Episode 63 of the Public Key podcast is here! We have another packed house on the podcast as Chainalysis welcomes the newly acquired Transpose team, Alexander Langshur (Co-Founder & CEO, Transpose), Michael Calvey (Co-Founder & COO, Transpose), and Harry Mellsop (Chief Technology Officer, Transpose) to discuss all things blockchain data and how they are using it to become the foundation on which every web3 business is built.
Public Key Episode 63 preview: How to turn raw blockchain data and make it useable for the industry
With blockchain data becoming the apex of most decentralized and centralized protocols and platforms, the Chainalysis team is extremely excited to invite to the podcast and the company Alexander Langshur, Michael Calvey, and Harry Mellsop of Transpose.
The panel discusses the importance and the drawbacks of vast amounts of blockchain data and how Transpose was able to make these massive data sets usable for developers in the web3 ecosystem.
The team describes use cases of their on-chain data collection for tax, compliance, and cryptocurrency investigations purposes and highlights the emerging trends that they are seeing in their real-time crypto data.
The Transpose team walks through the journey of building a company amidst a downturn in the market and the decision to join Chainalysis to accelerate the scalability of their data.
Quote of the episode
“We consolidate this wide array of protocols in any of these given categories into one unified format. So a developer at the other end can go, ‘Well look, fundamentally these three protocols, they’re all dex protocols, they’re just exchanges, let’s display them, let’s communicate them in exactly the same way” – Harry Mellsop (Chief Technology Officer, Transpose)
Minute-by-minute episode breakdown
- (2:25) – The founding team shares their experience in A.I., derivative financial products, and self-driving cars and how Transpose was created
- (10:52) – Discussion on the importance of blockchain data infrastructure and how Transpose provides a solution for the crypto industry
- (18:45) – Solving the scalability challenge and how Transpose captures real-time blockchain data with the wide variety of blockchains
- (22:37) – The flexibility of utilizing on-chain data for tax, compliance, and other crypto investigations use cases
- (25:45) – Emerging trends in data, including stablecoins finding product market fit and NFT and ENS Domain usage
- (29:45) – The evolution of making blockchain data easier to read and what sets Transpose apart from other blockchain data dashboards
- (32:59) – Focusing on improving the UX of crypto for developers vs. customers
- (36:15) – The difficulties of building a company during crypto winter and the importance of perseverance as blockchain data founders
- (40:15) – The decision to be acquired by Chainalysis vs. continuing to build the company as a stand-alone business
Check out more resources provided by Chainalysis that perfectly complement this episode of the Public Key.
- Website: Transpose: Real-time cryptocurrency data. Reliable data infrastructure for companies and developers at any scale
- Report: The Chainalysis Guide to On-Chain User Segmentation for Crypto Exchanges
- Blog: The Chainalysis Guide to On-Chain User Segmentation for Cryptocurrency Exchanges Report Preview
- Chainalysis In Action: Israeli Authorities Disrupt Hezbollah and Iran Quds Force Terrorism Financing Crypto Infrastructure, Seize $1.7 Million
- Announcement: Unveiling Indexed On-Chain Lending Data in Transpose
- Blog: How Transpose is different to Dune
- Announcement: Welcoming Transpose to Chainalysis
- YouTube: Chainalysis YouTube page
- Twitter: Chainalysis Twitter: Building trust in blockchain
- TikTok: Building trust in blockchains among people, businesses, and governments.
- Telegram: Chainalysis on Telegram
Speakers on today’s episode
- Ian Andrews * Host * (Chief Marketing Officer, Chainalysis)
- Alexander Langshur (Co-Founder & CEO, Transpose)
- Michael Calvey (Co-Founder & COO, Transpose)
- Harry Mellsop (Chief Technology Officer, Transpose)
This website may contain links to third-party sites that are not under the control of Chainalysis, Inc. or its affiliates (collectively “Chainalysis”). Access to such information does not imply association with, endorsement of, approval of, or recommendation by Chainalysis of the site or its operators, and Chainalysis is not responsible for the products, services, or other content hosted therein.
Our podcasts are for informational purposes only, and are not intended to provide legal, tax, financial, or investment advice. Listeners should consult their own advisors before making these types of decisions. Chainalysis has no responsibility or liability for any decision made or any other acts or omissions in connection with your use of this material.
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Unless stated otherwise, reference to any specific product or entity does not constitute an endorsement or recommendation by Chainalysis. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent. Views and opinions expressed by Chainalysis employees are those of the employees and do not necessarily reflect the views of the company.
… to the end, I’ll kind of guide us into closing questions and make it obvious. But here we’ll go ahead and kick off. Hey everyone, welcome to another episode of Public Key. Excited to have a trio of folks on the podcast today. A full house. We’ve got the leadership team from Transpose. This is an acquisition that Chainalysis completed. Just a few weeks ago, the 30th of May, we announced to the world that Transpose is now part of the Chainalysis family. So I’m joined today by Alex Langshur, Michael Calvey, and Harry Mellsop. Gentlemen, welcome to the show.
