Hello, and welcome to the Better Home and Finance Holding Company first quarter 2025 results call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, please press star 1 on your telephone keypad. I would now like to turn the conference over to Tarek Afifi, Corporate Finance. You may begin.

Welcome to Better Homeland Finance Holding Company's First Quarter Earnings Conference My name is Tariq Afifi, Corporate Finance at Better. Joining me on today's call are Vishal Gard, Founder and Chief Executive Officer of Better, and Kevin Ryan, Chief Financial Officer of Better. In addition to this conference call, please direct your attention to our First Quarter Earnings Release, which is available on our Investor Relations website. Also available on our website is an investor presentation. Certain statements we make today may constitute forward-looking statements within the meaning of federal securities laws that are based on current expectations and assumptions. These expectations and assumptions are subject to risks, uncertainties, and other factors as discussed further in our SEC filings that could cause our actual results to differ materially from our historical results. We assume no responsibility to update forward-looking statements other than as required by law. During today's discussion, management will discuss certain non-GAAP financial measures, which we believe are relevant in assessing the company's financial performance. These non-GAAP financial measures should not be considered replacements for, and should be read together with, our GAAP results. These non-GAAP financial measures are reconciled to GAAP financial measures in today's earnings release and investor presentation, both of which are available on the Investor Relations section of BETTER's website, and when filed in our quarterly report on Form 10-Q filed with the SEC. Amounts described as of and for the quarter ended March 31, 2025 represent a preliminary estimate as of the date of this earnings release and may be revised upon filing our quarterly report on the Form 10-Q with the SEC. More information as of and for the quarter ended March 31st, 2025 will be upon filing our quarterly report on Form 10-Q at the ICC. I will now turn the call over to Vishal.

Thank you and welcome to our first quarter 2025 earnings call. We appreciate everyone joining us today and for your continued support as we advance our mission to make homeownership better, faster, and easier for our customers by building a technology platform that revolutionizes the homeownership experience. I want to set the tone for today's discussion by reiterating that while the mortgage industry and housing markets are facing challenges, this dynamic creates tremendous greenfield opportunity for us because we are truly the first scaled-up AI platform built to empower consumers and now also empowering local mortgage brokers and banks with the technology to serve their customers. The mortgage industry is massive, estimated by the MBA to be $2.1 trillion in total origination volume for the full year 2025, of which approximately $1.4 trillion is purchased and approximately $700 billion is refunded. So even just a 1% share of this massive TAM would result in $14 billion of volume for better, approximately 3x from where we are today. We continue to drive progress towards our mission in which every customer can seamlessly buy, sell, refinance, insure, and improve their home digitally, online, instantly, and towards executing on our key objectives, which are, one, to lean into growth and AI to drive increased volume and revenue. Two, ongoing efficiency improvements driven by continuous advancement in our technology and the implementation of AI through our entire operating model. And three, diversification of our distribution channels and corporate cost reductions. In the first quarter of 2025, on a year-over-year basis, we grew funded loan volume by 31% to $868 million and revenue by 46% to $33 million, driven by funding more loans both through our DTC and Tin Man AI platform channels. Last month, we were very pleased to announce the retirement of betters outstanding convertible debt and right-size the liability structure. This transaction is expected to create approximately $200 million of positive pre-tax equity value and create a path to long-term value creation for our equity shareholders removing this debt overhang is a monumental achievement and key milestone to our capital structure and kevin will talk to this in more detail in the meantime we remain focused on driving towards profitability in the midterm by continuing to lean into tinman technology and ai with the betsy ai loan assistant executing 127,000 consumer interactions in March, our AI underwriting growing from over 40% of LOC loans to 75% in the near future, and increasing loan officer productivity in terms of loans per month to over 3x the mortgage industry median. As we look forward to the second half 2025 and beyond, our strategic priorities remain focused on what lies in our control. Our first priority is to continued to thoughtfully propel growth. In the first quarter, year-over-year funded loan volume growth was driven by increases across all three of our main product categories, with home equity products and refinance loans being the largest growth drivers. Specifically, HELOC and home equity loan volume increased 207%, refinance loan volume increased 64%, and purchase loan volume increased 9%. This growth is attributable to the strategic investments we've made in technology, product innovation and distribution expansion, including the launch of Betsy voice-based AI loan officer, deployment of our Tin Man AI platform strategy with the addition of NIO powered by better, and efficient expansion of D2C. These strategic initiatives have positioned us to capitalize on market opportunities, enhance operational efficiency, and drive sustainable growth. Our second priority is to continue to reduce expenses and improve operational efficiency with the goal of reaching profitability in the medium term. While we expect loan origination expenses will increase as we lean into growth, as we further implement Betsy into the sales, processing, and underwriting workflows, we expect continued operating leverage with revenue growth outpacing expense growth. Using our TinMan AI platform, we have been able to automate time and labor-intensive components of the mortgage process and reduce our cost to originate by over 40% of the industry average. We believe our continued investments in AI with our product and engineering roadmaps well on track will significantly drive down costs further, resulting in improved operating efficiency and superior customer experience. Lastly, our third priority is to continue diversifying our product and platform distribution channels. We now have three ways of serving the customer using our technology.

