naming specifically which one. even outside of that where there's niche use cases for example we just won one of the largest toll authorities in the u.s without naming specifically which one Down by us in South Florida, we have what they call the SunPass program for the Florida Turnpike. down by us in south florida we have what they call the sunpass program for the florida turnpike They can do bill by plate. they can do bill by plate Let's say you're running through the toll, you don't have this SunPass to automatically pay for it. let's say you're running through the toll you don't have this sunpass to automatically pay for it It's going to run, it's going to take a picture of the plate. it's going to run it's going to take a picture of the plate Then they have to do a search based off that plate to understand who's the owner of this vehicle, registration, search, and then they have to figure out the address, send out a bill to that vehicle owner. then they have to do a search based off that plate to understand who's the owner of this vehicle registration search and then they have to figure out the address send out a bill to that vehicle owner If that owner doesn't pay that bill, it goes through a collection process and they have to go through right party contact information. if that owner doesn't pay that bill it goes through a collection process and they have to go through right party contact information We won this contract, just call it maybe a quarter ago. We disclosed it on our last earnings call. That's going to be well into the seven figures once that's up and running. That's one of 50, call it. Some other niche use cases are like homestead exemptions. Are these individuals truly living in that home that they're claiming a homestead exemption? Doing an address verification, homeowner verification, and so forth, or even something as niche as, is this child truly living in this public school boundary, right? As you can imagine, there's a lot of situations where parents say, hey, my child's living with the grandparents so they can go to this A-rated school in a public school system, but in reality, they're not. We won this contract, just call it maybe a quarter ago. we won this contract just call it maybe a quarter ago We disclosed it on our last earnings call. we disclosed it on our last earnings call That's going to be well into the seven figures once that's up and running. that's going to be well into the seven figures once that's up and running That's one of 50, call it. that's one of 50 call it Some other niche use cases are like homestead exemptions. some other niche use cases are like homestead exemptions Are these individuals truly living in that home that they're claiming a homestead exemption? are these individuals truly living in that home that they're claiming a homestead exemption Doing an address verification, homeowner verification, and so forth, or even something as niche as, is this child truly living in this public school boundary, right? doing an address verification homeowner verification and so forth or even something as niche as is this child truly living in this public school boundary right As you can imagine, there's a lot of situations where parents say, hey, my child's living with the grandparents so they can go to this A-rated school in a public school system, but in reality, they're not. as you can imagine there's a lot of situations where parents say hey my child's living with the grandparents so they can go to this a-rated school in a public school system but in reality they're not We have school districts reaching out for that type of data, address verifications essentially to validate that those children truly live in that area and they are eligible to attend that school. Those are some of those niche use cases in the public sector that we're really excited about. Those five verticals, we say revenue is pretty evenly distributed, call it 20% across the board. I'll pause there to see if you guys have any questions. If not, then I'll jump into the financial model. We like to say our data is fixed cost in nature. We go out, do long-term agreements with the data providers. As you can imagine, some of those are going to be credit bureaus, niche data aggregators that are getting data from different municipalities, aggregating that information, we'll consume it. We do long-term contracts on that, and it's fixed cost for unlimited use. We have school districts reaching out for that type of data, address verifications essentially to validate that those children truly live in that area and they are eligible to attend that school. we have school districts reaching out for that type of data address verifications essentially to validate that those children truly live in that area and they are eligible to attend that school Those are some of those niche use cases in the public sector that we're really excited about. those are some of those niche use cases in the public sector that we're really excited about Those five verticals, we say revenue is pretty evenly distributed, call it 20% across the board. those five verticals we say revenue is pretty evenly distributed call it 20% across the board I'll pause there to see if you guys have any questions. i'll pause there to see if you guys have any questions If not, then I'll jump into the financial model. if not then i'll jump into the financial model We like to say our data is fixed cost in nature. we like to say our data is fixed cost in nature We go out, do long-term agreements with the data providers. we go out do long-term agreements with the data providers As you can imagine, some of those are going to be credit bureaus, niche data aggregators that are getting data from different municipalities, aggregating that information, we'll consume it. as you can imagine some of those are going to be credit bureaus niche data aggregators that are getting data from different municipalities aggregating that information we'll consume it We do long-term contracts on that, and it's fixed cost for unlimited use. we do long-term contracts on that and it's fixed cost for unlimited use Every additional dollar we're bringing in today is nearly 100% contribution margin. Our last quarter on the gross profit line, we reported just over 80% gross profit. We're starting to approach 40% adjusted EBITDA margins. If you take a look at our financials, our disclosures in the 10K, you'll see that one data provider accounts for 40% of our data costs. That is not just one data asset, its multiple data assets. We have a really good relationship with that credit bureau. Ultimately, we just renewed that contract for another five years here a couple of months ago with minimal incremental increase. The renewal prior to that was flat. We always get the question, hey, if you see these, if the credit bureaus are seeing the type of margins you guys are having, why wouldn't they just escalate the price on you? One, we're multi-sourced on every data point. Every additional dollar we're bringing in today is nearly 100% contribution margin. every additional dollar we're bringing in today is nearly 100% contribution margin Our last quarter on the gross profit line, we reported just over 80% gross profit. our last quarter on the gross profit line we reported just over 80% gross profit We're starting to approach 40% adjusted EBITDA margins. we're starting to approach 40% adjusted ebitda margins If you take a look at our financials, our disclosures in the 10K, you'll see that one data provider accounts for 40% of our data costs. if you take a look at our financials our disclosures in the 10k you'll see that one data provider accounts for 40% of our data costs That is not just one data asset, its multiple data assets. that is not just one data asset its multiple data assets We have a really good relationship with that credit bureau. we have a really good relationship with that credit bureau Ultimately, we just renewed that contract for another five years here a couple of months ago with minimal incremental increase. ultimately we just renewed that contract for another five years here a couple of months ago with minimal incremental increase The renewal prior to that was flat. the renewal prior to that was flat We always get the question, hey, if you see these, if the credit bureaus are seeing the type of margins you guys are having, why wouldn't they just escalate the price on you? we always get the