Awesome. Thank you for having us. Excited to be here.
Thanks for having us on.
I’m excited to talk about Transpose. This has been a fun time as we were going through the acquisition process, getting to know your team, learning about the product and the capabilities and the customers you’ve been working with. There’s been a ton of moments where I’ve wanted to share this out publicly, drop a subtle sub-tweet or in a customer conversation, just kind of whisper to them, “Hey, what do you think about this market?” Of course I haven’t done that because you got to be in that confidential mode of deal making.
So this is really first opportunity of what will be many to come in the future where we get to share with the audience out there what Transpose is. So maybe Alex, let’s start with you as as CEO, tell us a little bit about your founding story and what led you to want to build this company and maybe work into that, a brief overview of what is Transpose, what do you guys do?
For sure, that sounds great. My background was very technical, so my whole life I would really describe myself as a bit of a tinker. Not really one to apply my technical knowledge to a big organization or work in large groups, but more just hack around at school with Harry. I really believed in that methodology all throughout school, lots of awesome projects. Built a close friend group of similar interests, studied AI, but had kind of a really wide array of interests of technical interests from systems AI. And a lot of that bled into crypto and finance especially.
So right out of college I got into traditional finance. There was this really exciting company called Deep Gray Research that was recruiting out of Stanford, a small company, 10, 20 people basically just building algorithms to crack future markets. So just a bunch of bright people, not a lot of red tape or bureaucracy. So I was super aligned with that. Moved to New York to pursue that. Had a great, great experience. And throughout that entire opportunity my eyes opened more and more to the world of crypto. I got involved from a technical angle, built out a lot of exciting tooling, some projects, did some NFT arbitrage stuff at one point, really was all over the place with the development angle.
And that gradually led to the idea of Transpose. Michael, I’ll let him introduce himself, but came from a very similar background in traditional finance and we shared a lot of ideals in the space, which led to us building out this NFT marketplace products that we called Nifty City back in the day. And eventually given our understanding of the lack of really solid data infrastructure in the space, we kind of pivoted and transitioned that product to what is now known as Transpose.
So we’ll dive deep into it, but Transpose from a bird’s eye view is a blockchain data platform with the sole purpose of making, developing on blockchain data, whether you’re interacting with the lowest level components, the raw blockchain data on chain or high level abstractions like who bought what NFT, who exchanged what token, who’s bridging this asset from this chain to that chain? Regardless of how complex the data you’re working with, the idea is you can use Transpose to build applications on top of that. So that’s what we’re here for.
I love that approach where you were attempting to build something completely different and ran into an even bigger problem than the one that maybe you started out to solve. And realized that that bigger problem was universal. That everybody in crypto was dealing with this right now and there just wasn’t a good solution out in market and you jumped on that opportunity. Now Harry, did you guys know each other at Stanford? Obviously Alex has got the Stanford hat on, you’re also a proud grad. Did you guys overlap there? What was the connection? Is that how you ended up as part of the Transpose team?
Yeah, I must have forgotten to bring my Stanford hat today. Yeah, we did know each other. Alex and I, we kind of lived together for a year actually before that was cut short by COVID. As he mentioned we kind of worked on a bunch of different projects together, probably more in the machine learning space than in the crypto space, although we did kind of dabble more in crypto towards the tail end of that. But we were part of a group of great friends who worked on a number of really exciting technical projects and we had a bit of fun as well.
That’s awesome. Now, I have to ask you, before you joined Transpose, you spent some time at Tesla working on the self-driving team. Is that thing ever going to be ready?
That’s a great question. I think we’re at this fascinating point in the evolution of self-driving vehicles where at a deep technical level, the competency is there and we’re now kind of fighting price trade-offs, the regulatory environment. There are some companies out there who have the technical stuff and they’re kind of racing to lower the price to a reasonable point. And then you’ve got companies more on the Tesla camp where they’re there on the scalability and affordability, but their fight is really making sure that they’re ready for prime time from a pure competence perspective.
Then of course, just like crypto, you’ve got a layer in the regulatory uncertainty there as well. But I don’t know, I think I’d cautiously bet that by the end of the decade the stage is probably set and we’ve got a few companies who are hitting on that sweet spot of capability and price and then it’s just a question of adoption.
I love it. I think as soon as it hits that sweet spot, we’re going to see widespread adoption. I keep telling my kids, you probably are not going to get a driver’s license because the cars are just going to drive themselves, but we’ll see. Michael, last but not least, so you were at Morgan Stanley prior to jumping in with Alex to create what became Transpose. Talk a little bit about your journey into crypto. Because u come at it from this traditional financial markets perspective.