Direct-to-consumer, TinMan AI as a platform, and TinMan AI as a software.

In our D2C business, we serve the consumer directly on better.com. Better was founded on revolutionizing the consumer experience for the home finance process. And as such, our D2C business has always been at the forefront of pushing the envelope on what technology can do in the mortgage industry at its core. Within the D2C channel, contribution margin or per loan profitability is increasing as the operating cost of fund is decreasing due to implementation of AI in both the sales and operations workflows. Next, we serve the consumer through our Tin Man AI platform, powering loan officers across the United States locally, for which we are seeing rapid early growth. For context, over $1.2 trillion of mortgage volume in 2024 was originated by retail loan officers on antiquated technology and high operating costs. We are quickly disrupting traditional retail mortgage origination by onboarding loan officers and branches onto our Tin Man AI platform and powering them to do more loans than they've ever done before, removing friction from their fulfillment process and expanding their capacity to help more customers. These loan officers keep the pricing they've been able to get historically based on the service level that they provide locally and within their communities and networks, all while compressing a staggering 80% of their back office costs using our platform. As we've discussed on recent earnings calls, Neo Powered by Better, our first and now proven traditional retail mortgage originator leveraging the TinMan AI platform is deeply benefiting from our AI technology and digital lead funnel, supercharging their loan officer teams who have demonstrated track records in customer service excellence within the communities they serve. Further, Betsy, the first AI voice-based loan assistant for the U.S. mortgage industry, is being individually branded for each loan officer at NEO and rolled out through their entire Salesforce. We are making great early progress with NEO Powered by Better, well ahead of our internal expectations and have high aspirations for the road ahead. Since beginning production in January 2025, we have onboarded approximately 115 NEO loan officers across 53 branches. Currently, NEO loan officers are doing three loans a month, and we have the goal of tripling plus their capacity to 10 loans a month, thereby also increasing their earnings 3x and helping them serve even more families than they do currently. In January, we funded $2 million of loans for four families. In February, we funded $42 million for 104 families. And in March, we funded $119 million for 258 families. And this is during the slow season in mortgage origination. This is the first successful launch of taking an entire mortgage company off of their traditional mortgage industry software stack within Compass and the other antiquated mortgage technology that NIO had been working with before, and within 90 days, getting them to exceed the loan volume they previously had while dramatically increasing their efficiency. And as we have proven this out with NIO, with the entire mortgage industry watching, we have been inundated with other mortgage teams and companies wanting to move their business to the Tin Man AI platform. We see massive opportunity in the road ahead to replicate the success of NIO Powered by Better with other traditional mortgage originators. And lastly, we are serving the customer by powering banks that seek to license our TinMan AI software to become more efficient and customer-centric. We have built a highly fine-tuned platform for our own business and customers, and now there is demand from others in the industry to license our software. This quarter, we are excited to sign an agreement with a bank partner to power their entire mortgage platform from a software perspective, from click to close, with their sales and operations people across the full range of products that they offer, including non-QM and other niche products, entirely on 10 minutes. As you all know, banks have traditionally had to offer mortgages, but the cost to originate these loans to their customer base has been well over $10,000 per funded loan, making bank origination of mortgages largely unprofitable. To be clear, banks want to originate mortgages, but they know they need to invest in technology to make it a profitable business in any environment. That is a huge opportunity for Better and Tin Man. Notably, this will be the first implementation of TinMan as a direct competitor to the point-of-sale system, plus CRM system, plus pricing engine, plus document engine, plus loan origination software, plus underwriting calculation engine setup that the vast majority of the mortgage industry has. Seven to eight systems, all by different vendors, with different pricing, with different middleware integrations, mostly not talking to each other, with stale data, with the ability to have only one person logged in at a time, creating the massive workload and massive cost that it takes to make a mortgage the traditional way. We look forward to sharing more information about disrupting this entire software stack in the coming quarters ahead, as we believe a very large addressable market exists within the mortgage ecosystem for a holistic one-stop software solution powered by the industry's leading AI engine, TinMan. To put the opportunity into context, over 5 million mortgages were built on the Encompass platform in 2024. To the extent that we can achieve even 1% penetration of the Encompass customer base, we believe based on our current pricing, that could drive an incremental 50,000 new loans and $75 million of revenue to better per year. And unlike other traditional mortgage software, our SaaS platform does not charge on a per seat or a per application basis. Rather, we are uniquely charging on a per funded loan basis where the revenue event for the mortgage company is directly tied to the technology cost, which is a fundamentally disruptive model to the traditional software players in the industry and enables the full adoption of AI because unlike those other players, we are paid on a per-successful transaction basis, not by filling seats or filling the application funnel. To sum it all up, while our D2C business has always been at the forefront of pushing the envelope of what technology can do in the mortgage industry at its core, we have started making great advancements in diversifying our product and platform distribution channels, notably through the TinMan AI platform, both empowering local loan officers and mortgage brokers and empowering banks with our software. Looking ahead to the second half of 2025 and beyond, the opportunity ahead of us has never been more exciting. We remain focused on enhancing our go-to-market with growth being our North Star alongside continued expense management and channel diversification. We will continue to invest in building the leading AI platform in the mortgage industry, TinMan, to improve the customer experience and further drive down labor costs, making our platform more efficient and scalable, ultimately driving the business to profitability. Furthermore, we are substantially broadening the use of TinMan through diversification on both TinMan AI as a platform for other mortgage originators and TinMan AI as a software service to solve for the mortgage industry's broken tech stack. With that, let me now turn it over to Kevin Ryan, our Chief Financial Officer, who will discuss the quarterly performance and our financial strategy. Kevin?

