Real Estate’s Data Problem: Why Tokenization Keeps Failing | Matthew Schneider, Building Inc.

Speaker A: Earth metals like gold and silver, those are all RWAs. So once you tokenize that, it will maintain being a cornerstone of a healthy portfolio. What might change is that people will allocate to these alternatives more, so long as they're more transparent and potentially liquid. And this is where the tokenized version of the RWA could be superior,

Speaker B: foreign. And welcome to episode 91 of Colors of Web 3 and Entrepreneurship. My name is Lum, your Web 3 host. Uh, for those of you who are new to the Show, Colors of Web 3 and Entrepreneurship is a, uh, show highlighting the journeys of builders and innovators in Web3 and entrepreneurship. Some topics, especially in Web3 may get technical, but we do our best to keep it accessible for most people. Here I'm today I'm joined by Matthew Snyder. He's the, uh, CEO and founder of Building Inc. Matthew, do you want to give a. Give a quick intro by yourself.

Speaker A: Sure thing. And thank you so much for having me on the show this morning. It's great to be back on a Web3 podcast. I've been doing a lot of real estate recently and so I don't get to talk about the technical side of things. People don't care about blockchain, but on this show the audience does. So it's great to be here. Uh, my background has been in real estate tokenization for six years now. I've seen the industry change a lot. I've worked alongside different corporations, institutions, and those who are really at the frontier, like pushing the envelope in terms of what blockchain can do in legacy industries. So, uh, this is my second company in the space. I started in real estate syndication, so helping to raise money for projects, but doing that in a tokenized manner. And then I pivoted along the way, started focusing on data instead because I realized that was the big missing gap between what's going on in the real world and these digital tokens. Somehow they have to interact and the answer to that is data. You can deliver data outside of this. Uh, I am also on the board of directors for an organization called fibery, which is the foundation for international blockchain technology and real estate expertise. It is a non government organization that brings together different researchers from around the world to talk about new things in blockchain. And then on top of that, as if I wasn't busy enough, I do lots of podcasts, lots of public speaking. I write papers for the industry and just do as much as I can to help educate people about this new technology. The pros and cons, challenges and opportunities of implementing it.

Speaker B: Nice. Yeah. Well, that's very, uh, comprehensive introduction, I would say. Um, I'm sure many of you will be very, you know, curious about like your professional background. Maybe if you could walk a bit more in details about like what you did before, before you actually joined the web. 3 I think you said you did in real estate, right, Most of your professional career. Maybe walk us from that point walking forward.

Speaker A: Yes. Well, my, my career is a lot less interesting than people might think. Most of it really has been spent in. At the crossroads of property, technology and tokenization or blockchain plus capital markets. So finance, real estate and technology. That's been most of my career, to be honest. Before that, I was doing some day trading with an independent prop deck desk. So I was looking at things going on in the market. We were doing fundamental and technical analysis, but I didn't do that long enough that you could call it a segment in my career. It was more of an entrepreneurial endeavor. So I had published a book in high school. I was making music. I wanted to trade the markets. I then graduated. I stayed trading the market. I got into e commerce. Like I, I was just juggling, uh, a multitude of different entrepreneurial things. Co founded some groups. I wouldn't even call them companies at that point. And then I had started to look into real estate. I got introduced to the industry, to the market, started researching that, and then eventually founded a company. And that was uh, just as Covid was beginning. So that was 2020. Co founded a company. We were helping to facilitate the investments for real estate. Decided to take the tokenization edge. But really m. All, all of my career has been focused right here in this space. I spend every waking hour thinking about how tokenization and real estate and finance, how they can all interact and solve real problems that people face. And four, if you can solve those problems, you're going to get adoption. I didn't want to be taking the perspective of, hey, this, this technology is great. Let's, uh, try. Let's try to apply it to everything. That. That doesn't work. You can't be a solution searching for a problem. So I, I took the perspective of real estate has lots of problems. Let me see if there's anything that can be addressed by some of these new technologies. And the answer is yes, there's lots. If you just understand the fundamentals of blockchain and transparency and how real estate is the complete opposite, if you mesh those together, there's definitely areas for improvement.

Speaker B: Nice. Yeah. It say that you've been in Full real estate for a long time. You said there's some interesting problems there to be sold in real estate. I'm sure there's so many problems in just about any industrial. So maybe you can walk us through like some of the problems that you're planning to solve with in real estate. Sure.