question hey if you see these if the credit bureaus are seeing the type of margins you guys are having why wouldn't they just escalate the price on you One, we're multi-sourced on every data point. one we're multi-sourced on every data point If that does occur, we'll just say, hey, we're not going to accept that, and then we'll just bring our tier two data provider up to tier one and then continue to move on. We can give those data points back. If those data points go back, we don't lose the learnings, those connections between brother, son, family connections, those learnings. Those are all proprietary to us. Those learnings stay with us and data points go back, and we just bring up our tier two. The last couple of renewals, we have a really good relationship, and we renewed pretty much flat from that in our last renewal. No concern there. If that does occur, we'll just say, hey, we're not going to accept that, and then we'll just bring our tier two data provider up to tier one and then continue to move on. if that does occur we'll just say hey we're not going to accept that and then we'll just bring our tier two data provider up to tier one and then continue to move on We can give those data points back. we can give those data points back If those data points go back, we don't lose the learnings, those connections between brother, son, family connections, those learnings. if those data points go back we don't lose the learnings those connections between brother son family connections those learnings Those are all proprietary to us. those are all proprietary to us Those learnings stay with us and data points go back, and we just bring up our tier two. those learnings stay with us and data points go back and we just bring up our tier two The last couple of renewals, we have a really good relationship, and we renewed pretty much flat from that in our last renewal. the last couple of renewals we have a really good relationship and we renewed pretty much flat from that in our last renewal No concern there. no concern there Are you dependent upon your apps, and do you have some special technology that's not able to do so? Sorry. Are you dependent upon your apps, and do you have some special technology that's not able to do so? are you dependent upon your apps and do you have some special technology that's not able to do so Sorry. sorry No, no, no. No patents, right? We don't have any patents. It's the technology that we have that aggregates that information and links those data points together and continues to learn from that information. A good example that I like to give is, in certain industries, they have what they call the waterfall effect. You'll go into an industry and try to gain a customer and they're going to, we will go in and say, hey, let us come in at tier three on your waterfall. What that means is they're sending a million records to, let's say, TransUnion. TransUnion is going to have a certain hit rate, call it 60%-70%. Some of that's going to fall out. They don't have information. They'll send it to a second provider, Reed Elsevier LexisNexis Accurint product. They'll be able to hit some amount on that fallout as well. No, no, no. no no no No patents, right? no patents right We don't have any patents. we don't have any patents It's the technology that we have that aggregates that information and links those data points together and continues to learn from that information. it's the technology that we have that aggregates that information and links those data points together and continues to learn from that information A good example that I like to give is, in certain industries, they have what they call the waterfall effect. a good example that i like to give is in certain industries they have what they call the waterfall effect You'll go into an industry and try to gain a customer and they're going to, we will go in and say, hey, let us come in at tier three on your waterfall. you'll go into an industry and try to gain a customer and they're going to we will go in and say hey let us come in at tier three on your waterfall What that means is they're sending a million records to, let's say, TransUnion. what that means is they're sending a million records to let's say transunion TransUnion is going to have a certain hit rate, call it 60%- 70%. transunion is going to have a certain hit rate call it 60%- 70% Some of that's going to fall out. some of that's going to fall out They don't have information. they don't have information They'll send it to a second provider, Reed Elsevier LexisNexis Accurint product. they'll send it to a second provider reed elsevier lexisnexis accurint product They'll be able to hit some amount on that fallout as well. they'll be able to hit some amount on that fallout as well We'll say, hey, let us come in third. We know we have high confidence in our data assets and we'll get a high hit rate on that. We'll come in even though it's been scrubbed twice already and we'll get a high confidence hit rate. That's how we'll start moving up the waterfall as well. If you have a high hit rate, the customers want you to be first in waterfall because you get economies of scale. As you go down the waterfall, it gets more expensive for them. As you can imagine, they're pushing less, less data through you. It's what we do with that data and how we aggregate that data, as opposed to just hustling or just reselling data points, right? Because a bankruptcy is just a bankruptcy. It doesn't tell you anything. As you're transacting in the commerce today, you're leaving a footprint every single day. We'll say, hey, let us come in third. we'll say hey let us come in third We know we have high confidence in our data assets and we'll get a high hit rate on that. we know we have high confidence in our data assets and we'll get a high hit rate on that We'll come in even though it's been scrubbed twice already and we'll get a high confidence hit rate. we'll come in even though it's been scrubbed twice already and we'll get a high confidence hit rate That's how we'll start moving up the waterfall as well. that's how we'll start moving up the waterfall as well If you have a high hit rate, the customers want you to be first in waterfall because you get economies of scale. if you have a high hit rate the customers want you to be first in waterfall because you get economies of scale As you go down the waterfall, it gets more expensive for them. as you go down the waterfall it gets more expensive for them As you can imagine, they're pushing less, less data through you. as you can imagine they're pushing less less data through you It's what we do with that data and how we aggregate that data, as opposed to just hustling or just reselling data points, right? it's what we do with that data and how we aggregate that data as opposed to just hustling or just reselling data points right Because a bankruptcy is just a bankruptcy. because a bankruptcy is just a bankruptcy It doesn't tell you anything. it doesn't tell you anything As you're transacting in the commerce today, you're leaving a footprint every single day. as you're transacting in the commerce today you're leaving a footprint every single day Banks for like know your customer or loan decisions can't rely on data from yesterday or the day before. They have to have the most recent data. They want to understand, did you just file for a bankruptcy? Did something occur? Was there a lien or judgment placed on your property or anything of that nature? You need the most current information. Banks for like know your customer or loan decisions can't rely on data from yesterday or the day before. banks for like know your customer or loan decisions can't rely on data from yesterday or the day before They have to have the most recent data. they have to have the most recent data They want to understand, did you just file for a bankruptcy? they want to understand did you just file for a bankruptcy Did something occur? did something occur Was there a lien or judgment placed on your property or anything of that nature? was there a lien or judgment placed on your property or anything of that nature You need the most current information. you need the most current information What about the international market? What about the international market? what about the international market Today, we're not in the international space. We have a lot of go-get in the U.S. Today we have about 9,500 