Thank you, Vishal. As we've discussed on prior calls, even through a continued challenging market environment and now heightened macro volatility, we continue to make great progress towards our goals of increased volume and revenue balanced with ongoing expense management and improved efficiency. In the first quarter of 2025, on a year-over-year basis, we grew funded loan volume by 31% to $868 million and revenue by 46% to $33 million, driven by funding more loans both through our D2C channel and TinMan AI platform. We had an adjusted EBITDA loss of $40.4 million and total gap net loss of approximately $50.6 million. By channel, First quarter funded loan volume was 71% generated through direct-to-consumer and 29% generated through TinMan AI platform along with Beat the Bee. By product, funded loan volume was 67% purchase, 18% second lien or home equity, and 15% refinance. On a sequential quarter-over-quarter basis versus Q4 2024, Q1 funded loan volume was down approximately 7%, as Q1 is always seasonally the slowest quarter in the DTC business, and this compared quite favorably to our prior guidance down 10 to 15%. We are pleased that despite the sequential quarter-over-quarter decline in volume, revenue is up approximately 30%. Revenue grew in the quarter despite the expected decline in volume due to volume from NEO coming on board with higher gain on sale margins are continued push towards increased pricing and a tailwind from the loan loss reserves turning to expenses during the quarter when excluding one-time costs related to cleanup items from the SPAC transaction total expenses decreased approximately 11 in q1 compared with q4 of 2024 and we reduced the adjusted EBITDA loss on a month-over-month month basis during the quarter. Loan origination expenses were down in Q1 on a sequential basis versus Q424. While these loan volume-related expenses will increase as we further lean into growth, operating leverage will rise as revenue growth outpaces expense growth. Turning to our balance sheet and capital structure. Last month, we announced the retirement of approximately $530 million of convertible notes, creating approximately $200 million of positive pre-tax equity value to continue expanding our AI mortgage platform. We are very pleased to reduce the debt overhang and improve our balance sheet positioning and strategic optionality. With the completion of the debt restructuring, our priorities squarely remain growth and profitability. We continue building out our TinMan AI platform and TinMan software channels, lean into productivity-driven savings through AI deployment across the mortgage business, and drive costs down further in our corporate functions. We are excited about using AI to drive the business towards growth and profitability, similar to the advances we experienced in 2016 to 2021, when we grew originations by over 100 times. Turning now to our outlook, we remain focused on managing towards profitability in the midterm, and we expect to drive growth through efficiency from Tin Man AI, distribution channel diversification, and optimized marketing while balancing these growth expenses with further corporate cost reductions. For the second quarter of 2025, we expect funded loan volume to be up compared to the first quarter of 2025, driven by efficiencies in our TinMan AI platform. We are particularly excited that the TinMan AI platform loan volume is pacing well ahead of our internal plan in March and April, despite the heightened macro volatility, and we expect over $450 million of NEO originations in Q2, which is growth of over 250% versus Q1. Additionally, for the second quarter, we expect core expenses, including compensation and benefits, to be down relative to the first quarter. For the full year of 2025, we expect funded loan volume growth to increase year-over-year, driven by tailwinds from the growth initiatives, including Neo Powered by Better, offset by continued macro pressure and the loss of the ally business, a roughly $1 billion headwind. We expect growth to come particularly in the second and third quarter of the year, at which point we expect Neo Powered by Better to be more fully ramped and to benefit from improved seasonal tailwinds. We also expect further improvements to our adjusted EBITDA losses in 2025 as compared to 2024 due to a combination of efficiency gains and continued corporate cost reductions. Lastly, we continue to undergo efforts to exit our non-core UK assets. We'll focus on growing Birmingham Bank. We expect to more than double UK bank originations again in 2025 as we deploy AI with the goal of building the leading AI-driven specialist mortgage bank in the United Kingdom. We expect the exiting of three smaller non-core UK businesses to start being a benefit to our adjusted EBITDA losses in the second half of 2025 as a result of their disposition. With that, I'll now turn it back to the operator for Q&A.