Yeah, that’s a great question. So I feel like in hindsight, crypto is a very obvious place for me to end up. I’ve always been threading the needle between loving new technology and wanting to think about ways the world could be better and could be different, but also just being interested in finance and how the wheels actually spin for the world to make things happen. To have companies execute, to allow people to create things and interact and transact and everything. So my first job out of college was at Morgan Stanley. I was working on the structured products desk there in capital markets, basically putting together bespoke over the counter derivative products for Morgan Stanley clients.
And I was sitting on the product team there, which really sat at the intersection of sales, of structuring of the settlement and operations businesses and really was the middle point for all of that. Which meant that I had a sort of unique view in terms of the overall company into how traditional financial rails are actually set up. And I got to witness how a lot of that worked and see that there is so much that can be changed. That we are not at the final iteration for how value moves around for how people transact and interact and do all of that.
I initially very naively was hoping to change things at Morgan Stanley that obviously doesn’t work in practice like that. Morgan Stanley is a world-class organization. They’ve got many brilliant people who are working really hard to push a lot of progress across the board on this stuff. So crypto really seems like an almost inevitable place for me to jump to. So in terms of getting to know Alex and getting started on everything, I met Alex actually only a couple of months into my time in New York, which I think is a testament to how incredible this city is. And we instantly bonded over building things and started tinkering and very quickly things span out of control and we found ourselves leaving our jobs and going to go raise some money and then somehow convinced Harry to come join us and the rest of the team to come back.
There’s actually a funny bit of the story that hasn’t been told yet that Harry and Alex actually worked on the precursor to Nifty City together and I joined onto that originally way back in the day when it was still just a side project and a bunch of hacked together scripts and concept ideas. And then Alex and I ended up running with that for a while and finally had this beautiful full circle moment when Harry returned and we got everybody back together. And it was really quite funny because I’d never met Harry in person.
We’d only had a small handful of Zoom conversations and I was always like, “Oh my God, we’re finally going to get to have Harry.” And it was all we’d made it out to be. So it’s been awesome. I think the most incredible thing about what we’ve done here is putting together an awesome team and definitely want to make sure we give a shout-out to everybody else as well. It’s been an absolute pleasure working with everybody and we’re really just getting started. Now it’s time for us to supercharge and take things to the next level.
Exactly that’s why I’m so excited about bringing you all into the Chainalysis family now. I guess Al Alex, maybe we should talk a little bit more about what Transpose does and maybe we can illustrate with some customer examples of how people are actually using the technology today. I’d love to hear the perspective you had when you pivoted from Nifty City into Transpose. What was the core problem that you observed that you suspected everybody in crypto was going to struggle with that you could go out and fix?
It’s a great question Ian, and it’s an interesting problem as well because crypto data, blockchain data in general is fundamentally this public asset. Everyone has access to the entire history of every blockchain. Anyone could go to any Etherscan or any Blockchain Explorer and access the entire history of its contents through an archive node. But at the same time, the data is so difficult to work with in its raw format, so it’s a commodity, but the usability of that raw data is so low that it still requires companies to come in and translate it and transpose it into some more usable format.
So when Michael and I were building out Nifty City, the project before Transpose, we needed to build out an entire aspect of the application that would show a user’s NFTs they own, tokens they would own, entire transaction history, token transfer history, all these facets of what a wallet is doing, what their on-chain assets. And accessing that data generically since the Genesis block for a chain like Ethereum, especially when we started that project, was virtually impossible to do. It was so difficult to do. We strung together this Frankenstein solution that added so much collect complexity to our platform that we realized then and there that it could be its own product.
We saw all these small similarly sized companies around us struggling with the exact same issue of I’m building a product within a product by virtue of not having access to usable blockchain data and it doesn’t make any sense. So we started vending the data we were processing and outputting to all these companies around us. And then we realized that it was a business opportunity and we ran with it. So the simplest customer profile is effectively what we were. Think of an application that needs to provide a view of an individual wallet’s activity or an individual addresses activity. The simplest example being a wallet application.
So a wallet application could come to Transpose, pull data across a variety of our data sets, interrogate our data sets for exactly the data they need, basically go to our data sets and say, “Hey, I want to know the balance of this individual address across all tokens on Ethereum or all NFTs on Ethereum.” Or how about the USD price of a addresses most recent NFT sale? Or what volume has that address done on Major DEXs like Uniswap or SushiSwap or curve? All those data sets are available on Transpose and it’s up to the application developer to decide how they’re going to leverage Transpose to solve their business problem.
So the wallet application is a very obvious one, but really the world, the universe of applications is expansive and quite endless. So you think of a tax and compliance company that needs to render a view of the tax overhead of an individual address in the last fiscal year. That’s something you could do with Transpose. You think of analytics and trying to build out a snapshot of TVL or value locked on chain over time. That’s something you could do with aggregations on Transpose. Really the universe of use cases as is endless. And so we spend a lot of time exploring the boundaries of how companies can build on top of us.