customers. The Accurint product has about 400,000 customers, and then the TLO product has anywhere from 60,000 to 70,000 customers. We're just scratching the surface. We're powering seven of the top 10 identity players, to the likes of, call it, Prove, Jumio, Econo, now owned by Mastercard. They all have their own niche way of doing identity verification, how to clear a transaction. Some of them are going to be mobile authentication. Let's say you're logging into Bank of America, Wells Fargo, using facial recognition. What's actually happening behind the scenes is saying, hey, this mobile ID belongs to Camilo Ramirez as a match to the bank side. Here's the PII information. Yes, grant access. If not, ask security questions of that nature. Today, we're not in the international space. today we're not in the international space We have a lot of go-get in the U.S. we have a lot of go-get in the u.s Today we have about 9,500 customers. today we have about 9,500 customers The Accurint product has about 400,000 customers, and then the TLO product has anywhere from 60,000 to 70,000 customers. the accurint product has about 400,000 customers and then the tlo product has anywhere from 60,000 to 70,000 customers We're just scratching the surface. we're just scratching the surface We're powering seven of the top 10 identity players, to the likes of, call it, Prove, Jumio, Econo, now owned by Mastercard. we're powering seven of the top 10 identity players to the likes of call it prove jumio econo now owned by mastercard They all have their own niche way of doing identity verification, how to clear a transaction. they all have their own niche way of doing identity verification how to clear a transaction Some of them are going to be mobile authentication. some of them are going to be mobile authentication Let's say you're logging into Bank of America, Wells Fargo, using facial recognition. let's say you're logging into bank of america wells fargo using facial recognition What's actually happening behind the scenes is saying, hey, this mobile ID belongs to Camilo Ramirez as a match to the bank side. what's actually happening behind the scenes is saying hey this mobile id belongs to camilo ramirez as a match to the bank side Here's the PII information. here's the pii information Yes, grant access. yes grant access If not, ask security questions of that nature. if not ask security questions of that nature Or they're going to be doing document verification, take a selfie, load a picture of your ID, and they're going to do the identity verification on that physical document. It's still pulling PII information behind the scenes. Those providers have the technology, the front-facing solution, but they don't have the data in-house. Every time they're clearing a transaction, they have to call out to someone like us or one of our competitors. I think there was a question over here. Or they're going to be doing document verification, take a selfie, load a picture of your ID, and they're going to do the identity verification on that physical document. or they're going to be doing document verification take a selfie load a picture of your id and they're going to do the identity verification on that physical document It's still pulling PII information behind the scenes. it's still pulling pii information behind the scenes Those providers have the technology, the front-facing solution, but they don't have the data in-house. those providers have the technology the front-facing solution but they don't have the data in-house Every time they're clearing a transaction, they have to call out to someone like us or one of our competitors. every time they're clearing a transaction they have to call out to someone like us or one of our competitors I think there was a question over here. i think there was a question over here Can you talk about the switching costs for your system? Can you talk about the switching costs for your system? can you talk about the switching costs for your system It's a good question. It's a good question. it's a good question Is it difficult if someone's there to know? Is it difficult if someone's there to know? is it difficult if someone's there to know No, so it's use case dependent, right? If someone's coming on board using our online platform, there's no use cost, no switching costs. They're just going through our credentialing process. We're validating that they have a valid use case, and then they can start using our system. That's how we win business too, right? Because they're not coming in, dropping a team, building out a whole platform. On the API side, our API is very flexible. We like to say it's very customizable, and behind the scenes, it's just turning on and turning off levers depending on what they need. Usually they're going to have teams on their side and they're up and running between 24 and 48 hours. No, so it's use case dependent, right? no so it's use case dependent right If someone's coming on board using our online platform, there's no use cost, no switching costs. if someone's coming on board using our online platform there's no use cost no switching costs They're just going through our credentialing process. they're just going through our credentialing process We're validating that they have a valid use case, and then they can start using our system. we're validating that they have a valid use case and then they can start using our system That's how we win business too, right? that's how we win business too right Because they're not coming in, dropping a team, building out a whole platform. because they're not coming in dropping a team building out a whole platform On the API side, our API is very flexible. on the api side our api is very flexible We like to say it's very customizable, and behind the scenes, it's just turning on and turning off levers depending on what they need. we like to say it's very customizable and behind the scenes it's just turning on and turning off levers depending on what they need Usually they're going to have teams on their side and they're up and running between 24 and 48 hours. usually they're going to have teams on their side and they're up and running between 24 and 48 hours A good example there on switching costs and ease of use on the API side: a number of years ago, we had a customer, they were doing well into the six figures a month. They were an identity verification, mobile, they do mobile authentication. What they're doing is just the example I gave. They were raising capital. TransUnion ended up leading that capital raise. The TransUnion CEO gave them a call and said, hey, for us to close this transaction, you're going to have to stop using Red Violet's IDI data and move all your consumption to us. The customer gave us a call, told us what was going on. We said, we understand, we would do the same if we needed the capital. We're here if you need anything. We saw that revenue drop off pretty much immediately. Within a couple of months, we started to pick back up. A good example there on switching costs and ease of use on the API side: a number of years ago, we had a customer, they were doing well into the six figures a month. a good example there on switching costs and ease of use on the api side a number of years ago we had a customer they were doing well into the six figures a month They were an identity verification, mobile, they do mobile authentication. they were an identity verification mobile they do mobile authentication What they're doing is just the example I gave. what they're doing is just the example i gave They were raising capital. they were raising capital TransUnion ended up leading that capital raise. transunion ended up leading that capital raise The TransUnion CEO gave them a call and said, hey, for us to close this transaction, you're going to have to stop using Red Violet's IDI data and move all your consumption to us. the transunion ceo gave them a call and said hey for us to close this transaction you're going to have to stop using red violet's idi data and move all your consumption to us The customer gave us a call, told us what was going on. the customer gave us a call told us what was going on We said, we understand, we would do the same if we needed the capital. we said we understand we would do the same if we needed the capital We're here if you need anything. we're here if you need anything We saw that revenue drop off pretty much immediately. we