Thank you. If you would like to ask a question, please press star 1 on your telephone keypad. If you would like to withdraw your question, simply press star one again. Please ensure that your phone is not on mute when called upon. Thank you. Your first question comes from Kartik Mehta with North Coast Research. Your line is open.

Good morning, Vishal and Kevin. Vishal, you talked about the Neo platform and obviously how much success you're having with it. As you've looked at early stages, I know you talked about have 90 days, but what do you think is a fair number of time before the loan officer really can feel the impact of that model, and how do you expect that to trend over the next 12 months?

I think they start to see the impact within 30 days, and that starts with taking out a huge chunk of the sales related tasks that the loan officer has to do other than speaking to the consumer. So they immediately start getting back hours of their day that they were spending either putting data into the system, getting data out of the system, following up from the system, following up with processors, underwriters on where loan files are at, where customer files are at, All of that is done automatically by the system. So they immediately start getting time back. Then from there, they start getting productivity back because the customers that they've locked, they're not having to chase them up for the documents. The engine is doing it directly. If there's some problem with the document, the engine is doing it directly for them. And then they encounter the AI underwriter, where if a loan file needs to get restructured, and I'm really excited about the AI underwriter because it captures the logic across all 35 of our investors' guidelines, right? We're talking almost like 40,000 pages of guidelines and pricing that's updated three times a day. It's capturing all of that. And it basically gives like the loan officer the means of addressing the customer's questions. Hey, how do I get a lower rate? Hey, how can I qualify for a bigger mortgage? Hey, how like, you know, what do I need to do to get this loan approved? If it's, you know, this new thing came up or, you know, I want to also buy a car or, you know, my dad doesn't want to co-sign anymore, but my mom does. All of that, it just totally does it instantly. Something that would have taken a human underwriter three to ten hours to resolve, it's getting back answers in three to five seconds. So people are seeing it immediately. And that's why we're seeing the traffic come in from these other loan officers. We already have, you know, on top of the NIO $2.5 billion we're talking about, we already have inbound on another $50 billion of loans. loan officers who are funding loans today who are excited and interested in the platform. Now, all $50 billion is not going to pan out. People are going to have things in their cycle. People are going to want to figure it out. But we have created a mechanism by which if you're a successful retail mortgage loan officer or retail mortgage team or retail mortgage company, You can get full transparency, you can get total control, and you have to share a much smaller percentage of your profits with our platform and get mass productivity increases. So what we're promising the retail loan officer is we're going to help you make three times more money and cut your costs in half.