Speaker A: So the biggest problem that real estate, whether we're talking about construction or operations, financing properties, transacting them, the biggest problem that persists is always the presence of data. Do we have data? Do we have information? Can we make decisions? And without that, you have an industry that is full of risk. It's full of people who don't get along, they don't trust each other, transactions slow down, buildings fall apart. Don't even try tokenizing it because that's going to be a lawsuit at some point. That's how real estate operates right now. And it's true many industries are like that. However, if you look at healthcare or agriculture or finance, they were very open to innovation, they were open to new technologies. Real estate has always been stubborn. And actually other asset classes like real estate as well that are trying to be tokenized, they're all very stubborn. And the reason why is because they think in longer cycles. Buildings exist for decades. And so the adoption cycle for new technology is just as slow sometimes. And the people who are in charge of these properties or portfolios tend to be older. So they're more resistant to new technologies, especially if those tech, if that tech is untested or unproven. And in many cases blockchain and tokenization was unproven. Now that's changing. Now we're seeing that there are good benefits and efficiencies, but uh, the, the issue of data. How do we capture data, how do we communicate that, how do we trust that, how do we put that on a, put that in a single source of truth? Can we move that onto a shared ledger? And then at the same time as you move data, can you also move value associated with that property or that assets in general? So it took a while to figure out exactly what we wanted to do with data because that's uh, more of a concept than it is a tangible item. Are we talking about files or data sets? Are we talking about artificial intelligence? But what we landed on was the methods for holding data like cloud are actually pretty good, but the methods for developing trust in the industry are not so good. So what if we mix that, so what if we maintain cloud and all the uh, software and systems that already exist and then we VIN blockchain In a way that becomes like a layer for receipts because blockchains are immutable, so you can't tamper that. So if I'm moving data in real estate paperwork, if I'm showing you that work has been done, that two people have agreed on something, that money was moved, and then I hold that the evidence of that on a blockchain for as a receipt that everyone can look at and confirm that it was completed, or go back in time and see who did this at, ah, what point. That although it seems like one extra step, it's actually very helpful to the industry because what happens is now when we double check documents, when we're conducting diligence, when we're trying to trade real estate tokens, when we're trying to collateralize something, we can view the data and then we can look, is there on the blockchain some sort of hash, is there metadata, is there a package, a payload that we can reference to see if that data is any good? I see the data. The question is, is it any good and can I confirm that using, uh, some sort of anchor to the blockchain? Again, it seems like an extra step. Wouldn't you just put everything on blockchain? That's what people ask me. And you can't do that, especially not in an industry that loves privacy and is resistant to change. So this is sort of a half step in that direction where we said, okay, we will respect the way real estate is operated and, and we will use blockchain in a unique way. We won't force everything onto blockchain. And it's not good at that anyways. It's not a big, uh, database. You don't want terabytes of data trying to be called from the blockchain. But we will use it as a receipts layer to show integrity. That's the approach that we took. And uh, it's worked really well so far actually. People, if people who don't like blockchain trust the blockchain in this capacity and, and then people who are familiar with Web3 and blockchain like this use case because it's unique, most people are just talking about moving ownership or fractionalization and we say we have a new way to involve the blockchain as a receipts layer. And people are, they're intrigued by that concept.

Speaker B: Nice. Uh, yeah, like certainly, I think what you said is a very valid use case for blockchain as well. Uh, curious about, like what, how did you learn about crypto in the first place?

Speaker A: What brought you to crypto it was around 2016. Yeah, I was looking into bitcoin and it was very affordable. At that point I was telling people you should buy bitcoin. If they listened to me, they would have made a lot of money. Uh, I did not have enough money to really put into bitcoin so I didn't make anything from it myself. But I saw that crypto was emerging. I thought it was interesting. This was back when Litecoin and dogecoin were serious contenders. Now they're pretty much non existent. But um, I thought it was interesting. I thought it was mostly uh, for investments, maybe a store of value. I wasn't looking at the underlying technology. It wasn't until maybe 2018 or 2019 that I saw a case study for a project in Singapore, I believe it was, that was trying to move ownership of traditional assets onto a blockchain. I thought, wow, that's, that's really interesting. I should explore that. I was already looking at capital markets. I knew people in real estate. I hadn't yet made the transition into that industry, but I thought it was interesting that people were trying to use the technology behind bitcoin, which I didn't even know was a blockchain, in order to create new efficiencies. And then I started talking about this with friends investigating it further and eventually made that decision to that I think it just, you know, it just makes sense. Peer to peer transactions, no intermediaries, transparency. I think this is going to be the new rails of finance. It was, it wasn't so much an epiphany, it was just this growing conclusion that the technology seems to work and you had all these events that were going on around the world where other people in the space are saying the same thing. Blockchain is awesome. Web3 is the new form of ownership. It's the new version of the web. And I said okay, you know, let's, let's ride this wave and went all in.

Speaker B: Nice. You must have had a very strong conviction then, right after looking into blockchain first use case and then Ohlin. Nice. Yeah. Well, I just one fun question before we dive into your uh, current company that you're building right now and you share with us what you do for fun. I mean besides uh, fun as in like your personal hobbies, not in like writing papers. I'm sure that's fun as uh, well too.

Speaker A: But I do a few things. I like playing chess, piano, working out, going for hikes. I try to do things that still stimulate my mind and body so I stay productive. In some capacity or stay healthy at least. I know some people say concerts or watching Netflix. It's never appealed to me. I like puzzles, you know, that type of stuff. So it's fun to me. Fun is what you make of it. I'll add in one more. I really like traveling around the world, so I've had the unique opportunity to travel to different parts of the world, including Japan. Um, I don't know if you make it out to Fukuoka very often, but I was there.

Speaker B: I've been there a couple of times. Yeah, it's.

Speaker A: Yeah, it's a, it's a wonderful little startup city that they have because a lot of people are leaving Tokyo. It's very expensive and trying to find other places to start companies. Uh, so I like traveling and then often when I travel I get to speak publicly. So I'll go up on stages or give keynotes, give participate in panels and talk about very similar things. What is real estate? What is tokenization? Why are these involved with one another and walk people through that transformation that's happening and hopefully help them build up confidence in it to say, yes, let's adopt this technology.

Speaker B: Nice. Awesome. Ah, wait, so you've been to Japan? Did you go to Tokyo or you just go straight to Fukuoka?

Speaker A: I went straight to Fukuoka and then I hopped on over to Hong Kong for Fintech week.

Speaker B: Nice. Okay. Yeah, actually I like Fukuoka a lot as well too. Actually I think I even spent a couple months already over there at least. Yeah, it's a lovely city. And one of the four, I think designated like startup cities in, in Japan. I think the other. I don't know if I can recite them off top of my head right now. One is Sendai, the other one is Sendai. I think one more in Kansai. I forgot the name, but yeah, uh, anyway, but yeah, Fukuoka is one of them. Nice. So you were speaking there, I suppose.

Speaker A: Not, not in Fukuoka itself. Although I had a meeting with the city because they were trying to attract, you know, companies from around the world if we wanted to set up an office there. So it was still business related, but it was fun. Traveling is fun.