saw that revenue drop off pretty much immediately Within a couple of months, we started to pick back up. within a couple of months we started to pick back up What came out of it was that once they switched to the TransUnion product, they basically couldn't handle the throughput and the latency on the platform, right? Because they're building a data room as opposed to the cloud. During peak productivity hours, their customers were calling them and complaining, hey, I sent this call through. I'm not, I'm getting false positives or I'm not even getting a response. Ultimately, that business came back to us pretty shortly after. The CEO gave us a call. They said, hey, TransUnion said they can build us a custom API. Just give us a month or two. They said that you guys built us a custom API. We reminded them, hey, you guys were up and running within 48 hours. There's nothing custom about it. To this day, years later, they're continuing to grow with us, even though they have that relationship with TransUnion. What came out of it was that once they switched to the TransUnion product, they basically couldn't handle the throughput and the latency on the platform, right? what came out of it was that once they switched to the transunion product they basically couldn't handle the throughput and the latency on the platform right Because they're building a data room as opposed to the cloud. because they're building a data room as opposed to the cloud During peak productivity hours, their customers were calling them and complaining, hey, I sent this call through. during peak productivity hours their customers were calling them and complaining hey i sent this call through I'm not, I'm getting false positives or I'm not even getting a response. i'm not i'm getting false positives or i'm not even getting a response Ultimately, that business came back to us pretty shortly after. ultimately that business came back to us pretty shortly after The CEO gave us a call. the ceo gave us a call They said, hey, TransUnion said they can build us a custom API. they said hey transunion said they can build us a custom api Just give us a month or two. just give us a month or two They said that you guys built us a custom API. they said that you guys built us a custom api We reminded them, hey, you guys were up and running within 48 hours. we reminded them hey you guys were up and running within 48 hours There's nothing custom about it. there's nothing custom about it To this day, years later, they're continuing to grow with us, even though they have that relationship with TransUnion. to this day years later they're continuing to grow with us even though they have that relationship with transunion Would it be fair to say that you've got to stay ahead of the technology? Would it be fair to say that you've got to stay ahead of the technology? would it be fair to say that you've got to stay ahead of the technology That's a fair statement. Yeah. We're continuing to build that moat around our platform. You have TransUnion, you have Reed Elsevier LexisNexis. They both committed, I believe TransUnion committed about $200 million-$250 million a number of years ago to get their platform into the cloud. We know they're not going to get that platform. We like to give the example, if you take a 757, you want to make it much more efficient, fuel efficient, you're not going to just take parts off, put parts on. You're going to start from the ground up. Once you start removing a couple of pieces from here and here, there's going to be downstream impacts that they're not going to be able to correct within the cloud. They have to start from the bottom up. That's a fair statement. that's a fair statement Yeah. yeah We're continuing to build that moat around our platform. we're continuing to build that moat around our platform You have TransUnion, you have Reed Elsevier LexisNexis. you have transunion you have reed elsevier lexisnexis They both committed, I believe TransUnion committed about $200 million- $250 million a number of years ago to get their platform into the cloud. they both committed i believe transunion committed about $200 million- $250 million a number of years ago to get their platform into the cloud We know they're not going to get that platform. we know they're not going to get that platform We like to give the example, if you take a 757, you want to make it much more efficient, fuel efficient, you're not going to just take parts off, put parts on. we like to give the example if you take a 757 you want to make it much more efficient fuel efficient you're not going to just take parts off put parts on You're going to start from the ground up. you're going to start from the ground up Once you start removing a couple of pieces from here and here, there's going to be downstream impacts that they're not going to be able to correct within the cloud. once you start removing a couple of pieces from here and here there's going to be downstream impacts that they're not going to be able to correct within the cloud They have to start from the bottom up. they have to start from the bottom up We're continuing to build that moat, one through the technology we're using, the throughput, the latency, and then also just from product features as well. Something as simple as, when you're doing a search on the individual, understanding is this relative, is this relative of that individual, is it its mother, father, sibling, cousin, or anything of that nature? We get that granular based off of certain algorithms we have. We're able to tell you what type of relative that is as opposed to the competition. They're just going to tell you it's an associate. They don't have that level of granularity. I think I was going through the financial model. Fixed cost model, nearly every additional dollar is 100% contribution margin. Once you get below the gross profit margin, we like to contract everything long-term contracts to any of our vendors. We're continuing to build that moat, one through the technology we're using, the throughput, the latency, and then also just from product features as well. we're continuing to build that moat one through the technology we're using the throughput the latency and then also just from product features as well Something as simple as, when you're doing a search on the individual, understanding is this relative, is this relative of that individual, is it its mother, father, sibling, cousin, or anything of that nature? something as simple as when you're doing a search on the individual understanding is this relative is this relative of that individual is it its mother father sibling cousin or anything of that nature We get that granular based off of certain algorithms we have. we get that granular based off of certain algorithms we have We're able to tell you what type of relative that is as opposed to the competition. we're able to tell you what type of relative that is as opposed to the competition They're just going to tell you it's an associate. they're just going to tell you it's an associate They don't have that level of granularity. they don't have that level of granularity I think I was going through the financial model. i think i was going through the financial model Fixed cost model, nearly every additional dollar is 100% contribution margin. fixed cost model nearly every additional dollar is 100% contribution margin Once you get below the gross profit margin, we like to contract everything long-term contracts to any of our vendors. once you get below the gross profit margin we like to contract everything long-term contracts to any of our vendors Most of our variability is going to be headcount, and that's going to be related to sales and commissions. We verticalize all our teams into subject matter experts. We have a real estate team, law enforcement team, and so forth. There's a couple of verticals we're really excited about for the coming years. One, I alluded to public sector. As you can imagine, that's a very large TAM. With everything that occurred earlier in the year with DOGE, that's a tailwind for us. It worked out perfectly. We weren't deeply penetrated into the public sector market, so we didn't lose contracts. Historically, you have to wait until those RFPs come up. A lot of those contracts were cut, and they're coming back to market for RFP. They cut too much. Most of our variability is going to be headcount, and that's going to be related to