Red Violet at East Coast IDEAS Conference: Strategic Growth and Innovation

On Wednesday, 11 June 2025, Red Violet Inc. (NASDAQ:RDVT) presented at The 15th Annual East Coast IDEAS Conference. The company highlighted its strategic focus on identity verification solutions, emphasizing both growth opportunities and challenges. Red Violet remains committed to leveraging its data aggregation capabilities and technological innovations to maintain a competitive edge in the market.

Key Takeaways

• Red Violet’s gross profit margin is in the low 80% range, with an adjusted EBITDA margin around the low 30% range.
• The company is expanding its public sector revenue, expecting significant contributions by 2026.
• Red Violet competes with major players like LexisNexis and TransUnion, emphasizing data quality over quantity.
• The company is exploring M&A opportunities to acquire unique data assets.
• Red Violet operates in the AWS cloud, providing scalability and efficiency advantages.

Financial Results

Red Violet’s financial performance showcases strong profitability and strategic investments:

• Gross Profit Margin: Currently in the low 80% range, approaching the target for $100 million in revenue.
• Adjusted EBITDA Margin: Around the low 30% range, with a target of 40%.
• Share Repurchase Program: Approximately 1.5 million shares repurchased at an average price of $19 per share.
• Data Costs: 40% of data costs tied to one vendor, with renegotiations aiming for flat renewal rates.

Operational Updates

Red Violet is enhancing its operational capabilities and market reach:

• Sales Strategy: Verticalized sales floor targeting law enforcement, financial institutions, and background screening sectors.
• Public Sector Expansion: Hiring Jonathan McDonald to drive public sector revenue growth, with significant contributions expected by 2026.
• Technology: Leveraging AWS cloud for scalability and throughput advantages over competitors using data rooms.
• Efficiency Initiatives: Automating repeatable tasks to improve margin profiles as the business scales.

Future Outlook

Strategic plans and initiatives for future growth include:

• M&A Opportunities: Actively seeking acquisitions involving unique data assets or end-use cases.
• AI Integration: Using AI to enhance data acquisition and improve user interaction.
• Data Acquisition: Focused on acquiring more data through AI and the purchase of unique data assets.
• International Expansion: No direct international presence; partners to service international customer needs.

Q&A Highlights

Insights from the Q&A session include:

• AI Opportunities: Seen as a tool to enhance data acquisition and user interaction, not a threat.
• Data Security: Emphasized with multiple third-party penetration tests and certifications.
• Pricing Strategy: While competitors could underprice, Red Violet focuses on data quality and throughput advantages.

Readers are encouraged to refer to the full transcript for a detailed understanding of Red Violet’s strategic initiatives and financial performance.

Full transcript - The 15th Annual East Coast IDEAS Conference:

Unidentified speaker: No. No. No. This is fine.

Steven: Is it time? Yeah. It is. Bingo. Okay.

Good afternoon, everybody. I hope you saw on the schedule that, we’ve got a happy hour coming up at the end of the day. Stick around with us, please, for that. Next company we have for you is Red Violet based in, Boca Raton. With us today we’ve got the Senior Vice President of Finance and Investor Relations, Camilo Ramirez.

Neat business, they’re actually a client of ours, leader in the identity verification space. So with that, I’ll turn it over to Camilo.

Unidentified speaker: Thank you, Steven. Appreciate it. Thank you everyone for joining me today. I’ll go through a little bit about management history, what we do, competitive landscape, give you a couple of use cases, competitive advantage, and then I’ll leave time for q and a. My preference is to make it more conversational than anything, so if you have any questions, feel free to ask.

So like Stephen said, all things identity. We say we’re applicable to almost every transaction that occurs in The US. So management has been together for two, three decades. They started in the identity space in the late nineties. They created a company called Accurant, ultimately sold it off to Reed Elsevier’s LexisNexis for about $750,000,000 Non competes expired, got back into the space, created TLO and ultimately sold that off to TransUnion.

The main founding member that was funding it ended up passing away abruptly so it sold it a bit premature. Call it revenue run rate was about $25,000,000 burning about $1 to $2,000,000 a month. Ended up selling it for just under $200,000,000 so in aggregate, call it just under $1,000,000,000 within those two organizations. Again, non competes expired for some of the members. Ultimately they got back together, got in the space.

Technology changed drastically from the late ninety’s, early two thousand’s. AI and machine learning, large language models were not a thing. So call it in around 2014, team got back together, built this iteration in the cloud as opposed to the legacy two products that are in large data rooms. So we’re able to scale up and scale down during peak productivity hours, use the latest and greatest technology as well as opposed to the competition. So what do we do?