Speaker B: Yeah, I bet. Nice. Yeah. Well, I think it'll be more fun if you set it up here, you know. Well, obviously it would be very different. But yeah, I think they, they do, they do try to attract a lot of different companies. Yeah, I'm obviously, I'm um, based in Tokyo, so more involved with the startup ecosystem here. But yeah, I think they trying quite hard. Obviously a lot of the center of gravity is still around, uh, Tokyo, but having some other company I think in Fukuoka, Line is like the biggest company out there. Like the messaging app system here and then, yeah, very few. So if you would like to set yourself that you might actually be like the first Web3 company, the Crypto.

Speaker A: But potentially, yeah, especially tokenization there might be some payments companies. I see a lot of payments like web3crypto, cross border stuff. But for the more boring institutional use case that I categorize us with, we would definitely be the first. I mean we're really one of the only companies that is playing a role like this and maybe this would be a good segue. I can just go right into what we yes, are working on.

Speaker B: So yeah, sure, perfect. And yeah, if you have any documents or website, feel free to share as well.

Speaker A: Yeah, we'll jump into that in uh, a second. I can do ah, a quick show of our websites, uh, maybe some other items. But for folks in Web3 I think the best way to approach this is that they're probably most familiar with Oracles and the important role that Oracles play in moving data and to take data that exists off chain and to connect that to a smart contract, that's the mental model I want people to have. As I talk about what we're working on and if we go back to the fundamentals, when I said real estate has really bad data and it can't be found, it can't be trusted, how can you expect an Oracle to work in an environment like that? You have to connect the Oracle to something to grab that data and then you have to deliver that to the smart contract, which could be tokens that are, that people are trading or they're using in a protocol, they're collateralizing. So if you don't have the data and you can't deliver that, then your token is, you know, is could be displaying a number that's incorrect, that is out of date, that isn't trustworthy, that could be manipulated. It creates all sorts of weaknesses. So if people have that mental model it's like, okay, oracles are, they're useful but they have to connect to something in the real world. Right. Blockchains don't exist in the real world. So somehow we have to connect them to that. And that was the thought process for what building was trying to establish. We know tokens exist and it's at the point where the technology is there, the regulation is there, you have oracles, you have chainlink, you have other proprietary Oracles. They need something to connect to in the Real world. And that's easier said than done. People say, well can't you just upload some documents? You know, get, get some data. The problem is real estate is one outdated. So a lot of stuff is still in paper form. It, you, you can't access paper with an Oracle. That means you have to scan it, you have to vector, embed it, you have to put it into some sort of database and then it has to be hopefully machine readable so that it can be interpreted by an Oracle. Or you need to use something like artificial intelligence to extract data from the doc documents or system and then put that in a structured format for the Oracle to read. So first of all, yes, it's outdated. Second, numbers can't be verified where they came from. So someone could say we did work on the property or we refinanced or the estimated value of the property is this or the portfolio is this. But that is not. It doesn't have the qualifications for someone in web3 to go then invest in that or to use that as collateral because we need to double check that number number and we need to perform our own due diligence and say is this accurate? Where did that number come from? Can we get an independent opinion, a third party to come in and verify that for us? How do we know the work was actually done? How do we know these numbers add up? There's all sorts of questions that arise and that already exists in real estate. Those questions exist beforehand. People don't trust each other, deals fall through, there's lawsuits, there's all sorts of, of intermediaries. And now you're saying, you know, let's, let's attach this to a token that's going to be circulating around the world. If you haven't solved the major problem first, that token is going to be, it's, it's amplifying the, the issue even further. Especially if people start staking and collateralizing and just doing all the, the fancy defi stuff off of numbers that we can't verify. That is a huge risk. So you have that element. And then the third part is let's say someone does upload the right data. Let's say it is a good number, like it's a proper appraisal. The third issue is that it's not dynamic enough. When we talk about tokens, we're really thinking about dynamic digital markets. The fact that we can trade, we can move really quickly. If the data on the property is not being updated or it's being updated once a year, once every two years, that token, it's overkill is the way that I would describe that. Because now you have something that's very dynamic, dynamic and could change every second. And it's not being given that opportunity. It could maybe change once a year. You know, it has the capability to digest data every, every second, but it's never going to change if the underlying real estate is not uploading new documents, new data, if it's not going through the verification, and if it's not getting a new mark on the property. And so really what you need in place is data infrastructure like a data layer. You need pipes to be able to carry that data to the Oracle, which then connects that to a smart contract. That's the missing link. And to be honest, it's mostly a Web2 problem. It's a legacy industry that needs to be able to move data more quickly and then connects that to Web three. So Web three set. They, they just don't have the, they're waiting on the old industry in order to make its move and to back this up to, you know, reinforce this. Tokenized real estate has been talked about for years, and yet it's one of the least tokenized asset classes next to maybe venture capital. So people are bragging about how many trillions of dollars real estate is worth, and yet it's not being tokenized. And this is why. It's because the underlying assets cannot produce the data that an Oracle or a token needs in order to function properly, in order to be traded, in order to be used and valuable and compliant. So building steps in. And it took me a while to figure out how we wanted to approach this, but what we landed on was, okay, let's, let's do two things at once. Let's fix real estate data first of all, let's get it in a single source of truth. Let's help people verify it, structure it, use it in workflows and valuation models and tools. And then let's also solve the tokenized real estate data problem, which means take all that real estate data and package it in a way that an, uh, Oracle can grab it and then correlate that to a token somewhere. And that's usually it's looking for a price, it's looking for a risk rating, it's looking for some metadata and context. Let's solve both of those at the same time. And the reason why I separate that, why I don't just straight call US a, uh, Web3 company, is because if you're in real estate, you don't care about the Web Three portion you, you care what's going to make you money right now. So I said, I have an idea. Let's get a foot in the door. Let's sell a platform to real estate developers and asset managers that makes their life better. It helps them manage data, different AI tools for analysis and workflows, things that they worry about every single day. They get that, that's, you can sell that to them. And then at the same time, what I'm actually doing is I'm preparing them to have that data ready for tokenization. It is undeniable that either the data is going to be anchored to a blockchain or the capital stack. So the equity and the debt is going to be blockchain native, or at least blockchain represented at some point. So that's the future that's inevitable. So really we're getting them ready for that future by helping them prepare their data. And then if they want, they can open that up in the second portion of our platform and start working with a broker, dealer, a tokenization service provider, and Oracle and connecting all, uh, that good data that they already have to the token that is being used for fundraising or secondary market access. And we do this in the form of a platform. Now, we were talking beforehand that I'm not able to sign in right now because of two factor, but maybe I can at least do some screenshots. Uh, of.