sales and commissions. most of our variability is going to be headcount and that's going to be related to sales and commissions We verticalize all our teams into subject matter experts. we verticalize all our teams into subject matter experts We have a real estate team, law enforcement team, and so forth. we have a real estate team law enforcement team and so forth There's a couple of verticals we're really excited about for the coming years. there's a couple of verticals we're really excited about for the coming years One, I alluded to public sector. one i alluded to public sector As you can imagine, that's a very large TAM. as you can imagine that's a very large tam With everything that occurred earlier in the year with DOGE, that's a tailwind for us. with everything that occurred earlier in the year with doge that's a tailwind for us It worked out perfectly. it worked out perfectly We weren't deeply penetrated into the public sector market, so we didn't lose contracts. we weren't deeply penetrated into the public sector market so we didn't lose contracts Historically, you have to wait until those RFPs come up. historically you have to wait until those rfps come up A lot of those contracts were cut, and they're coming back to market for RFP. a lot of those contracts were cut and they're coming back to market for rfp They cut too much. they cut too much Now we have the opportunity to bid on those RFPs that potentially we wouldn't have had that opportunity for a number of years. It's no longer just go with incumbent. Everyone wants to show that they're executing on synergies, reducing costs, and so forth, and having better technology. We're having really good success there on the public sector team submitting those RFPs. These are a much longer sales cycle, as you can imagine. Some of those are going to be year-plus sales cycles, and then there's ramp-up period as well because it follows the budget. On the federal side, budget renewals are around September. On the SLED, state, local, and education, that's usually around the July timeframe for the state and local budget renewals. The RFPs usually follow that as well. Those contracts can be material, as you can imagine. Now we have the opportunity to bid on those RFPs that potentially we wouldn't have had that opportunity for a number of years. now we have the opportunity to bid on those rfps that potentially we wouldn't have had that opportunity for a number of years It's no longer just go with incumbent. it's no longer just go with incumbent Everyone wants to show that they're executing on synergies, reducing costs, and so forth, and having better technology. everyone wants to show that they're executing on synergies reducing costs and so forth and having better technology We're having really good success there on the public sector team submitting those RFPs. we're having really good success there on the public sector team submitting those rfps These are a much longer sales cycle, as you can imagine. these are a much longer sales cycle as you can imagine Some of those are going to be year-plus sales cycles, and then there's ramp-up period as well because it follows the budget. some of those are going to be year-plus sales cycles and then there's ramp-up period as well because it follows the budget On the federal side, budget renewals are around September. on the federal side budget renewals are around september On the SLED, state, local, and education, that's usually around the July timeframe for the state and local budget renewals. on the sled state local and education that's usually around the july timeframe for the state and local budget renewals The RFPs usually follow that as well. the rfps usually follow that as well Those contracts can be material, as you can imagine. those contracts can be material as you can imagine We've seen contracts, they're going to be the whale contracts, $10 million, $15 million contracts with Reed Elsevier LexisNexis. Even if we get a portion of that, that's going to be material to our revenue. Secondly, we're excited about the background screening space. We won one of the large payroll companies out there for their background screening process. They're in the process of onboarding. When Innovatives was purchased by Aperis and ultimately Equifax, we had a buy versus build decision. Do we continue to just service this industry behind the scenes, or do we go front-facing? We made the effort to build out the products. We already had the data, but how do we productize this and go to market with it? We started productizing it, and this year, we're now ready to go to market in a formal fashion. We've seen contracts, they're going to be the whale contracts, $10 million, $15 million contracts with Reed Elsevier LexisNexis. we've seen contracts they're going to be the whale contracts $10 million $15 million contracts with reed elsevier lexisnexis Even if we get a portion of that, that's going to be material to our revenue. even if we get a portion of that that's going to be material to our revenue Secondly, we're excited about the background screening space. secondly we're excited about the background screening space We won one of the large payroll companies out there for their background screening process. we won one of the large payroll companies out there for their background screening process They're in the process of onboarding. they're in the process of onboarding When Innovatives was purchased by Aperis and ultimately Equifax, we had a buy versus build decision. when innovatives was purchased by aperis and ultimately equifax we had a buy versus build decision Do we continue to just service this industry behind the scenes, or do we go front-facing? do we continue to just service this industry behind the scenes or do we go front-facing We made the effort to build out the products. we made the effort to build out the products We already had the data, but how do we productize this and go to market with it? we already had the data but how do we productize this and go to market with it We started productizing it, and this year, we're now ready to go to market in a formal fashion. we started productizing it and this year we're now ready to go to market in a formal fashion We had a lot of beta testing per se, where we got a lot of feedback from the different background screening players. It'd be really nice to do this if you can do X, Y, and Z. Now we're at the place where the product is fully functioning, should service, I call it, 99% of the use cases out there. Proof in the pudding, we won one of the largest payroll processors. We don't, we typically don't disclose names on the IDI side. We'll disclose names on the Forewarn side. If you look at our press releases, they're mostly all Forewarn related. That's by design because there's going to be real estate agents, hey, our sister association offers this product to their members as a member benefit. Why don't you? It's more of a marketing perspective on the Forewarn side. We had a lot of beta testing per se, where we got a lot of feedback from the different background screening players. we had a lot of beta testing per se where we got a lot of feedback from the different background screening players It'd be really nice to do this if you can do X, Y, and Z. it'd be really nice to do this if you can do x y and z Now we're at the place where the product is fully functioning, should service, I call it, 99% of the use cases out there. now we're at the place where the product is fully functioning should service i call it 99% of the use cases out there Proof in the pudding, we won one of the largest payroll processors. proof in the pudding we won one of the largest payroll processors We don't, we typically don't disclose names on the IDI side. we don't we typically don't disclose names on the idi side We'll disclose names on the Forewarn side. we'll disclose names on the forewarn side If you look at our press releases, they're mostly all Forewarn related. if you look at our press releases they're mostly all forewarn related That's by design because there's going to be real estate agents, hey, our sister association offers this product to their members as a member benefit. that's by design because there's going to be real estate agents hey our sister association offers this product to their members as a member benefit Why don't you? why don't you It's more of a marketing perspective on the Forewarn side. it's