So we aggregate the spirit of databases, call it liens, judgments, credit header data, PII information, call it IP addresses, mobile IDs, anything and everything on every adult individual in The US, create that three sixty degree profile on that individual and sell it back out. Data aggregations, they’re contractually obligated long term contracts So our cost of revenue is fixed in nature. So we say today every additional dollar is nearly a 100% contribution line contribution down the P and L. So we we like to say we serve five different verticals made up about of about 26 different industries. I’ll start with one of the easiest ones to understand, collections.

Let’s say you have a debt collector, they bought a million records from Capital One. They need to understand right party contact information, anyone that’s deceased in those records, and have they filed for bankruptcy because they cannot contact them. If not, they get fined, call it around like $10,000 per incident. Secondly, real estate. So we service real estate in two manners.

One through our forewarned product which basically a skin on our IDIQ core platform. Let’s say you’re a real estate agent, you get a call, I want to see this $3,000,000 home, I’m only in town till tomorrow. My name is John Smith, I’m gonna show up in a Mercedes S Class. Basically does a reverse phone search on that phone number. You don’t see John Smith, you don’t see that S Class.

What you do see is this individual on the extreme example, just got out of prison for sexual assault. So you’re not gonna show that home, right? What we noticed is that there was a lot of crime against realtors. Realtors show a certain persona, how they present themselves to the community, and there was a lot of crime being committed against those individuals. So this is a safety solution for them.

So we go after the real estate association so they can offer it as a member solution for the real estate agents. So today we’re in about just over 500 real estate associations. There’s about 1,100 associations in The US. From a realtor count today we serve about three twenty five individual users. From an addressable market, there’s just under a million real estate agents.

There’s more that are actually have real estate license but some of those, as you know, don’t practice or they’re not with the National Association of Realtors. So addressable is, call it, just about a million. The other manner that we service the real estate industry is going be through our IDI core platform. That is essentially we power the prop tech companies. It’s going be more like the more of a marketing solution propensity to sell.

Give me a list of everyone that’s 65 and over that lives in a two story home. And then industry we service is financial and corporate risk. That’s going to be like your large banks KYC background screening. A good example there is let’s say I work at Ford Motor Company, I’m in the accounts payable department, I write a million dollar check to a vendor. Ford would want to know that my brother-in-law is a director at that company, right?

It’s not showing fraud, but it’s a potential red flag if there’s distress or something of that nature. So being able to make those connections across family trees per se. is going to be investigative. That’s going to be our law enforcement, private investigators, those kind of interactions. And then lastly, emerging markets.

So emerging markets for us are verticals where we don’t have a deep penetration. It’s going to be the likes of government, insurance. These have been large revenue contributors in the previous iterations, but today we haven’t penetrated those. Ultimately those will most likely be their own vertical as we state externally. So I’ll go through a couple use cases as well, just some differentiations, set the landscape of the competitor.

So LexisNexis, they have about, call it, around 400,000 customers. On the TransUnion side, they have about 60,000 customers. Those who are our main competitors, outside of that it’s going be pretty fragmented. You have some specific niche players whether you’re getting real estate data, motor vehicle data, or anything of that nature. And then some use cases, I always like to provide use cases as opposed to telling you, hey, we’re better because x, y and z, right?

And I can stand up here and speak all day about that. But so I’ll start one with background screening. So there was a custom we won early on from the competition, Innovative. Innovative, early on they were doing about 5 figures a month. Ultimately, they grew well into 6 figures.

They were purchased by Apris and Apris was ultimately purchased by Equifax. As you can imagine, Equifax has all the data in the world, way more data than we have. The innovative contract was up for renewal at that point in time. They came to us and said, Hey, can we get a three month extension? We said, Of course, we’re here.

If you need anything, we understand you’re trying to execute on synergies. They asked for a couple more extensions. Ultimately they came back and said we can’t create the same lift on the data. So they signed a multi year long term agreement with us. Even though they have the individual data aspects in house, they just can’t reproduce the same lift, the data connections on that data asset.

Another great use case I like to use is on our cloud infrastructure, right, so throughput. That’s a big differentiator from the competition. Early on, call it around 2018, 2019, one of our customers was going through a funding round. They received funding through TransUnion. The CEO of TransUnion called them and said, hey, we’re not gonna close this deal unless you move your usage to us and move IDI data.