Speaker B: Sure, yeah.

Speaker A: The platform that we have here, so it's for folks who aren't able to see the video portion of this as we talk about data. First, I, uh, just want you to think about a normal data room. I mean, that could be Google Drive, Dropbox, whatever it might be. When I talk about data, we're still talking about data rooms. We're talking about a place to upload files, to be able to organize things. We're not reinventing the wheel there. It's a very similar process. In fact, we connect to those systems. So if someone's in real estate, private credit, private equity, and they have a bunch of files sitting around, we'll connect to that and then we'll start to organize that in a better format that's associated with the property. And the first thing that we put together. Let me see if I can zoom in here. Oh, perfect. I can zoom in. We said, you know what, there should be a way to be able to track all the data to the correct property. And so we created the asset record. What, what if there was an accumulation of every single document and data point that a property could possibly need? And we could find that in one place. That was the first module that we built. Let's get all the data concentrated to one property. Then we went a step further because as I explained before, you can move data, but the industry still has a trust issue. We still have to verify things. And this, this is where we have a very unique. I don't think I can zoom in any further. We have a unique approach to using blockchain here as the, the receipts layer, because every single document or data set that gets clustered to a single property is also getting a fingerprint. So it's getting a hash that is anchored on the blockchain. And so this, this functions as a receipt to see who uploaded, uh, the rent roll, who uploaded the, uh, engineering report, the sustainability report. So what system did that come from? How is that being handled now? And what's the current condition of that? Is it up to date? Has anything changed? And if it has changed, can we go back and see how that has changed over time? And in every single step, blockchain is acting as the immutable receipts layer so that our team or someone else's team, like the, the real estate developer, their stakeholders, or a counterparty can confirm the veracity of that data. That is so, so valuable. It's such an important step. It's a lightweight use case of blockchain, but it is worth its weight in gold. So that was first and foremost, let's concentrate data. Let's create an asset record. Let's have people or help people inspect the data and understand it. Then of course, we needed something that we could really sell to real estate developers and asset managers and convince them to use new technology. And the way that you go about that nowadays is artificial intelligence. So if we can help them do something faster and save time, save money, and ultimately boost the value of their portfolio, they will pay for it. So I can tell people about, uh, good data, I can tell them about tokenization, but it will go in one ear and out the next. They need to think about what's going to make them money. So we said, okay, we'll also include artificial intelligence. And so what this does is it allows people to process their data at speed and at scale. You can do a lot more with AI. I don't have to explain that too much. So you can imagine in the platform, we're helping them extract data from documents and put that in spreadsheets, put that in different models. You can also do analysis of the documents or of the deals and understand this better. A counterparty can do Their diligence. On top of that, if, if there's two documents that conflict with one another will help reconcile. Like, AI is super, super helpful. It sells itself within the platform. So we have data, we have AI, we have the provenance. So diving into the um, and it's more robust in the platform itself, these are screenshots. But the, the provenance layer is really important because when I give you something that's been handled by AI or give you a, documents that you're not familiar with, you want to understand it better. And so I can show you, here's where that document came from, who signed off on it when it was updated, the context that it sits around, what it influences. So what are the dependencies? And if people want to do something like an appraisal and produce a valuation, if they're doing a risk analysis or, or trying to produce a rating, whatever it might be, this is an important step. And it, it compresses the amount of time that it takes for them to do that task. And if we go back to saying tokens want a lot of robust data, but it needs to be good data, this makes that happen. Because if it usually took six months or 12 months, uh, to get around to an appraisal because it's so expensive and it takes a lot of time, but now you could do that every month, you could have a monthly mark on the asset which is correlated to a token. Right? So does that logic follow where now we're helping people do things faster and at scale, which means that we could deliver more good data faster to the token. So no longer do we have, is it a trade off? Do you want a lot of data or do you want good data? Now you can have both because of a system that exists in place and then different, uh, oversight tools and then what we call live markets. And this is where you can start to involve the actual tokenization of the capital stack, where after you process all the data and it has receipts so it can be trusted and people are looking at it and using it, let's start to turn this into value. So are we producing a new, uh, valuation for the asset? And if so, can that number be calculated? Can we run the waterfall, apply it across the capital stack, and then connect that to a token using an oracle? And so what happens is as the underlying system of record that we offer gets updated, new information, new intel, new documents, new performance and health about the property, as that happens, it can be connected to the token in near real time. And now what people are holding, what they're invested into what they're trading is truly reflective of what's going on in the real world. Or to the best of our capabilities. If we see certain documents aren't being updated, we can send a notification and raise a red flag so that investors can see that if everything is updated and there's a new number, people will see, hey, my token is worth more. This is the appreciation I was looking for. I would be more ready to exit. Or for people who are collateralizing, we don't, you know. So the whole basis of FTX was that there was, um, unknown value of the collateralized, uh, asset. It's the same case in real estate. Actually. This happens more often than you would think. And it's happening right now in the US where people did not reprice their real estate for years because they didn't want to admit that it was vacant. And so rather than being transparent about their vacancies, they just let that accumulate to the point that these properties, some of them were selling at a 95% discount. Well, if you had loaned $50 million and now the property is being sold at, uh, a 95% discount. So if we do the math backwards, you know, let's say it's worth 75, you loan 50, it's, it's being sold for, um, nearly $7 million. Pretty much everyone gets screwed there. The bank is screwed, the investors are screwed. That's, that's a big issue. And that gets fixed with good data that flows so that we see all the time how the assets are performing. That was a lot to throw at the listeners. So I'll pause there.