And this is an area where I think more and more companies are coming into the space and they got a great product idea and they’re kind of hitting a wall when it goes to this data collection. Yeah, absolutely, it’s super easy to in theory, stand up your own node and all of a sudden you’re on the network and you can get an archival copy of all the transactions ever done. But in practice that’s pretty hard to do. And then even once you’ve done that, you’re still meaningful amount of work away from turning that data into something that reliably produces answers to the questions you just posed, Alex. So Harry, maybe shifting gears to you, I’m really curious about some of the big technical challenges that you encountered as you were building the product. Where were the big speed bumps along the way? So we saw the vision now executing on it, what made this hard to do? Why were you all one of the first to really go and tackle this?
That’s a great question and I think there are probably thousands of very reasonable answers to that. One that we think about a lot is solving the scalability challenge. And scalability manifests in a couple of different ways. One obvious one is thinking about data size and data throughput that goes through our systems. We are indexing every piece of activity more or less that’s happening on the blockchains that we support. And particularly with this trend of L2s and other chains that have higher block rates and higher transaction throughputs, that data can be incredibly expansive. And one of the key parts of our value proposition is that we capture everything and we capture it in real time.
We tend to be, these days, somewhere between one or two seconds behind the live block for our lowest level data. And so we really need to think a lot about how we scale those data ingestion services, how do we read data that quickly. Can we keep up, especially some of these chains with variable block rates? When things are really taking off when there’s incredible amounts of trade volume happening. In the bull case, we really get put to the test. And so we’re thinking a lot about that and we’re sort of dealing with thousands of gigabytes of data, every single day that come through. We don’t store all of that, but we’re having to process it and make decisions about it.
There’s also scalability in the number of people using Transpose. And this comes in a couple of ways as well. One of the most powerful aspects of our tools is that people can write custom queries that run on our infrastructure. And as our data grows and as the number of users grow and as the complexity grows of what people are trying to do with us as we kind of solve this communication problem, the complexity of dealing with that kind of utilization problem grows really, really rapidly. We’ve had many months this year where we’re growing 40% month over month in certain metrics and servicing that is really, really truly challenging.
When you’re at that kind of exponential growth rate assumptions that you make one month suddenly may not hold at all even two months later. It’s just so difficult to reason about that kind of change. I think this really comes back to our sort of fundamental reason to be. Is we want to be the foundation on which every Web3 business is built and to do that we have to cope with and become symbiotic with the success of the ecosystem. And with that comes scalability.
I think one of the things that struck me the first time I signed up for an account and started using the product was you’ve actually got almost two competing problems on the user data consumption side. You’ve got this exploratory SQL interface, so anybody that can write SQL can type in a query and it could be a really simple one like look up this single piece of data well indexed in your existing table structure or it could be a bananas query that’s like join across these data sets with some really hard filter. Anybody that’s ever done any sort of database design or played with a database backend before, the wrong query can sync your system performance terribly.
So you’ve got that challenge to architect around and kind of manage, “Hey, we want an open-ended flexible experience for everybody. It’s also got to be performant.” But then you’ve also got this layer where I can take that SQL query and I can publish as an API endpoint, so now I want that data, that really hard query that maybe you squeezed it through the performance queues and you got the answer out. Okay, now I want that any time from a rest end point that an application or potentially tens of thousands, hundreds of thousands of consumers, a wallet for example. Anytime they open the wallet is now hitting that endpoint querying for data. And that’s a totally different performance profile and challenge to manage through. And so, I can imagine just when you describe scalability, this was kind of a Herculean task to start to architect around.
Yeah, that’s exactly right. And the key premise behind the solution there is thinking about being elastic and horizontally scalable at every point throughout our pipeline. And so, that’s when data comes off the nodes can we make sure that those nodes are scalable? When they’re going through the pipeline, the pipeline of ingestion into our databases, can we make that scalable? The databases themselves, the infrastructure that sits on top of that at every step, we’ve put so much time into making sure that those things can spin up and spin down in a matter of seconds if they need to. Because it’s so important to us to be able to solve exactly what you’re describing right there.
We want people to be able to throw whatever they can think of at us, and we want to be able to handle that. Particularly as we’re growing, particularly as we’re learning from our customers. And we might think one thing about how our customer might use our product and they actually want to use it in a different way. We want to be able to flexibly move with them. And when you’re talking about, I mentioned how many thousands of gigabytes of data flow through the system every day and we’ve got so many terabytes of data that sit there at rest. Being able to make all of that horizontally scalable and maneuverable requires a lot of advanced planning. So that’s really something that we’ve been very big on.
Awesome. We’re going to get some excited customers and listeners who are going to come out and say, “All right, let’s really put that scale to the test.” So I’m excited for that challenge. Michael, a moment ago, Alex mentioned this tax use case. In the U.S. we had tax season about two months ago. It was nerve wracking for, I think, a lot of people in the crypto community as you’re trying to figure out, “What do I actually owe?” And, “Where did I earn money from? At an individual basis it’s complex. I can’t even imagine trying to file taxes if you’re a business holding crypto on your balance sheet, which people are increasingly doing.