more of a marketing perspective on the forewarn side On the IDI side, as you can imagine, most of our customers don't want individuals knowing how they're doing their identity verification or where they're getting their data from. They're very hesitant on allowing us to press release their name. We also don't want the competition knowing where we're winning. From just a couple KPIs, actually on this slide, from a gross retention number, typically we say these businesses run from 90%-95%. Last quarter, we came in at 97% on the gross retention side. We continue to guide towards that 90% to 95%. We don't publicly disclose net revenue retention, but as you can imagine, we mimic those information solutions companies. Mid-teens, high mid-teens to upper, to 120%, from a net revenue retention perspective. On the IDI side, as you can imagine, most of our customers don't want individuals knowing how they're doing their identity verification or where they're getting their data from. on the idi side as you can imagine most of our customers don't want individuals knowing how they're doing their identity verification or where they're getting their data from They're very hesitant on allowing us to press release their name. they're very hesitant on allowing us to press release their name We also don't want the competition knowing where we're winning. we also don't want the competition knowing where we're winning From just a couple KPIs, actually on this slide, from a gross retention number, typically we say these businesses run from 90%- 95%. from just a couple kpis actually on this slide from a gross retention number typically we say these businesses run from 90%- 95% Last quarter, we came in at 97% on the gross retention side. last quarter we came in at 97% on the gross retention side We continue to guide towards that 90% to 95%. we continue to guide towards that 90% to 95% We don't publicly disclose net revenue retention, but as you can imagine, we mimic those information solutions companies. we don't publicly disclose net revenue retention but as you can imagine we mimic those information solutions companies Mid-teens, high mid-teens to upper, to 120%, from a net revenue retention perspective. mid-teens high mid-teens to upper to 120% from a net revenue retention perspective What I understand, the revenue, the retention is just a decline. It's not that revenue. What I understand, the revenue, the retention is just a decline. what i understand the revenue the retention is just a decline It's not that revenue. it's not that revenue Correct. It's just gross revenue retention. Yeah, we're not accounting for any upsells or anything of that nature in this gross revenue retention. Correct. correct It's just gross revenue retention. it's just gross revenue retention Yeah, we're not accounting for any upsells or anything of that nature in this gross revenue retention. yeah we're not accounting for any upsells or anything of that nature in this gross revenue retention It is very interesting. It is very interesting. it is very interesting Correct. Any other questions? Correct. correct Any other questions? any other questions Above the flags, could you go, would that be a bus to work or a car? [guess] Above the flags, could you go, would that be a bus to work or a car? [guess] above the flags could you go would that be a bus to work or a car? [guess] Yeah. There are two clear. There is Clear at the airport, and then Thomson Reuters has a legacy product called Clear. That legacy product is mainly in the public sector space. It is a very clunky, dated product, but they have a huge presence in the public sector. We are starting to bump up against them. Yes, Clear like at the airport. They are running identity verification. They are using facial recognition. They are also scanning your ID and doing PII information behind that, validating that it is a valid ID, and then they are pulling up your picture as well with your facial recognition. That Clear would be a potential customer of us or one of our competitors because they need that PII information. It is a good question. Yeah. yeah There are two clear. there are two clear There is Clear at the airport, and then Thomson Reuters has a legacy product called Clear. there is clear at the airport and then thomson reuters has a legacy product called clear That legacy product is mainly in the public sector space. It is a very clunky, dated product, but they have a huge presence in the public sector. that legacy product is mainly in the public sector space. it is a very clunky dated product but they have a huge presence in the public sector We are starting to bump up against them. we are starting to bump up against them Yes, Clear like at the airport. yes clear like at the airport They are running identity verification. They are using facial recognition. They are also scanning your ID and doing PII information behind that, validating that it is a valid ID, and then they are pulling up your picture as well with your facial recognition. they are running identity verification. they are using facial recognition. they are also scanning your id and doing pii information behind that validating that it is a valid id and then they are pulling up your picture as well with your facial recognition That Clear would be a potential customer of us or one of our competitors because they need that PII information. It is a good question. that clear would be a potential customer of us or one of our competitors because they need that pii information. it is a good question How does artificial intelligence, whether a threat? How does artificial intelligence, whether a threat? how does artificial intelligence whether a threat Yep. A very valid question. I'll answer it a couple of different ways. We get that all the time, right? Because AI is out there. You can aggregate this, the data. ChatGPT is layered over the internet. Bad data in is bad data out, right? As you can imagine, banks can't clear transactions with ChatGPT. They can say, hey, tell me about John Smith. Who is John Smith? Is this the right individual? How are you leveraging AI today in a couple of different fashions? We have a high confidence data cohort. How do we leverage AI on top of it so we can go out and interact, customers can interact with our platform differently? Today, when you go into our online platform, you're entering a name, date of birth, social, something of that nature, and getting information back. What we're working on today is being more interactive. Yep. yep A very valid question. a very valid question I'll answer it a couple of different ways. i'll answer it a couple of different ways We get that all the time, right? we get that all the time right Because AI is out there. because ai is out there You can aggregate this, the data. you can aggregate this the data ChatGPT is layered over the internet. chatgpt is layered over the internet Bad data in is bad data out, right? bad data in is bad data out right As you can imagine, banks can't clear transactions with ChatGPT. as you can imagine banks can't clear transactions with chatgpt They can say, hey, tell me about John Smith. they can say hey tell me about john smith Who is John Smith? who is john smith Is this the right individual? is this the right individual How are you leveraging AI today in a couple of different fashions? how are you leveraging ai today in a couple of different fashions We have a high confidence data cohort. we have a high confidence data cohort How do we leverage AI on top of it so we can go out and interact, customers can interact with our platform differently? how do we leverage ai on top of it so we can go out and interact customers can interact with our platform differently Today, when you go into our online platform, you're entering a name, date of birth, social, something of that nature, and getting information back. today when you go into our online platform you're entering a name date of birth social something of that nature and getting information back What we're working on today is being more interactive. what we're working on today is being more interactive Tell me everything about Camilo Ramirez or something as simple, which sounds very simple, but it would require a number of searches. Is there a family member of Camilo Ramirez that has a violent criminal history, whatever you want to call it? Today what you'd have to do, you'd have to search Camilo Ramirez, go into