Speaker B: Yeah. Wow, cool. So, um, sounds like a very comprehensive, uh, product, but I think. What, what stage would you say the company is in? I think you said earlier in the beginning that you're still doing some fundraising. Right. But I think there is a product.

Speaker A: Yeah. So we have this version of the product in a private beta. So we have a handful of customers that we're working with and we're fine tuning the product to their satisfaction. So I might as well reveal it to the listeners. The reason why I can't do a proper demo is because I'm locked out of my own two factor authentication. I can't get into my own email right now, but otherwise I could give a real tour of what the platform looks like and what's going on there. We have a lot of exciting things and, um, yeah, some early customers. We're at the point that we're going to expand globally, build out some of our enterprise function and so we are raising our round. I've been building up lots of uh, relationships in the space. It's an interesting time to be fundraising because AI has really democratized people's ability to create a demo or to create a front end. And so investors are bombarded by these AI created platforms that might not actually be functional or have a back end. Yeah, so it's an interesting time to be fundraising, but nonetheless, when you have a product that is functional and robot robust like ours is between the platform and the system of record and how this is going to change how real estate and tokenized real estate is priced, how it's handled, how regulators look at it, people are pretty invested in the concept and it's just a matter of growing our company successfully.

Speaker B: Nice. Yeah. You said there's some m. Very early customer, right? Ah. Are these people coming from the real estate industry? How are they, are they using your product the way you imagine them to be using it or.

Speaker A: So far, yes. So everyone is from real estate. They're folks who are developing new properties or they already own properties and are going to go through some sort of legal or capital event, in which case they need their data to be prepared, they need all their documents. And our platform is just a very useful and affordable tool. In order to see what documents do I have, are they up to date, Are there any changes? Has someone signed off on this? So that when they go to the accountants, the attorneys, the broker dealers, or the brokers themselves, the appraisers, the insurance companies, the next buyer, whoever, they're completely ready. They can say, look, we have everything prepared for you, you can perform your diligence very quickly and hopefully we'll all agree in the end, not too much negotiation, not too big of a spread on the bid and ask, let's just move on forward. And they, they appreciate that. We don't even talk about the technology that much. It feels like magic. Sometimes you just press a button and it extracts data or it gives you an analysis or the, the provenance portion and the receipts. We do have uh, the, the capability to let them view that on a blockchain if they want. But uh, really they're more interested in the logic. If five years from now someone is getting sued, could I use this as evidence in court? And the answer is yes, actually, because we can, you have that auditability. So we can produce how this was, you know, what source the doc, the data came from, how it's been handled, the current condition of it, and people can verify that using the blockchain as A uh, as a receipts layer or like an audits layer. So, so that's, that's what Web, Web 2 thinks about it. What's interesting is that in Web 3 we've had multiple different tokenization platforms and broker dealers come to us and say hey, we have all these tokens that we're handling but there's no way to really connect it to the real world. And an Oracle is not the solution because again Oracles need to read something. And so we become that solution, we become the data layer that would support those tokens and Oracles.

Speaker B: Makes sense. Yeah. So you did say that everything works right, which is great to hear. Whenever I hear that a product is working well and then user just press a button. It's like magic. Ah. Uh, so under the hood of the product, which blockchain is it actually used as the immutable layer right now?

Speaker A: So we're multi chain. So that's a combination of Hedera polygon. We have quantum resistant chain that we're integrating. And then I've had two more come to us. I mean so we had a public partnership announcement with Integra but they're just on their test net currently. So the idea is to be multi chain or at least chain agnostic because one, you don't know which chain is going to persist and have an active, have it, have enough nodes, have enough resources and also stay competitive in their costs. Like if we had chosen Ethereum, um, you know over time that would have been a very bad uh, choice. Just too expensive. But it's interesting because when you're just creating the fingerprint and the uh, hash, you don't need the same chain requirements as if you're doing high frequency trading. So uh, we might not need like hyper liquid because there, there aren't all these transactions like that happening. You, you have hashes that could be happening periodically and there's not a lot in that payload being delivered to the block. So you, you, you simply don't need a whole lot of resources. And then the, I mean another way to think about the, the multi chain is that as we enter a quantum era you need resistance to any sort of manipulation. So if we can say this data has been hashed on multiple chains simultaneously and each of those chains uh, is there is its own immutable ledger and you can cross verify that, cross check that it just acts as another layer of trust in the whole thing. I mean at that point it's getting pretty technical. I don't, you know, not a lot of real estate people are going to dive into that. But if it came to worst case scenario and we definitely needed to prove something, multi chain would be the way that you can say look, it's been hashed in multiple places, you can confirm that makes sense.

Speaker B: I think it's like equivalent to the redundancy concept. Right. In Web two. A lot of the servers obviously one chain fail or fail to record for whatever reason, reorg or whatever. Then you still have um, the provenance on the other chains. Correct. Can you talk a bit more about the current business model? You still continue focus on Web2 Real Estate Company and how do you plan to generate revenues? Sure.