And as soon as you get into more exotic things like protocol staking or if you’ve sent funds from a wallet through a Dex or you’ve been a liquidity provider, now you have really exotic calculations. I’m kind of curious, does the system capture that type of activity where you could actually build a tax gain calculation on the platform? Is this an area, a use case that you all explored at all?
Yeah, this is an incredibly interesting question and one that I could talk about all day. One of the fascinating things about the flexibility, you mentioned Ian with Harry a moment ago. The flexibility to serve this wide range of use cases is that we can solve these incredibly tangible problems that businesses and people today face every single day. And like you mentioned, tax season just passed. This is something that a bunch of us on the team faced and went through in a personal way. So we’ve experienced these troubles and know how difficult it can be.
The tax use case especially is really exciting to us because there’s this distinction in tracking what happens on chain where kind of this simplistic view is we’re going to track every transaction and know that this is doing this low level operation, whatever. That is fairly useful already. Even getting to a comprehensive view of that takes some work. What we’ve done is taken that a step further and honestly living by the Chainalysis mission of every chain, every interaction, and taking things further than just the transaction level to understand what’s happening, what are people actually doing? We’ve been putting in a lot of that foundational work already and we’ve been doing this since day one.
So what that means is that we can now couple this with the expertise that is here from amazing people like Roger Brown. I know you guys had him on earlier. We’ve been working with him closely already to try to start tackling some of these challenges. But again, with the breadth of what we can do here, this extends beyond just tax calculation. There are so many applications for this data. We’ve only scratched the surface of what’s actually possible here. One angle that we’ve looked at a little bit in the past and we’re really excited to dive deeper into is things like rug pulls and compromises and hacks.
When the FTX situation was going down, I remember very vividly one Friday night when all of a sudden the funds started moving, we all gathered at the office, I think it was like 2:00 AM and a couple of us had been at a concert before and we gathered back and we were just sitting there glued to our screens watching everything unfold in real time in front of us. And this is before it was breaking on Twitter, before you could go read an analysis and see what was happening. And that was incredible. But we aren’t professional investigators. We aren’t professional researchers doing a lot of this. That’s one of the awesome synergies I’m super excited for with this integration. Is that there is that world-class talent at Chainalysis already and people who know more about what’s happening and how to dig into all of the data than anybody on the planet. To put that together with the data that we’ve started to put together is going to be really special. So super excited for that. A lot that’s going to be coming that I’m sure our listeners are going to be excited about here.
I know Kim Grower, who’s a frequent guest and my occasional co-host on the show has been number one fan since we announced the acquisition. She’s excited to get access to Transpose and start using that to augment a lot of the research work that her team does. So expect to see that coming out and in research we publish soon. I’m also kind of curious, like you mentioned that FTX watching session, I have to imagine that you’ve got access to a fairly unique data set here inside of a really powerful platform. What are some of the other trends that maybe you’ve seen in the data that people might find interesting, like ENS domains as an example, or NFT trading volumes or smart contract behaviors that you’ve seen emerge over the last year and a half? Anything spring to mind in those areas?
I mean the main thing I think here is really the high level point that even when things were getting pretty bad, underlining everything was the fact that there was still this steady use from the things that are actually delivering more value to people. Whether that’s identity based things like ENS, which has just been constantly active for a while, or key NFT projects that people care about, key DeFi protocols. Stable coins is one where actually it feels like they’ve all of a sudden found product market fit and we’re seeing increased Stablecoin usage just week over week the way you would with a SaaS business, not the way you would with some gambling, whatever casino situation, which is really what things in DeFi were looking back.
So I suppose that from the viewpoint that we’ve had, things have been more optimistic than a lot of the doom and gloom that we’ve had. I feel like that’s starting to become more accepted broadly. Now things are getting better, it feels like a little bit, and it’s less existential. There’s still obviously a lot for us as an ecosystem to figure out around regulation and all of that. I’m hoping that we can help now that we’re Chainalysis and really move the needle on that front. But really that there’s a lot more to be optimistic about than to be fearful of, I think.
I love optimism on this show. It’s one of my favorite perspectives. So thank you for bringing that to this episode. Alex, I’m interested in a blog post that you wrote. We’ll link to it in the show notes. I think a lot of people are probably imagining, “Okay, Transpose. It collects all this data. It’s got a database, front end, powerful SQL query interface. We’ve got an API, we can publish those queries too, so I can consume it as a rest endpoint into my application.”
They’re now imagining a product category and there’s definitely some other things out there that are in kind of an adjacent space. I’m wondering if you can help folks now draw a little bit of a contrast. Ones that maybe come to mind is Dune that’s pretty popular from a community standpoint, kind of a open source free to the people sort of approach as a dashboarding tool. I imagine there’s a couple others that you guys pay attention to in the competitive landscape. Just walk us through that market scape.