They gave us a call, told us the situation. We told them we completely understand. We think you’re gonna have a degradation throughput and data quality, but we’re here if you need us. They moved off for a couple months. Ultimately, they gave us a call and said, hey, we’re seeing fallout.

We don’t see the same throughput. We’re sending calls through, we’re not getting any responses back, customers are starting to complain, they want to understand what’s going on. TransUnion is saying you built us a custom API, we reminded them we didn’t build you a custom API, we were up and running within forty eight hours. And ultimately that customer came back on board and they’re still one of our largest customers and never left our platform even with the TransUnion investment. We like to say that we power seven of the top 10 identity players without naming any customers, the likes of Prove, Jumeo, Ecada, ID.

Me. So these are all orchestration platforms. They don’t own any of the data, but they have the front facing solution. So they have a customer making a call, they’ll call out to us or our competitors and clear that identity verification. They have their unique way of clearing that identity verification.

Some of them are going to do document verification, load a picture of your ID, take a selfie, make that match and call for that PII information behind the scenes. Or it’s going be like mobile authentication. Let’s say you’re logging into Bank of America, Wells Fargo, Blue Cross Blue Shield with your iPhone using facial recognition. What it’s doing behind the scenes, it’s saying, Hey, this mobile ID belongs to Camilo, here’s the PII associated with Camilo, does it match the bank’s side? Yes, grant access.

So mobile authentication. ID. Me is big in the government space, so let’s say you’re applying for government benefits, unemployment, anything of that nature, they need to do an identity verification for the government before they start sending out benefits. And then with that, we’d like to say we’re a very barbelled approach to the economy with the ever evolving economy, right? We see currently we’re seeing starting to see the innings of degradation on The US consumer at the lower end of the spectrum.

Top end of the spectrum, they’re still out there spending. So when an economy is booming, you have new account openings, new applications, car loans and so All that requires an identity verification. So we’ll power that. And on the other side of the spectrum, you have individuals defaulting on their credit cards. An early indicator for us is in the repossession market, a little canary in the coal mine.

Once you miss that payment, you’re most likely going get repoed. So we service the repo market in a very small fashion, but we’ve seen really good acceleration in that vertical. Individuals are just missing payments, they can’t afford those thousand dollar COVID payments anymore. So we’re starting to see that starting to flow through and then that will lead us into the collection cycle as well. The current administration gave the green light to start collecting on student loan debt so it’s only a matter of time before we start seeing that flow through.

We did a really good job of acquiring new logos on the collection side so when that collection does commence we should see that lift as well. I’ll pause there, see if there’s any questions and if not, keep going. Yep. Very good question. So some of it’s industry and vertical specific.

How we go out and win business. We have a sales vertical. Our sales floor is verticalized so we have an investigative vertical that’s going to go after law enforcement specifically or PI customers. We have a financial and corporate risk, they’re going after that KYC application, financial institutions, background screening team. But specifically to Steven’s questions on, let’s say, the collection side.

So that’s more of a waterfall effect. How we’ll enter the space there is they get that million records that we spoke about, they’ll send it to the tier one provider, their main customer economies of scale, let’s call it TransUnion. Whatever fell out, call it 20%, didn’t get matched, they’ll send it to tier provider and then we’ll come in and say, hey, let us scrub at the tier. It’s already been scrubbed twice. We know our data quality is there and we’ll get a high hit rate on that data that’s already been scrubbed and then we’ll start moving up the waterfall from that perspective so you can get economies of scale because they want to match, have the highest hit rate on that instance.

And then today, so today we’re starting to move to the sole provider as well, especially on these larger relationships where they require an API connection, some type of custom solution. What we’re seeing is they’re going out to the end user. The end user has a specific scenario. They go to one of these top 10 identity providers, say, have this specific issue, can you guys solution it for us? That company will come to us, say, hey, my end user is asking for this, can you guys do it?

And we’ll work directly with them to implement that solution and work around it. But what we’re seeing now is that they’re realizing that we’re behind the scenes and they’re coming directly to us. So one, we’re able to get the lift from wholesale to retail rates, we can go to the end user as well and then replicate that scenario across the board. I’ll give you a little color on do you have a question? Yeah, so generally speaking in the identity verification process, we’re in the automated process, right?

That’s where you get the most margin. You’re doing that identity verification on that PII. So again, to collections just because it’s easy to understand for everyone, there’s an automated and a manual process there. On the automated side, you’re trying to garnish wages, right? So you need to understand who that individual is, where that individual works, and you have to have a live verification.