Speaker A: So we have a few different ways that we commercialize this. We have our subscription model where people are paying to get access to the platform because it's going to help them organize their documents, index their documents, use AI to extract data, to build models, to run analysis, to assign their stakeholders to certain tasks like hey John Smith, can you please review this documents? Can you upload it? We're getting ready for an appraisal. So that comes in at a, at a monthly subscription cost that you pay for every property that you bring on and it's actually quite affordable all things considered. We tried to match it with like an enterprise claude or enterprise Chat GPT where I saw finally a lot of folks in real estate are trying to adopt technology but they're doing it, they're, they're grasping the low hanging fruit where they're saying we're going to revolutionize our portfolio with ChatGPT. You know, it's, it's a half step in, in the direction where it's like I'm really glad that they're using AI, but that is not going to connect them to digital markets. That's not going to get them ready for tokenization. It's not going to help them with inter organizational diligence. So we said okay, we'll match the pricing on that if, if you want to pay more. We have enterprise success contracts, we have white label, we have all that type of stuff. But let's just get people to, to pay for something that they're going to use and that they see value in because they're trying. Right. They're paying for chat GPT but that's really not the right solution for this. So we're saying hey, if you're going to spend the money, you might as well spend it on building. It's much wiser that way. And then we have different usage things and we're working on integrations as well. So Anyone in the Web3 ecosystem, if it's a blockchain tokenization service provider, if it's a broker dealer, hopefully we can connect and interact with your services either by API or MCP and, and plug in if, if anyone's working with agents that would be, you know, we're really eager to see how agents, AI agents can interact with this data substrate especially because agents need governance and this is providing the verification rails that agents would need to be held accountable. Because if agents are creating price, if they're trading and you can't pinpoint at a moment in time what the data was that the agent acted on, you're going to have problems. Agents are not held accountable. People are held accountable. So people have to prove what the agent acted on. And this is the governance the, the walled garden that they, they can enter. So I'd be really eager to see other integrations that we can include there and if anyone has them, love to explore that and how we can commercialize that together.

Speaker B: Nice. Wow. Sounds like um, quite, quite a lot of things going on under the hood. Can you share more about the AI models or the thing that you are running or using for the product?

Speaker A: Yeah, a couple items are proprietary, a lot of it. And I'm happy to admit this actually because the, the best thing that a company can do right now is to leverage the huge robust, multi trillion dollar models that have already been created. So there's certain places where we will give a user access to whether it's OPUS or maybe fable at some point. And then you have chat GPT's models, I can't think of what they're called. I think it's just 5.5 or whatever we do give them access to those tools to say hey, do you want a chatbot? Do you want an analysis? Are you trying to do some math? Here's some models, they don't belong to us. They're in the platform, they're accessible to you. Um, and this is actually, it's a win win because instead of having to go to different software and drag in documents, in this case the AI comes to you where the data already lives, where it's already been verified. And what we're fine tuning is to help that those AI models that, that aren't ours to understand the different qualifications of data. Because there's a difference between a document that I just give you and a document that says it's credentialed, it's signed off, it's verified, it's up to date, it's trustworthy, the AI needs to be able to understand what to use and what not to use and then to be able to cite those when it makes a decision. Multi, you're not going to make a 50 million dollar decision off of something that AI just, just spits out. You're going to audit that, inspect that. You're going to go through all the work. So that's what we're trying to modify in the AI realm. Other applications. So data AI is really good at anomaly detection, is really good at data extraction and data placement. So we have like model builders where you can open up a spreadsheet and it's going to place the numbers in there. It's going to give you a confidence score. AI for just synthesizing documents. So if, if you have a document and we have, have certain fields that we're looking for and we want that summarized, it's, it's good in that capacity. And then what we do, because we don't actually hold on to any of the files ourselves, that's a big issue for privacy and controls. You can opt into each of this. So AI never runs on anything automatically. You have to opt in, you have to consent to this whole process that says, okay, AI, it's operating in an encrypted manner, but it's going to pull out some of these data artifacts or it's going to synthesize this document. So if it's highly confidential and you don't feel comfortable in a model doing that, then don't, you know, you shouldn't, you can manually extract that data. Um, the, the other thing is the, the potential for agents. And so there's a couple different shapes and forms this will come in. So you could have agentic surveillance of a portfolio or documents. And what it's going to do is it's going to see is everything up to date? Is, is anything expiring? Are there any, are there any lapses and deadlines? Are there any conflicts between two documents that have just been uploaded and can we try to correct that and what it will do? And this isn't really fully built out, this is more thesis speaking, but it can then notify the correct stakeholder and say, hey, we detected something out of line in the property or in the portfolio, or someone is performing diligence and they say, hey, I'm a bank. Do you give us permission to deploy an agent into your, into your property and just check the documents every month or every week and make sure that your, your everything is compliance, that all the covenants are covered, um, that the Value is good. And someone might say yes. The property owner will say yes. You, uh, you can deploy your agent and it can look over everything, all the data and then notify each party if something seems incorrect. And while we're on this topic, I should probably clarify why would someone opt into that? Like that seems kind of scary to have agents doing that. Because there's real benefits to transparency. So if a bank were able to offer you a lower cost of capital, like a lower interest rate, they have an agent deployed that's looking for non compliance or it's monitoring the value of your asset. That's a pretty good trade. And already if you look at highly regulated environments or public markets, they have very similar reporting and audit requirements. So for a private market to abide by that, it's not really an invasion of privacy. Like the big players already do this. They already have interrogation of their property or asset data. It's just that for the longest time people in private markets didn't have the tools to actually update all of that to stay compliant. And so they, they, for lack of a better term, stay in the shadows. They, they maintain that opacity. And, and that's, that's just the trade off. You know, we say okay, you're not going to update everything, you're not going to be transparent. Then there's limitations on um, who can invest in your deal and who can trade this and what your cost of capital is and what your reserve requirements are, are, that's just how it's, it's, it's operated. So we're helping them take the leap. And as long as the agents have good governance, they first of all they'd have good instructions and then rules about what they can look at. I don't think it's going to be a problem at all. And if they're just looking at certain data artifacts and the receipts layer and not the entire document and personal information, it shouldn't be an issue.