each known associate, look at their criminal background, and that's going to take you time because depending on how many relatives, you're going to have to go through each individual relative. You can keep going down the family tree, as opposed to when you're interacting with the platform. You ask it that question and it'll either say yes, this family member, and it'll give you the family member name, has a criminal history, and it'll give you all that information. It's doing all those searches in subseconds. That's how we want to interact. Tell me everything about Camilo Ramirez or something as simple, which sounds very simple, but it would require a number of searches. tell me everything about camilo ramirez or something as simple which sounds very simple but it would require a number of searches Is there a family member of Camilo Ramirez that has a violent criminal history, whatever you want to call it? is there a family member of camilo ramirez that has a violent criminal history whatever you want to call it Today what you'd have to do, you'd have to search Camilo Ramirez, go into each known associate, look at their criminal background, and that's going to take you time because depending on how many relatives, you're going to have to go through each individual relative. today what you'd have to do you'd have to search camilo ramirez go into each known associate look at their criminal background and that's going to take you time because depending on how many relatives you're going to have to go through each individual relative You can keep going down the family tree, as opposed to when you're interacting with the platform. you can keep going down the family tree as opposed to when you're interacting with the platform You ask it that question and it'll either say yes, this family member, and it'll give you the family member name, has a criminal history, and it'll give you all that information. you ask it that question and it'll either say yes this family member and it'll give you the family member name has a criminal history and it'll give you all that information It's doing all those searches in subseconds. it's doing all those searches in subseconds That's how we want to interact. that's how we want to interact Even on the, let's say, let's say you're a CVS. You want to understand the upward and downward mobility of the population within this intersection. We have all those data points of that population. Basically, their analyst is going to say, hey, typical crime rate in this corner is X, Y, and Z. Here's the upward mobility of individuals and here's the house prices that continue to go up. They're going to make a decision, do we build a CVS on this corner or not? It's aggregating all that data and giving you insight into a population as opposed to just individuals as well. From an operational standpoint, how we're leveraging AI, today, let's say for every, call it, thousand customers we onboard, we're going to have to add one or two additional credentialing individuals, right? Even on the, let's say, let's say you're a CVS. even on the let's say let's say you're a cvs You want to understand the upward and downward mobility of the population within this intersection. you want to understand the upward and downward mobility of the population within this intersection We have all those data points of that population. we have all those data points of that population Basically, their analyst is going to say, hey, typical crime rate in this corner is X, Y, and Z. basically their analyst is going to say hey typical crime rate in this corner is x y and z Here's the upward mobility of individuals and here's the house prices that continue to go up. here's the upward mobility of individuals and here's the house prices that continue to go up They're going to make a decision, do we build a CVS on this corner or not? they're going to make a decision do we build a cvs on this corner or not It's aggregating all that data and giving you insight into a population as opposed to just individuals as well. it's aggregating all that data and giving you insight into a population as opposed to just individuals as well From an operational standpoint, how we're leveraging AI, today, let's say for every, call it, thousand customers we onboard, we're going to have to add one or two additional credentialing individuals, right? from an operational standpoint how we're leveraging ai today let's say for every call it thousand customers we onboard we're going to have to add one or two additional credentialing individuals right How do we automate our internal processes so we can continue to expand on that margin? As we continue to grow, we don't have to add those one or two individuals for every thousand customers. It can be one individual for every 5,000 customers or something of that nature. How do we automate our processes internally so we can continue to extract margin in the future? From a margin perspective, just to note, at maturities, these businesses on the gross profit line, they're generating 90% plus gross profit margins. On the adjusted EBITDA, call it around 60% adjusted EBITDA, just because of that fixed cost nature of the data asset. How do we automate our internal processes so we can continue to expand on that margin? how do we automate our internal processes so we can continue to expand on that margin As we continue to grow, we don't have to add those one or two individuals for every thousand customers. as we continue to grow we don't have to add those one or two individuals for every thousand customers It can be one individual for every 5,000 customers or something of that nature. it can be one individual for every 5,000 customers or something of that nature How do we automate our processes internally so we can continue to extract margin in the future? how do we automate our processes internally so we can continue to extract margin in the future From a margin perspective, just to note, at maturities, these businesses on the gross profit line, they're generating 90% plus gross profit margins. from a margin perspective just to note at maturities these businesses on the gross profit line they're generating 90% plus gross profit margins On the adjusted EBITDA, call it around 60% adjusted EBITDA, just because of that fixed cost nature of the data asset. on the adjusted ebitda call it around 60% adjusted ebitda just because of that fixed cost nature of the data asset That's how artificial intelligence helps you, though. That's how artificial intelligence helps you, though. that's how artificial intelligence helps you though Yeah, that's a good question, right? There are barriers to entry. As you can imagine, let's say you want to go out and start a business in this nature. You have the credit bureaus. They have the largest balance sheet, and as you can imagine, they have all the data in-house. When they wanted to get into the market, it was a buy versus build decision. Each iteration, they went out and bought the businesses just because of the amount of information that you have to gather and the type of information that's actually beneficial. A good example, early on, we didn't have business data. If me saying that, you're like, why wouldn't they have business data, right? It's very integral to understand who owns businesses, but it's a nice to have. It's a very expensive data set, and there's not much lift. It's low ROI on that business data. Yeah, that's a good question, right? yeah that's a good question right There are barriers to entry. there are barriers to entry As you can imagine, let's say you want to go out and start a business in this nature. as you can imagine let's say you want to go out and start a business in this nature You have the credit bureaus. you have the credit bureaus They have the largest balance sheet, and as you can imagine, they have all the data in-house. they have the largest balance sheet and as you can imagine they have all the data in-house When they wanted to get into the market, it was a buy versus build decision. when they wanted to get into the market it was a buy versus build decision Each iteration, they went out and bought the businesses just because of the amount of information that you have to gather and the type of information that's actually beneficial. each iteration they went out and bought the businesses just because of the amount of information that you have to gather and the type of information