So someone needs to call the employer and says, Does Camilo work here? Yes. And then you can start garnishing wages and send judgment. So we outsource all that manual work. We don’t want to bring that in house.

It’s just a one stop shop for the collection space, more of a convenience for them, but we don’t focus on it at all. So we stay in the automated space so we can reap the benefits of of the automation and ultimately the margin profile that comes with

Steven: it. Yep.

Unidentified speaker: Correct. Yeah. If we’re powering ID. Me, yes. Without naming any customers.

Maybe I’ll give you a little color on data costs to understand the margin profile. Alright, so all of our contracts are long term in nature. If you look at our 10 ks, you’ll see that 40% of our data costs is associated to one vendor. That is not just one data asset, it’s multiple data assets. It’s a relationship we’ve had with this provider for through the multiple iterations.

That contract is up for renewal call at twelve to eighteen months. We’ve started renegotiations early on this year and we’re in the late stages so we’re really excited about where that’s landing. Ideally, we’ll be able to report on that here come once we report earnings. But last time we renewed with them, call it six years ago, we renewed flat. We anticipate the same.

And then I’ll just run down the p and l, give you a couple margin profiles. So at the gross profit line, you can right now, we’re about low 80%. A couple years ago, we gave some color on what what this company looks like at $100,000,000 At that time, we said at $100,000,000 gross profit is going to be around that 80%. We’ve already approached that margin profile at a much lower run rate. On the adjusted EBITDA line, about 40%.

Right now, call it we’re around low thirties. If you look at if you run down the P and L, sales and marketing, most of the variability there is going to be headcount. As we expand into a specific vertical, we’ll lean into it. We see success there, we’ll continue to invest in it. And then on the G and A side, we like to contract long term on all our vendor costs, whether it’s through general G and A just so we can control those costs.

Again, most of that variability is going to be through our data scientists essentially. As we, I’d say about four years ago, yeah, about four years ago we had an investment year on the data science side, building out the products that we’re going to market with today. Last year, we had an investment year in personnel again, more towards our sales individuals. So we brought on Jonathan McDonald to run our public sector. He was responsible for ramping up the government revenue at TransUnion from zero to where it’s at today.

So we’re really excited about what he’s brought to the table. His team in totality, call it around fifteen, twenty individuals, both on the federal side and state and local side. So we break out public sector, Fed versus SLED. So SLED, that’s going be state local education, that’s where law enforcement agencies land. Call it today we have 1,000, 2,000 law enforcement agencies.

Total scope there is going to be about 15,000 agencies in The US and we’ve seen really good traction on the law enforcement, one through our data quality and then some of the unique interfaces that we have. So one unique interface is going to be you have a witness, there was a hit and run, this individual was driving a red F-one50, partial license plate AB. So basically you can geofence a location, give give me a mile radius, two mile radius, half mile radius, call it what you want, enter that red F150, the partial plate, and it’ll drop pins for every individual registration that matches that and their location so you can continue to do your investigation. So we’ve seen good success there in our mobile application as well. Let’s say there’s a law enforcement agency, they’re arriving at a home, they understand the individual in that home, they see three other vehicles.

They don’t know what type of individuals these are, are they violent individuals, so through the mobile app they can quickly look up those license plate numbers, get a good picture of who’s driving those vehicles, who’s potentially in that home. Previously, had to call in back to home base, say, hey, can you run these plates for me? Let me know. Background. So it’s all at their fingertips, so we’ve seen good success there.

On the federal side, when Jonathan came on board, he stated most of my revenue contribution is going to be in the beginning of my revenue contribution is going to commence 2026. We’ve seen some early wins, which gets us very excited. It’s a much longer sales cycle, as you can imagine. Budgets renew every September on the federal side. On the state side, some of those renew around that July timeframe.

And I’m sure the next question is going to be like, how about the current environment, current administration with Doge? So I’ll say we posed that question to Jonathan McDonald and it’s a net positive for us. Essentially, as we all saw with Elon Musk and Twitter, he went slashed and burned, cut everything back, realized, hey, I cut back too much, started bringing back certain items. And that’s what we’re seeing in the government sector as well. They cut these contracts, they weren’t up for RFP for another two, three years, we wouldn’t have an opportunity until then to submit a bid on these, but we’re going start seeing them come back because they cut back too much and now we have that opportunity to bid on it much sooner.