Speaker B: Makes sense. Yeah, that's very smart. You know, heading preparing a lot of companies for the future where we have like millions and millions of agents, right. Interacting with each other and acting on behalf of the companies and. Yeah, very cool. Want to switch a bit about like the human side of the company. So I mean to build out this product, probably not easy. How many people working with you in your company? Founders, employees, whatnot.

Speaker A: It's a team of five. It's a mix of full time, part time, contract and whatnot. We're a small team but we're early on. So after we raise There'd be, let's see, probably at least 12 people on the team. That's between engineering and account executives, general company executives. In an age of AI, I mean, we can leverage the same benefits of the tools that we're offering to our clients. So we can be lean as well. Whether it's agentic workflows or just different tools in general, we can accomplish a whole lot more. I have a fantastic engineer, director of engineering, and he guides the, really all the engineering and the uh, engineers coming on board and helps them understand what the purpose of everything being built is. And, and that's one opinion that I have that's very strong about this, is that people need to understand the user experience and the business case in order to program this correctly. In the past I've worked with software developers or engineers who didn't understand real estate. And there's just certain things that get overlooked because they don't. You know, I try my best to describe everything and this is why you have a business analyst. But some things get overlooked and it just doesn't make sense to the end user to build a successful product. You, you absolutely need to understand, uh, the industry and there's just so much nuance. So that's the approach that I take. I don't know if that answered the question.

Speaker B: Yeah, I think it does. Obviously, I mean it, it would just be seeing much better and the product is much closer to what you expect or what the user expect. Right. If the, if the engineer also understand the industry as well. So sounds like things are going well. Beside the fact that the website is not currently functioning at the moment, what are, uh, the challenges facing your team?

Speaker A: M. Well, this isn't a challenge so much. At least it's not a problem. The education portion. So helping people understand why good data hygiene is important, why you should take care of your data, have your documents properly organized, and how you can leverage that to get more out of your real estate properties. That's, that's a big conversation to be had. And then helping people understand how to use different AI tools and, and whether that's providing tutorials or onboarding and like guided instruction, just, just helping people understand the, the power of the tools in front of them, how to navigate it, how to leverage it, how to get the most out of it, and then how to keep coming back to it, because you can show them the platform once, but they need to make a habit of, of coming back to it. So that's, that's been sort of a challenge. Just the, the onboarding process and education. Then of course, like any company, it's when, when you're early stage, it's always a chicken and egg. You know, you want more clients, you want more publicity, you need more resources. You just go back and forth, you need more clients. Um, so you're always in this cycle of, of chasing the next thing and trying to use that as leverage and then obtaining that and then going after the, the next item. So that's, that's very much the case for us. You know, entrepreneur to entrepreneur. There's just the fact that you're always either fundraising or trying to close clients. And you're doing that. You're getting the clients so that you can fundraise or you're fundraising so that you can get the clients. And then in the mix of all that, you're also trying to get, make the product better, hire people, take care of legal and overhead, grow the business, understand regulation. Other trends like there's, there's always just a lot going on. And that's okay. That's, that's the exciting part of running a business. I don't know what else I would do with myself if I didn't have all this to worry about.

Speaker B: Yeah, I bet. I mean, sounds like, uh, some tough problem, but I'm sure that with time and with a very talented team, you can probably fix it. Just a couple more questions before we close out the episode. How, how do you see that, you know, rwa, uh, real world assets, uh, real estate tokenization will evolve in the future. What, What? See, I guess what's the end game that you're expecting here?

Speaker A: Well, I think that rwa, so real world assets, they already exist. And the tokenization of that is just going to be a continuation of how the assets are already invested into. They're attractive. People want them in portfolios. It won't matter if it's tokenized RWA or just RWA in the traditional sense. These are reliable assets. And we're talking about real estate, energy infrastructure, commodities. People have always been drawn to them. Rare earth metals like gold and silver, those are all RWAs. So once you tokenize that it's, it will maintain being a cornerstone of a healthy portfolio. What might change is that people will allocate to these alternatives more, so long as they're more transparent and potentially liquid. And this is where the tokenized version of the RWA could be superior. Now, it comes with its trade off, right? We spent a lot of this conversation talking about data and Oracle. So if you tokenize gold, that might be really easy to pull in a spot price feed, you know, what's gold worth at this moment? And then that's correlated to the token for real estate. For a, uh, solar panel farm, a solar farm for, for other items, it's, it's much more challenging. Now if we can accomplish that to the point where we do have the flow of data, you've got an oracle in place and then that's connected to a token. If that makes price discovery and liquidity more accessible, then people will definitely gravitate towards that because right now you don't hold too much real estate because, you know, it's illiquid. But if tokenized real estate is more liquid, people will invest into that more. And think about the compounding effects of that more money into real estate. Well, real estate is the built environment. This is where people live, work, play. It is your skylines. More money going into that means more renovations, more developments, bigger cities, uh, changing skylines. So it really has a tangible effect when you can open up this asset class and make it more attractive to investors. There's so much of our, of our cities that are in shambles and could use renovation or improvements, or there's a housing shortage. Let's build more buildings. That needs to be attractive to capital. So by tokenizing it, you could actually make it more attractive, which means that you could develop that, which means you give people a place to live. It's very interesting how that all plays out, out. So tokenization in this particular asset class is very opportunistic. We just need to reach that point. We need to de risk it. I, I share finc's version. A lot of things are going to be tokenized. The benefits are there. You're creating a higher functioning digital instrument, you know, versus paper or PDF. It just makes sense to, to circulate that and socialize that and have people trade that. You could see democratization where as efficiencies increase, the ability to bring in new investors or at a lower check size increases. Right. More capability to do that. Yeah, it's very exciting, but we can't put the cart before the horse. Fundamentals first. And in real estate. Start with the data, please.