For sure. I mentioned this earlier, but blockchain data is a massive commodity and we’re talking about an industry where you can do anything without having read capability. And as I mentioned earlier, the data in its raw format is incredibly difficult to work with. And so over time, the entire history of blockchain has been riddled with new technologies propping up to better read that data. Way back in the day, I’m thinking a couple years after Ethereum was originally founded, we saw the advent of remote node providers, people saying, “Oh, we’ll host nodes for you making it easier to read data.”
Over time technologies like the graph appeared that allowed it, that made it much easier for individuals to parse protocol layer activity and basically build their own views of blockchain. And now we’re getting to a point where companies like Transpose, Dune and Flipside are coming in and making it substantially easier to work with this data, really exploring the end goal of providing every angle, every perspective of every piece of activity on chain so you don’t have to do any of that work. And as you can imagine, just like the node provider community and several technologies that came after that big proliferation, there are many companies that are working within this blockchain indexing space.
So you mentioned Dune and Flipside, the list goes on. Fundamentally, these companies are doing amazing, amazing things. What I think sets Transpose apart from companies, other companies that people may be familiar with are two things. First, the data. At Transpose, we kind of refer to this aspect of the business as ingress. So all the flow of data that goes into Transpose, all the indexing work we do, all the processing on top of that data, we call it all ingress. And in our efforts, in our ingress-related efforts, we kind of have three high level goals, standardization, completeness, and data freshness.
So standardization means how can we make it so that every single piece of activity outputted by all 30 major NFT marketplaces, for example, on Ethereum, all follow the exact same scheme of the exact same model. So people can interact with that data in a very standardized way. Completeness means just having every single major protocol on Ethereum or other chains in our database. So every single NFT marketplace, every single Dex, bridge protocol, lending protocol, staking protocol, you name it, Transpose needs to have it in our database. So completeness is incredibly important.
And then lastly, freshness. So we need our data to be fresh, we need it to be real time. The second nodes get it, we want to have it as well. So we’ve built out this ingestion pipeline that Harry alluded to that is really, really quick. The second a node picks it up, we talk to many node providers and we ingest it into our systems. We pipe it through this pretty complex pipeline to take it from this raw format on chain into something that end users that interface with Transpose can leverage in their applications without much technical knowhow.
So the ingress aspect sets us apart and that there are very few other players out there that have spent as much time focusing on having that data super, super live and super, super standardized and super, super complete. They’re fighting words, but I’ll confidently say that, that Transpose is the best in those three categories. And the other half of the equation is what we call egress. So ingress is everything that comes into Transpose and egress is everything that goes out. Really answering one simple question, how do customers interface with Transpose? And to do that, we provide a whole number of solutions that people may use to pull data ranging from rest endpoints, our SQL API, these custom deployed endpoints that you alluded to, Ian. Dedicated infrastructure and instances, web hosts and web sockets, this whole universe of tooling that customers may use to use Transpose.
I love that framing, particularly on the data completeness because that’s been such a philosophy of ours at Chainalysis. As soon as you’re building for use cases that people depend on, because they’re making money or they’re protecting customers or they’re chasing down bad guys, it becomes so much more critical that the data is right. Whereas in a free community dashboard that allows me to track trading volume and PepeCoin, if I’m off by 10% it, nobody’s really all that upset about it. And so the contrast, when it gets important is really, really high between the two architectures. And it’s not easy to shortcut your way. You can’t bolt on completeness after you’ve already deployed the product and it’s in market. You’ve got to be building for that.
So that framing is terrific. I’m curious, I’m going to jump to Harry here because I want to talk a little bit about the second part of what Alex just laid out, the egress side of it. Because one of the first features that I fell in love with when I was playing with Transpose was the query assistant, because I’ll admit it, I’m not a SQL wizard. I can do a select star query and I know drop table as well. So generally people do not ever give me DBA access. And I think this is actually one of the hardest things in crypto right now, by the way, is just UI/UX across every layer, whether it’s in a wallet or it’s on a marketplace or a dex or it’s at the layer that we’re talking about here in the data collection side. So maybe Harry, walk us through some of the features that you all have built in and talk a little bit about the query assistant that I mentioned as well to make that egress side awesome.
Definitely. I think that this is an important point that we’ve spent a lot of time considering too. And I think none of us are strangers to the fact that crypto as an ecosystem you suffers from the UX problems that you’re talking about. And I think a lot of companies spend a lot of time thinking about how can we improve the UX of crypto and crypto powered products for consumers. And we’ve taken a slightly different angle. And our angle is how can we improve the UX of crypto for developers? And in that we sort of try and leverage ourselves a little bit more. If we can make it accessible to a wider array of developers who maybe aren’t even as experienced in crypto. Obviously we’ve got some customers who are super deep in the technical weeds, but we also want to open ourselves up to customers who maybe aren’t and maybe just only understand the higher level concepts, but they should have just as much access to the tooling and data as anyone else.