And it’s no longer let’s just renew with the incumbent. They want to show efficiencies, price improvement and so So before we even start testing, there’s already scales tipping to our side, right? Once we start testing, then we can show the data accuracy and then economies of scale for them. We can get them better pricing as well. Any questions?

No, very good question. And that coincides with AI, right? So the question is, now with AI, why can’t anyone just do that? You can use ChatGPT, do a name search and you get some results back. So but as we all saw, there’s hallucinations.

It’s layered over the internet, right? So bad data in is bad data out. The institutions that are using our platform, they need to have a high confidence data asset. You can’t have Bank of America clearing bank accounts for KYC using ChatGPT. So we don’t see that as a threat.

But what we do see from the AI perspective is areas of opportunity. Now I’ll go back to answer your question. Areas of opportunity essentially on being able to acquire more data, right? So using AI to read long form data. So there’s a Ricoh complaint that had five named associates.

We look up those individuals within our system, we see three of those individuals linked together. Now we can have AI go read that long form data, simulate and assume those three additional named associates and tag those individuals together. Even obituary data, being able to understand mother, father, cousins, father-in-law and stepdad and so being able to consume that data and build that family tree per se as well, making those linkages. And then even how you interact with our system, making it more of a interactive as opposed to hey, enter name, date of birth, social. It’s tell me everything you know about Camila Ramirez that lives in South Florida and you can interact with it.

Even as far as, hey, does Camila Ramirez, is he related to anyone that has a violent background? Today, you to answer that question, you have to go in and search Camila Ramirez, click on every associate named, look at their criminal records and make that decision based off that search as opposed to with AI. You ask it, it’s doing all those searches automated and it’s going to say yes, he’s related to an individual or no, he’s not related to it and it’ll give you that individual’s name. So being able to do those investigations in a much more efficient manner. From barriers to entry, I would say to get these up and running, even just from a dollar perspective, call it around $50,000,000 to get the data asset and data contracts up and running.

And your P and L is going to be upside down for a number of years. As you can probably tell if you go back early into our history, that P and L was not very attractive then. And then that’s essentially the barrier. Secondly, you have to go to these credit bureaus and convince them, Hey, give me the whole U. S.

Population and give them confidence that you’re not going to misuse that data or it’s not going to get leaked out. And understanding what data assets to buy. As stated earlier, Equifax has all the data in the world. They have way more data than us. So it’s not about quality, it’s about quantity.

It’s not about quantity, it’s about quality. So essentially being able to decide, hey, these are the data attributes I need and how you simulate those. Any other

Steven: questions? So do you compete with Equifax?

Unidentified speaker: No, so Equifax, we do compete with them. They don’t have a data aggregation layer, they have the individual data assets. They purchase the innovative technology. So we compete with them in the background screening space specifically. We get data from two of the top three bureaus.

You can assume one of the bureaus we don’t get data from based off our competitive history and who we sold to. So, yeah, so we do consume data from the credit bureaus and we sell it right back to them as well. Yep. So generally speaking, to look at our cash earn rate, right, so easy way to look at it, you take adjusted EBITDA, add back any internally developed software and you’ll get a cash earn rate. But that flow through is going to be very nice.

So last year, I think on the flow through, we had an investment year down to adjusted EBITDA, was about 60% flow through and that to cash conversion, high cash conversion rate. It’s going to continue to expand, right, because we’re not going to be able to spend it quick enough from that perspective without being reckless. So then it gives us it takes us back to use of capital, how we’re going to use that money, what are we going to invest in going forward. We do have a share repurchase program in place. To date, we’ve repurchased about 1,500,000.0 shares, one through our share repurchase program.

we bought about 200,000 shares from the Greater Miami Jewish Federation. They received their shares through a donation through one of the initial financial backers. He’s pledged all his wealth, so he gave all his Red Violet shares to Miami Jewish Federation. They no longer own any shares. And then lastly, through when there’s RSUs vesting for employees, instead of selling the shares for tax purposes into the open market, the company will buy them back and retire them.

So in totality, it’s about 1.5. On the share buyback program, we’ve repurchased at an average price of $19 Additional uses of capital is going to be M and A. Couple items that we look for is unique data assets. Is there a data provider out there that has a unique data asset that we can purchase and then cut out some data costs so further improving our margin profile or it’s going to be a buy versus build specifically on the end use case. It’s going to be so a good example there was the background screening space.