Speaker B: Makes sense. Cool. Well, sounds like a lot of different small pieces, right. With this big puzzle. But then once eventually everything's come together, then yeah. Uh, so like the whole system will work very smoothly. I guess this would probably be at least like few years out. Right. It's not going to be anytime soon.

Speaker A: It needs more time and early efforts to do this. Some of them have failed. Dramatically. So when I said, you know, start with the data, please. Because sometimes what happens is if you jump into tokenizing real estate and you sell it around the world, this, this just happens with the, with a Detroit portfolio. I won't name the company, but they had well over a hundred million dollars worth of tokenized real estate. And the underlying assets were in terrible condition. They were taking mortgages when they shouldn't have. They stopped collecting rent. Some of them were vacant. Investors started losing. The city sued them. Things just fell apart. It's. It's a really ugly case. And so if we start tokenizing real estate and getting in new investors selling this around the world, people are using it in their portfolios and as collateral, and you skip all the fundamentals, well, you might have just created a house of cards and all it takes is a gust of wind to take out the bottom card and the whole thing collapses and a lot of people lose a lot of money. So that's why I say we need to focus on the foundation first and make sure that's secured before we jump into making, you know, tokenizing this and bringing in billions of dollars. Because that can be really consequential. Consequential. Yeah.

Speaker B: Makes sense. Yeah. I need to begin with the strong fundamentals. Yeah. First if I'm bringing in. Yeah, cool. And, um, do you have any advice for people who want to build their own company?

Speaker A: Start with lesson. Yeah. Start with the problem. Identify a really good problem. And legacy industries have a lot of problems, you know, that, that can be solved. And if you can work in a new technology like blockchain or AI and start, start there. So start with the problem and just see if it can be addressed by one of the solutions out there. Build your company around that versus jumping into chat GBT and saying, wow, this is so awesome. I'm going to try to sell this, this to real estate or to healthcare, whatever. Don't start with the solution because you need to ensure that you're addressing a real pain point that people need to solve. And you also have to understand the pain point from the perspective of the industry. You're probably not the first person who has said, let's fix this. Let's. Let's go into that industry and figure out why it hasn't been fixed yet. And so when you start with that problem, when you start with the legacy perspective, you just have a superior expertise that tells you why the problem exists, what it would really take to solve it. And then you can see, maybe blockchain does work or it actually would not work because of this reason. So start there. And I know that takes a little bit more time, it takes a lot of research, but really become an expert and identify those problems, figure out why the problems haven't been solved because you're not the first one to come across it. And then look for new tech, new solutions out there that you could apply on top of that.

Speaker B: Makes sense. I actually, I like your approach. Yeah, hopefully, uh, I don't think many people use it this day, but I think those are the one. That's why, you know, a lot of the new companies that have failed, if they don't begin someone just like trying to fit the technology in with the, you know, trying to fit what is it square back to a rahole.

Speaker A: Yes, exactly. Blockchain does that all the time. AI is doing that a lot. And uh, unfortunately some of these companies are receiving too much funding, to be honest. People are saying, I mean this happened with Blockchain. People said, you're gonna book your Uber on the blockchain. Yeah, you know, you know, you're, it's, it's a new dog walking app where you pay on the blockchain. Like people just started applying technology and the same thing is happening with AI where they're using AI to fix problems that people don't have or not understanding it. And again, this is why I wanted to get our solution out to market really quickly because I saw people building AI tools for real estate and the tools are garbage, to be honest. Like they're, they're a walking lawsuit dragging. When someone says, hey look, we, we have a new chat GPT wrapper. Just drag all of your files into our platform, into our website, all of your confidential information and then we're going to give you legal advice and accounting advice and financial models. They're going to be in a lot of trouble when someone makes a bad decision off of that, you know, and then comes in and sues your company. You really need to under understand this. And yeah, I'm glad people are excited about the technology, but it's not the best approach.

Speaker B: Yeah, totally agree on that. One cool. And final question. Where can people follow you or your final book or your company? Feel free to, you know, share any public handles or username.

Speaker A: Well, first, I'm Most active on LinkedIn. You can find me me, Matthew Schneider on LinkedIn. Connect with me. They're happy to get involved and um, you know, let's, let's chat, let's do business outside of that most of the time, just not this week. You can find our website building.inc as an incorporated. We've got a lot of great resources on there. We've also got a podcast, so if you like listening to podcasts and you wanted to dive more into the property side of things, code and concrete available on YouTube, Spotify, Apple, all the usual spots.

Speaker B: So.

Speaker A: And I might be at an event, so if you see me going to any global event and you're going as well, say hello. Be really good to connect in person.

Speaker B: Awesome. Yeah, I'll make sure to put all those links in the show notes here so that the, uh, listener or the viewers can follow along and connect with you in your company. Cool. Once again, Matthew, it's been a real pleasure having you on the show today talking about your company building Dot Ink. Well, hopefully next time you know when people listen to this. So hopefully the website will be alive and wish you the best of luck going forward as well.

Speaker A: I appreciate that. Thank you so much for having me.

Speaker B: Pleasure. And we back to our studio. What do you think about Matthew and his startup Building Inc? I think Matthew brings a rare mix of experience across real estate, proptech, capital markets, finance, blockchain and AI. Uh, what stood out the most to me is that he's not trying to force real estate fully on chain. His approach is more practical. Start with the real problem. It's messy, outdated and haptic. Trust real estate data One key takeaway for me was that real estate tokenization will not work unless the underlying data is reliable first. I like his point that his startup is mainly solving a Web2 real estate problem first with using Web3 as the trust and provenance layer. What was your favorite moment from this episode? Please leave a comment below. If you enjoyed this episode, please leave a like and subscribe as well. It means a lot to us. Thank you for watching and see you next time.