And that's a pretty compelling value proposition. yeah so thank you for that and this is a follow-up uh maybe how many more loan officers in 2025 would you like to onboard i didn't know if there's a capacity or if there's a way that you wanted to kind of scale this in terms of adding to the platform yeah so to be honest to be honest like in 2021 we had 5 000 loan officers Here we are onboarding 150 of them on the retail channel, right?

So the other thing that the platform provides is effectively infinite capacity to any loan officer team. And so, you know, I think we'd like to grow. I think we'd like to triple or quadruple the Neo channel. We're already going to dull it this coming quarter, as Kevin mentioned, in terms of production. So I think there's a lot of capacity ahead.

Thank you very much. I appreciate it.

Question comes from Brendan McCarthy with Sidoti. Your line is open.

Good morning, everyone. Thanks for taking my questions here. I just wanted to start off looking at the unit economics, just curious as to how the unit economics at the loan level, you know, trended year over year. And I guess really aiming to get an idea of, you know, how do you quantify the AI functionality and really, you know, you mentioned operating leverages is kind of positioned to improve looking forward. Are you able to quantify maybe how much you expect that to improve looking ahead?

Yeah, Kevin, do you want to take that question and I can fill in?

Yeah, let me start. So I think, you know, Brendan, there's a couple things here. So the unit economics have improved. I just take Q1. February was better than January. March was materially better than February. And when you look at our aggregate losses, March came in about $7 million, so materially lower. And the mortgage company essentially was breakeven in March. And so the unit economics is a direct result of the AI improvements are coming fast and furious. Now, there's always a market cyclicality to it as it relates to purchase season, purchase season kind of deferred a little bit here given some of the macro. So it's not going to be linear, but to date it's been pretty linear to date. But I wouldn't assume that's going to be true month over month over month. Where are you going to see the savings? And so I'll kind of maybe just guide you through the income statement so that the majority of the savings you're going to see through the continued technology improvements are going to be in the compensation and benefits line. That number is going to go up as we onboard the loan officer that Michelle just talked about, right? As we add LOs, comp and ben is going to go up, but it's going to go up slower than revenue, and it's continued to improve. if I think a comp and ban is a percentage of revenue, it's getting materially better and will continue to get better. And that's always been one of our challenges. The other place you'll see it is in loan origination expense. We will, that will continue to come down. So basically think of that as non-comp expenses on a per loan basis. We're safely below $1,000 alone and going even lower on that line item. And that is really a direct result of being able to deprecate vendors, renegotiate vendors, drive better deals, and use our technology to really lower the expense, the non-comp expense cost of manufacturing a loan. So those are the principal areas you will see at.

Great, Kevin. I think, yeah, I think the North Star is getting the total cost of production of a loan down to $1,500 alone, you know, $500 of sales labor, $500 of ops labor, and $500 of, you know, credit bureau, income verification, and all of those other sort of external vendor costs. And, you know, we're driving hard towards that. And if we are able to do that, you know, we're going to be six times cheaper than the industry's cost to manufacture a retail mortgage originator today. You know, outside of sales expenses, spending $7,500 alone to, you know, basically get a loan all the way funded through the books. So we think that there's certainly a lot more to gains coming out of the AI. We're starting to actually see it in the numbers with, as Kevin mentioned, the mortgage company becoming profitable this quarter, which hasn't been in many, many quarters. And we're going to now be able to, like, continue to grow. The important thing is that, you know, as you know, mortgage is a scale business. And so what we did this quarter with the addition of two additional methods of addressing of reaching the market, one purely on a software basis and the second on a platform basis is going to drive substantially more volume through the entire funnel. that's going to enable us to get a better pricing across our vendor contracts, get better execution on, you know, hiring and, you know, deploying labor and really get the benefits of scale that, you know, plus the AI can bring.

That makes sense. I really appreciate the insight looking ahead. And then I wanted to talk on the balance sheet. First of all, congratulations on the convertible retirement. I think that's a big, big piece of the story, but I'm just curious as to maybe longer term, you know, what kind of leverage level makes sense for the business and kind of how do you think about the balance sheet at this point versus where you'd like to be?