We saw that Equifax was entering the space with the innovative purchase so we decided, hey, of purchasing we’re going to go out and build our own products so we have a directly competitive product in the background screening space. And a couple initiatives that we have here this year is acquiring more data. One through the AI aspect that we spoke about earlier, being able to read that obituary data or is there a unique data asset out there that we can go out and purchase. So we get items presented to us all the time and we’ll run through those, see if it makes sense and potentially carve it out and be able to own that asset. And then additionally, another initiative is how can we make our internal operations more efficient as we continue to scale the business.

We don’t want to have to add additional credentialing members, additional sales support members for every thousand customers, right? So how do we make those repeatable tasks automated? So we’re looking at certain tools, building them out either internally or using party to be able to automate some of these repeatable tasks. So as we continue to scale, it’s going to improve that margin profile even further. Correct.

So they’re the only two providers out there that have that three sixty view aggregation profile. There’s other providers that are niche players. You can go get real estate data, driver license information, or you’re gonna be competing against one of our customers. They’re just reselling our data.

Steven: Yep. Yeah.

Unidentified speaker: So it’s a yeah. So it’s a combination. A couple of the use cases went through it. So it’s it’s data quality. We have a lot less it’s what you do with that data, how you assimilate it and how you aggregate it.

The good background screening, right? So the background screening example, Equifax has significantly more data than us and they still couldn’t reproduce the same lift on those individuals so they contracted to consume our data even though they have the individual data points. They just aren’t able to simulate it and gather those learnings from that data. Can you talk about

Steven: the team that built your data engine and their history?

Unidentified speaker: Yeah. So the executive team’s been together, call it, for two and a half decades. They initially, in the late 90s, they built out the Akron product. Times were very different. That was a very static connection.

You had Derek Dubner, our CEO. Ole Poulsen was one of the founding members as well. He was the data scientist behind the technology. Hank Asher at that point in time was one of the founding members. He was like the identity guru per se that created what was coined data fusion, right?

Fusing all that data together. Ultimately, that was sold off to, like we said before, LexisNexis. They all got back together at TransUnion. Dan McLaughlin, our CFO, joined the group at that point in time. James Riley, our president, he drove revenue from zero to the $25,000,000 run rate at TransUnion.

He’s now our president here as well. And then Olli Polson also advised us on and worked on the code that built this iteration and he trained up our CTO Angus McNab on that technology, how to drive it, and then improved on it ultimately to where we’re at today. Our CIO, Jeff Dell, he’s been through the three iterations as well. and foremost, his main objective is security at the end of the day. So we have multiple party penetration tests occurring every year.

We get audited by the credit bureaus or PCI level one, ISO 2,700 certified and so So security is at the forefront every day. Data cost is related to one vendor for multiple data assets. They don’t break out that information at that granular level. They have a lot of data in house so I would imagine no. The answer to that would be no because that data is in house for them.

So we’d be purchasing data from the likes of LexisNexis and TransUnion and so

Steven: So they underprice you?

Unidentified speaker: They could underprice us. We haven’t seen that historically. Every year, TransUnion, come January, they hike up prices on their customers. So we get nice inbound calls from from those price hikes. And on the Lexus Nexus side, I think their egos are too big to compete on price.

They’ve been at it for a number of decades now. They’re an 800 pound gorilla. They have a big foothold in in the government space, public sector space specifically. But, no, in theory they could, but again, we fall back on data quality and then throughput on the technology. So we’re, again, we’re in the AWS cloud and as opposed to these platforms, they’re built in data rooms.

Good question. Yeah, So we’re multi sourced on every data point. So let’s say our main provider said, Hey, we’re going to escalate you 50%. We wouldn’t accept it. We’ll bring up our tier two provider, bring it into tier one and then would return all the data points.

But all the learnings that we acquired, proprietary learnings, that stays with us because that wasn’t their data. We had all the learnings already so we can keep that. We can’t say it would never happen but it’s highly unlikely. Because it’s all margin for them. They have this huge data asset just sitting on the shelf.

It’s not like they can sell the whole U. S. Population to the next company. There’s There’s not a great deal of companies out there that can consume that data and give them confidence that’s going to be safe and secure. Got two minutes left.

So when we speak about emerging markets, we’re speaking specifically about the verticals that we serve where we have a small footprint today. So 100% of our revenue is US based. If we’re gonna go outside of The US, we will partner with someone to service that customer. It’s usually a customer that wants to interact with us and they just want a one stop shop, So we’ll partner with someone to service any of their international needs. Any other questions?

If not, I’ll give you guys a minute back. Thank you everyone, appreciate it.

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