And then those people then go forth and bring that to wider communities again. So this is definitely something that we’ve thought a lot about. And what you and Alex were just talking about with data standardization, that is one area that we’ve really focused on for exactly that purpose. So if you take an example, for example, Dex protocols or bridging protocols, anything like that, we support a really wide array of protocols that at the implementation level, if you were an EVM purist, you’d go, “Well, this protocol is actually completely different to this protocol. They work in completely different ways.” But our approach to it is that’s absolutely true, and where it matters will give you the data about the differences, but in many cases those are implementation details that the downstream user shouldn’t need to actually worry about.
And so we consolidate this wide array of protocols in any of these given categories into one unified format. So a developer at the other end can go, “Well look, fundamentally these three protocols, they’re all dex protocols, they’re just exchanges, let’s display them, let’s communicate them in exactly the same way.” And in a similar light, the query assistant, I’m so glad that people are as fired up about the query assistant as I am. It’s got a hilarious origin story. It was a Sunday morning kind of get out of bed and go, “well, what if we just threw ChatGPT at this thing?” And that was the very first stab, and it’s come a really long way since then.
But the basis of the idea is in exactly the same place. And that’s many people out there, and I’ll be the first to admit, I’m not a SQL expert either. I’m an engineer, I spend a huge amount of my time writing code, but there are many engineers out there like me who specialize in other sorts of areas and not necessarily SQL and who might just really want to contribute to building on Transpose, to building products in the crypto ecosystem. And that’s kind of where the thought is. It’s engineers who specialize in other areas who want to be able to get access to our data in exactly the same way, providing a way to do that.
And we’ve got really grand plans for the query assistant. We want it to do a lot more than just what it is today where we’re going to try and rebrand it to just be the Transpose assistant. We want it to be able to do everything. We want to be able to directly answer questions for you. We want it to help you explore our documentation. We want it to write better queries. We want it to help you write the integrations. And really we want the sort of integration of Transpose for any developer to be effortless with zero information asymmetry. And in doing so, we want to hit on these same themes that so many great companies in the space have, which is just bring this to a wider audience.
That’s outstanding. I’ve actually noticed that Query Assistant seems to have gotten more capable in just a handful of months that I’ve been playing with it, where I occasionally pop in and try and throw a weirdly structured question at it, and it seems to be handling the miss-structure better and better every time I go after it. So yeah, excited for those improvements for sure. Michael, I’m curious about something. I think your role at Transpose, all of you wore many hats, obviously not just the literal ones that you’ve got on your heads right now. But I think you were primary on the kind of customer acquisition side and we have a lot of founders on the show and a lot of people who listen who are out there building right now in crypto.
You all founded the company a year and a half ago. So right as we passed the peak, maybe, of the last bull run. And you’ve been successfully building in a down market, which I think everybody in crypto right now is aspiring to do. What’s been your experience building the company through the down market? How do you find success when asset prices are going down? And any tips or suggestions you would give to other people out there in a similar situation?
Yeah, this is a great one. There’s just been so much learned and so many takeaways. I actually have a formal list I’ve been keeping that I might put out at some point on just like what to do, what not to do, what things we realized after the fact we should have focused on. A whole bunch of mistakes were made that led to honestly much bigger costs than we should have had in certain areas, or not seizing opportunities as early as we could have. But really, to boil things down to really one most important takeaway is that we discovered a lot of the problems that we ended up solving firsthand. We wouldn’t have gotten to where we are if we hadn’t actually gone and experienced these problems ourselves. And to do that, we had to honestly leave great jobs to try to build something crazy.
We had an idealistic vision for where we wanted to go. To think that we left our jobs to go build that and to go work on that is, I wouldn’t say embarrassing because I’m incredibly proud of what we’ve done, but it’s like shocking. I’m like, “What were we thinking?” Just not thought through at all. But if we hadn’t taken that leap and started and just started somewhere, we never would’ve even had the opportunity to learn the problems that people face. And first we experienced those problems ourselves with trying to extract realtime data, trying to get structured views of what was happening on chain.
And then we realized we were going through Alliance Accelerator program and we realized that every other founder was facing the same problems. So really the most important piece of advice is just to get started. There were certainly, after that, a lot of difficult parts of the process. A lot of times when things were harder than I had realized they ever could get. A couple of times to call out maybe or one during our pivot when we were switching from doing the metaverse thing to becoming a B2B SaaS infrastructure company. That was very existential. All of a sudden we were doubting, for a little bit, whether it was the right decision to cart off.
We were like, “You know what, we’re going to have to rethink everything.” But we ended up realizing that we had learnings along the way that were incredibly valuable. Then after that, once we’d actually launched and built an initial MVP, we knew this was a problem that a lot of people faced, but the users didn’t just show up. People didn’t start using our product. And this was one of the first real product learnings that we had. Was that you can create a great product that is generalized and can solve any problem for people, but that’s not very helpful. You have to actually create real solutions for people and you have to deliver them in a way that people can understand and can incorporate.