Sure. So, so I'll start, Michelle may want to supplement. I make a few comments as you think about our balance sheet and leverage. The first is to date, we have always sold servicing released. So we run a very capital-like business model. We don't really, you know, we're on a billion-dollar balance sheet, but half of that will be loans held for sale, and those loans are recycling quite quickly, right, particularly post the soft buying transaction. Because I think as we talked about in the AK when we did the deal, we didn't use much cash at all to actually do that deal, but we did sell loans held for sale that were unencumbered that we chose not to pledge to warehouse lines in order to fund that transaction. So from a leverage perspective, we don't really think about it as debt-to-equity per se, like where a lot of other companies may, because they run a big servicing asset on the balance sheet that they presumably, for most, lever through a financing facility against the MSR. But what I will say, the $155 million in new debt we've put on, it does not mature to the end of 2028. It's fully picked. Until we're profitable, we've informed our partner, our lender, that we will be picking the interest, and so that will accrue, but we will not cash pay it. And so we're quite comfortable with $155 million in debt due at the end of 2028. And I think the combination of market improvement and all the self-help we're doing and the work we're doing around technology, we think refinancing that, you know, three years from now should be, you know, well within our purview. So, we feel quite comfortable with our current leverage.

Great. Thanks for the insight there, Kevin. And one more question for me. Yeah, I know this is constantly a point of growth here is the B2B partnerships. What other opportunities are you seeing for B2B partnerships? and maybe you can talk about the pipeline there.

You want to start that one, Vishal?

Yeah, so I think there's, we've, so I'll give you some context on the bank that we signed up with respect to the, you know, I think there's basically two flavors of B2B partnerships going forward. First is a software-only partnership. There, we, what we, what we saw with, you know, ally leaving the business and all the banks sort of that we've pitched for ally-like deals over the past couple of years is that a lot of the systems and the processes that these banks have, you know, I think they're all in downsizing mode for their mortgage business. And quite frankly, they're not keen to outsource the bulk of the front office and back office like a full package, you know like what Al was doing to us. So now with the ability for these banks to utilize our software and basically then also scale up and down on the services so if they need a marginal processor a marginal underwriter they can they can use us but if they don't they don't have to but you know they can just use the software and get the efficiencies out of the software for their loan officers and their processors and underwriters, we think that's a much better go-to-market strategy, and we think we're going to be able to scale that up pretty rapidly. We have a whole host of fintechs and banks waiting as we deploy this one bank and get it across the finish line. To give you some context, for a typical bank to deploy the traditional mortgage industry stack. We're talking, you know, a million to $5 million in integration implementation costs and nine months. For this one bank client, we had them up and running on conforming loans in three days. And then they asked us to get up and running across their entire product set and also do wholesale for them. And we've got them up and running in 60 days. Zero implementation costs, zero third-party vendor fees, nothing. So all, you know, per funded loan basis. So, you know, with this one bank, just on the volume they're transitioning to the platform from what they did last year, we're going to make $4 million plus in revenue. And we think now with adding wholesale capabilities, we might be at like, you know, $10 to $12 million in revenue over the next 18 months on an annualized basis with this one bank alone. And they're small to medium-sized banks. So the pipeline is looking really good on that. The second part of the B2B pipeline that we have discovered really does work for us is other fintechs who want to get into the mortgage business, wealth management fintechs, lending platforms, personal loan platforms. And we're seeing a lot of interest from those platforms to start to diversify into home equity and eventually into mortgage and be prepared to turn their customers into mortgage customers. And these platforms have done a really good job aggregating millions and millions of users over the past couple of years, selling, you know, everything from buy now, pay later, personal installment loans. And, you know, we make it really easy for them to get in the mortgage business without having to hire LOs and underwriters and processors and so on and so forth. So we hope to share with you positive feedback and details and sign some big deals over the next nine months in this year with many of these large fintech platforms. So I hope that covers, like, the two different types of B2B that we're going to see going forward.

Absolutely. Very helpful. Thanks, Michelle. That's all for me.

The next question comes from Raina Kumar with Oppenheimer. Your line is open.

Hey, good morning. This is Jake Koiman on for Raina. Thank you for taking our question, and congrats on onboarding your first bank partner as part of the Tin Man AI as a Software opportunity. So I was just hoping you could expand on how this relationship works in terms of the economics and operational workflow, and what does the go-to-market look like to capture additional bank partners? Thank you.