that's actually beneficial A good example, early on, we didn't have business data. a good example early on we didn't have business data If me saying that, you're like, why wouldn't they have business data, right? if me saying that you're like why wouldn't they have business data right It's very integral to understand who owns businesses, but it's a nice to have. it's very integral to understand who owns businesses but it's a nice to have It's a very expensive data set, and there's not much lift. it's a very expensive data set and there's not much lift It's low ROI on that business data. it's low roi on that business data Early on, we didn't have business data. We'd go out and when we would go out to prospect customers, we'd say, hey, they would love our product. They would say, hey, but you don't have the business data. When you really dug in, they were doing a couple of searches a month on the business data. Ultimately, once our P&L supported purchasing that business data, we layered in that business data. Today we're starting to work on KYB, so Know Your Business. If we can understand everything about a consumer and individual as their sole data point, we can know everything about a business as well. LLCs, who are the true owners of these LLCs going down multiple businesses. Being able to come out in the future with a KYB product as well. There's the moat and then the know-how of what data to acquire. Early on, we didn't have business data. early on we didn't have business data We'd go out and when we would go out to prospect customers, we'd say, hey, they would love our product. we'd go out and when we would go out to prospect customers we'd say hey they would love our product They would say, hey, but you don't have the business data. they would say hey but you don't have the business data When you really dug in, they were doing a couple of searches a month on the business data. when you really dug in they were doing a couple of searches a month on the business data Ultimately, once our P&L supported purchasing that business data, we layered in that business data. ultimately once our p&l supported purchasing that business data we layered in that business data Today we're starting to work on KYB, so Know Your Business. today we're starting to work on kyb so know your business If we can understand everything about a consumer and individual as their sole data point, we can know everything about a business as well. if we can understand everything about a consumer and individual as their sole data point we can know everything about a business as well LLCs, who are the true owners of these LLCs going down multiple businesses. llcs who are the true owners of these llcs going down multiple businesses Being able to come out in the future with a KYB product as well. being able to come out in the future with a kyb product as well There's the moat and then the know-how of what data to acquire. there's the moat and then the know-how of what data to acquire The credit bureaus have to have high confidence in you keeping that data safe, right? As you can imagine, they're not just going to give the whole U.S. population to anyone out on the street. There's a lot of risk associated with that. We're audited by the credit bureaus on a regular basis. We're PCI Level 1, SOC 1, SOC 2 certified, ISO 2700 as well. Safety is at the forefront of our data safety. I believe you had a question. The credit bureaus have to have high confidence in you keeping that data safe, right? the credit bureaus have to have high confidence in you keeping that data safe right As you can imagine, they're not just going to give the whole U.S. population to anyone out on the street. as you can imagine they're not just going to give the whole u.s population to anyone out on the street There's a lot of risk associated with that. there's a lot of risk associated with that We're audited by the credit bureaus on a regular basis. we're audited by the credit bureaus on a regular basis We're PCI Level 1, SOC 1, SOC 2 certified, ISO 2700 as well. we're pci level 1 soc 1 soc 2 certified iso 2700 as well Safety is at the forefront of our data safety. safety is at the forefront of our data safety I believe you had a question. i believe you had a question How many sort of separate real estate deals do you have? How many not renew or in the end contract period? The second point question is, would Uber be a particular customer possible? How many sort of separate real estate deals do you have? how many sort of separate real estate deals do you have How many not renew or in the end contract period? how many not renew or in the end contract period The second point question is, would Uber be a particular customer possible? the second point question is would uber be a particular customer possible Yeah, that's a potential use case, right? I'll answer that one and go back to your other one. Uber, they're doing background screening on drivers as well. They have that high transactions. They're doing identity verification, make sure that's a valid individual, that valid license and so forth. That's a potential use case. On the real estate side, let me flip to the side of it. Today we service over 575 real estate associations. Again, there's about 1,100 associations in the U.S. From a real estate, from a realtor standpoint, we're servicing just about 350,000 real estate individuals. There's about 1.2 million real estate individuals in the U.S. Of those that are truly in the real estate business, call it about 900,000. Yeah, that's a potential use case, right? yeah that's a potential use case right I'll answer that one and go back to your other one. i'll answer that one and go back to your other one Uber, they're doing background screening on drivers as well. uber they're doing background screening on drivers as well They have that high transactions. they have that high transactions They're doing identity verification, make sure that's a valid individual, that valid license and so forth. they're doing identity verification make sure that's a valid individual that valid license and so forth That's a potential use case. that's a potential use case On the real estate side, let me flip to the side of it. on the real estate side let me flip to the side of it Today we service over 575 real estate associations. today we service over 575 real estate associations Again, there's about 1,100 associations in the U.S. again there's about 1,100 associations in the u.s From a real estate, from a realtor standpoint, we're servicing just about 350,000 real estate individuals. from a real estate from a realtor standpoint we're servicing just about 350,000 real estate individuals There's about 1.2 million real estate individuals in the U.S. there's about 1.2 million real estate individuals in the u.s Of those that are truly in the real estate business, call it about 900,000. of those that are truly in the real estate business call it about 900,000 Real estate individuals are actually transacting more than one home a year, they just those that have a real estate license just for family and friends and so forth. There's no true competing product in that space. For lack of a better term, most of the competing products are going to be like find the body, so panic buttons once you're attacked and so forth. This is more of a proactive safety solution. Any last minute questions? Real estate individuals are actually transacting more than one home a year, they just those that have a real estate license just for family and friends and so forth. real estate individuals are actually transacting more than one home a year they just those that have a real estate license just for family and friends and so forth There's no true competing product in that space. For lack of a better term, most of the competing products are going to be like find the body, so panic buttons once you're attacked and so forth. there's no true competing product in that space. for lack of a better term most of the competing products are going to be like find the body so panic buttons once you're attacked and so forth This is more of a proactive safety solution. this is more of a proactive safety solution Any last minute questions? any last minute questions Sure. Sure. sure No, I appreciate it, guys, for making it interactive. Thank you. No, I appreciate it, guys, for making it interactive. no i appreciate it guys for making it interactive Thank you. thank you