And this is something I think that Chainalysis does incredibly well and that we’ve looked up to Chainalysis about since well before starting talks on all this acquisition stuff, is just doing that. And honestly, it was the jump from being a generalized API to really focusing on what we focus today that got us product market fit. It was that key switch. And it wasn’t like we suddenly gained product market fit and then since then we’ve had it. There was a continuous process of creating things, testing ideas, talking to customers, understanding what problems people are facing and just dialing in and getting closer and closer and closer.
And as we’ve gone through that process, we’ve gone from people balking at spending a hundred dollars a month a Transpose, to now having incredible enterprise contracts where we deliver so much value to organizations. So that, I think, was difficult, but ended up becoming a great learning. As part of that, being told that we were wrong when we started off working on this stuff, people were like, “What are you guys doing? Nobody needs this. This isn’t something… People do it already a different way. They’ve solved these problems in a much more complicated, expensive way. No one’s going to need what you guys are doing.”
And it really took actually doing it to know that actually we were right. And we’re not the only ones. Other people have come to this realization too. So that part is very gratifying. And then one other point on the building side of things is that it was a lot more work than we expected to be honest. Getting those first few customers, this sounds kind of stupid to say, but it took going out there and manual conversations with each individual first customer and getting to know people, getting to understand them. Again, I was expecting to just put out a great product and to have people flood through the gates. And when that didn’t happen, it was disappointing, but it was a great lesson. I ended up figuring out how to have that capability.
And there’s me maybe a couple of points I’d call out on the acquisition side of things that also were interesting. Like Ian, throughout the process, there were a lot of times I just wanted to share so much with the world, but obviously we couldn’t, for confidentiality reasons. But I mean the regulatory landscape was not built for small businesses. And there were a lot of hoops that we had to jump through that I think neither side wanted to have to go through that made things take a lot longer, again, than we expected. I remember when we started the process, we spoke with an advisor and we were like, “Oh, this is great. By the end of the year, we’re going to get this wrapped up and we’re going to be getting back to work and getting building.”
And it ended up taking almost half a year of going back and forth and figuring things out. And then when we thought we were done and we’d agreed on everything actually, well, then there’s a whole nother set of regulatory hurdles to get through. And that was, I wouldn’t say demoralizing, but it was certainly a learning experience to realize how much more work goes into this process. I definitely respect people who’ve made it further along a lot more. Not that I didn’t before, I’ve always had admiration for entrepreneurs and people that create things. But now I’ve realized just how much it takes and how much putting up with BS that no single person wants to be there. You have to go through to actually make it. So much has been learned. I don’t know, I probably feel like I should turn this into a blog post at some point.
This absolutely deserves a blog post. There’s some real gold in that content. I think the message about just getting started and then I think a lot of people hear product-led growth and imagine it is exactly what you described, build a great product and then magically people start using it and the business just works. And it’s never that simple in practice. Alex, I’m curious, as we kind of wind down the discussion, talk to us a little bit about your decision to join forces with Chainalysis, because I know this probably… I would have to imagine as a founder with mixed emotion. The opportunity is a great one. We’re obviously super excited you’re here. But the alternative of continuing to build the company standalone, you had momentum, you had a good amount of capital, lot of opportunity in front of you. How do you make that decision? What’s the calculus behind it?
That’s a great question. To your point, at the end of the day, it’s a trade off. Do we continue independently in the face of our competitors who we’ve gotten very familiar with. Raise a Series A, raising money in a bear market is never fun. Put our heads down, continue to keep our heads down and drive hard for another couple years. Build incumbency ourselves, or do we put ego aside in a way, leverage the platform and really significant moat of a company like Chainalysis to massively accelerate our timeline. Doing so, it allow us to lock down big enterprises, augment our data with proprietary data sets, leverage a consortium of teams and their experience, stuff that we have no clue about, and quickly pursue this dream of being the go-to developer solution for blockchain data.
In the end, we realized that the desire to grow faster and win this market outweighed the opportunity to do so independently as Transpose. We kind of put our ego aside, we said, “You know what, this is just such an exciting opportunity.” We were so stoked. We just want to see Transpose flourish. We know there’s an opportunity here. We know we built the best tech. We just need the platform and the platform to do it, and we couldn’t be happier about that decision. So Transpose and Chainalysis compliment each other incredibly well, and it’s only up from here.
That’s right. Big things to come in the future. Last question, a lot of people listening, I’m sure, are wondering like, “Hey, the product sounds amazing. How do I try it out? Where is it? I want to get my hands on it.” The best way to get started?
Yeah, anyone can jump on for free, app.transpose.io. You can make a free account and get started, and please do.
Amazing. Gentlemen, thanks so much for joining me on the podcast and for making the decision to join Chainalysis. Big things to Come in the future.
Thanks very much for having us, Ian. It’s been a pleasure.