Sure. So the way that it works is that we take all of their existing software and it goes away and they get one platform. We load up the pricing that they want and give them self-serve pricing control. We load up the underwriting criteria they want and they can have that attached to the pricing so it's the only eligibility plus pricing platform in the industry so they can add an additional underwriting criteria and you know charge up or down for it on an overlay basis all in one flow and it automatically triggers what needs to be tasked out both to the consumer and the processor and underwriter it does it all automatically. So it's really easy to learn once they kind of get the hang of it. And we basically deploy it an account manager and a product manager to help them do that. On the other side, we've created a retail origination module for them for their bank branches. We've created a wholesale origination module for them and a direct to consumer module for them for their website. And so they deploy that, and they take in the applications, basically mimicking the same workflow that they have today, but with an AI assistant handling everything. So they basically can now be on 24-7, 365 days a year for their customers, and their LOs can become three times more productive and start reaching the productivity that, you know, betters LOs have traditionally had in the industry. And then from there, their underwriters are able to just basically become exceptions managers. And basically, all of those folks get trained by our team. We have a SWAT team that goes there to get deployed. And then from there, they're up and running. The math on that is, per loan, for a funded loan, we're earning about $1,500 per funded loan in software fees and platform fees. And basically, they don't have to deal with eight different vendors. They don't have to deal with multiple systems integrators. They don't have to deal with any of that stuff. And so for them, that's very, very compelling. It's not just compelling on a cost basis. It's compelling because we're increasing the throughput of their people by two to three X, which substantially takes their and gets it more like better cost to originate, plus brings them scale because then they're not delimited by each marginal for the next marginal loan they want to do. They have to hire a new loan officer, you know, and a new processor and a new underwriter and having to manage all that. Anytime they have a staffing shortage, they can then just turn on us kind of like, you know, what AWS did we're doing for mortgages.

Basically, you can turn on or turn down instantly capacity and it just accommodates it great thank you appreciate the details once again if you have a question once again if you have a question it is star one on your telephone keypad your next question comes from eric hagen with btig your line is open hey thanks good morning guys um back onto the balance sheet maybe i mean how does the restructuring give you better negotiating terms with lenders and other counterparties?

You guys talked about the bank partnerships. I mean, how does the restructuring itself play a part maybe in your ability to like source and maintain those relationships? And again, like the restructuring itself, does that make you more competitive with some of the other entities who may be looking at those similar partnerships?

Yeah, sure. So Eric, morning, it's Kevin. I'll start. And then Vishal may want to supplement. It's certainly helpful. I think as we disclose, we're going to create about $200 million of equity creation as part of the deal. I think there were people who definitely looked at us and said, you have a relatively high debt load. Certainly for a company that's kind of at the low point of the cycle, hopefully the cycle improves here, and all the AI improvements will kind of drive us through the cycle irrespective of what the cycle does. But I think we've definitely fixed the balance sheet and that we've taken equity up, debt down as a course of this deal. And so when people do their kind of high-level diligence on us as a partner, I think they're really looking at us for the technology, what we can provide, all the things Vishal just went through. That is what they're really looking for, but they certainly want to make sure they have a strong counterparty as well that they're going to work with for years and years and years to come. And so we feel like, you know, on the margin, we've improved our pitch to them as a result of the balance sheet transaction. But we did the balance sheet transaction because it was just the right thing to do for shareholders. And it was the right, you know, it was the right kind of ROI on the use of, you know, the cash we used to actually get the deal done.

Great color there. Appreciate that. But, I mean, we hear constantly about the range of borrower profiles, the need for loan officers to effectively tailor a loan to the borrower's profile. I mean, how do you guys work with the software to address these different profiles? How do you benchmark that flexibility to, again, address the different loan profiles? Or is it really more effective to instead think of the better platform as really just being the cheapest and most efficient platform for the borrower whose profile is down the fairway, so to speak? And the niche for the software isn't really trying to be super, you know, tailored. You know, how should we kind of think about, like, where you guys plug in?

I think that's a really great question. So, for the first seven years of our life, Better was great for straight down the fairway customers. And, you know, we just crushed it in terms of cost and efficiency, unconforming, jumbo, you know, iFICO, you know, medium TI, ADL-TV type loans. And that fueled our growth. And I would say really onboarding the retail loan officers, we've had to build out the functionality for every loan type in Tin Man in the past 120 days. So three plus borrowers, who even knew that was a thing? But apparently Bank of Mom and Dad is really big in retail, right? And you need to have three plus borrowers. So we had to build that into our system. And now we have infinite borrowers, you know, can qualify. You can have 12 borrowers on a loan file that we have to build, you know, all the CUSB products. We have to build the, you know, construction loan product. We have to build all these additional products all into the system. And now the system crushes all of those loan products. For this bank, we have to onboard bank non-QM, bank statement, dock light. System now crushes it. And more importantly, the AI underwriting automatically is matching the consumer to the full product set and exposing the full product set. The loan officer doesn't have to do any work to remember any of this stuff. It doesn't have to go from the loan officer, from a conventional product, and then go through the funnel, go through underwriting, then get kicked out, and then get matched to a different product, and so on and so forth. it's like all happening instantly and so i think um you know one of the things that tin man is going to now be known for is not just being super cost efficient but actually capturing the full scope of products that are available on the platform which by the way is helping d2c dramatically improve its unit economics because all these people that we were previously turning away in d2c that we didn't have a product for we now suddenly have a product for so this is a you know the growth of uh tin man ai and the breadth of the product offering is happening is improving conversion across uh all of our channels yeah i mean eric the addition which i'll just said it but the addition of products is one of the biggest stories for us over the last three to six months through TinMan AI, the onboarding of NEO.

It's been a game changer as it relates to rolling out products.

Great color from you guys this morning. Appreciate you.

The next question comes from Boast George with KBW. Your line is open.

Hey, guys. Actually, that was very interesting on your comments about the way TinMan could disintermediate some of the LOF systems. Are the companies that you're speaking to, like the bank you noted, generally on a system like Encompass, and then they're looking at you guys as a lower-cost, higher-efficiency alternative? Or is it kind of the de novo? Can you just characterize the people you're talking to?

The people we're talking to are on Encompass or Simple Nexus. And the bank that we've onboarded was on Encompass, and NIO was on Encompass. And fundamentally, I think the, you know, the efficiency gain from moving from those two systems, plus all the vendors, this ecosystem around it, to our platform is pretty dramatic. And, you know, these companies obviously have huge sales forces, long contract cycles. But I think what's been really fortuitous for us, is this is all happening now at the same time as everyone is reevaluating all of their technology to determine whether it can work with the AI agents and LLMs. And basically, the bulk of these technologies that exist in mortgage land, they can't because you have seven or eight systems. You know, if you talk to OpenAI, they'll tell you the maximum number of function calls that the LLM can do at the same time as two to three function calls, right? So now how are you going to do to, you know, if you've got a delimiter on two to three function calls right now, how are you going to do it across eight systems without the type of latency that we're talking about or minutes of latency and nobody wants that? So you can't deploy an AI agent on any of these old broken systems. So I think it's sort of like a seminal 1995 to 1999 moment where, you know, suddenly the internet's a thing. Well, now AI is a thing and none of these systems are AI equipped. That's why you haven't, I mean, we've rolled out Betsy six months ago and you haven't seen anything out of the industry other than an appointment scheduling bot for loan officers. It's like an interface on top of like, you can book me. So I think fundamentally, our lead is like a generational lead here. And, you know, I have been super surprised by the industry response, particularly from very large mortgage companies reaching out to us and saying, wow, this worked. I assume you get it to work for them. You'll get it to work for us. You know, come out and see us. And so we're going to scale into this.

Okay, great. That's interesting. Thanks. And then actually have companies that you're speaking to noted any concerns about essentially buying technology from a competitor and to the extent this thing grows meaningfully? Is there any sort of alternatives like maybe separate this out or is that too early to think about things like that?

I think it's too early to think about that. But I think the companies that we're talking to, we're not in retail. They don't view Better.com as a competitor. And I've been transparent with them. Better.com D2C might become 25% of our business or even 10% of our business over time. And, you know, I think, yes, there's that, but then there's also the, you know, when you're facing a potential extinction event, you're less worried about, like, buying a tool that helps you get past that extinction event from someone who could or would have maybe be a competitor.

Okay. Yep. Makes sense. Thank you.

This concludes the question and answer session. I'll turn the call to Vishal Garg for closing remarks.

Thank you all for continuing to support us as we build America's leading AI mortgage platform. And in doing so, help consumers get a marriage, get a better rate, have a better process, which lets them have a better house and a better life. While the past five years have been challenging for us given the state of the market, we're now playing offense hard again. We're looking forward to executing on our continued efficient growth and to share more positive news with you in the quarters ahead. Thank you.

This concludes today's conference call. Thank you for joining. You may now disconnect.