Building Verifiable Onchain Data for Institutions with Edge & Node CEO Rodrigo Coelho

GM, GM. Welcome to what's going to be another fantastic episode of DevNTell. So if you didn't know, DevNTell is a 30-minute podcast held every week allowing founders, hackers, and anyone in between the opportunity to come on the show and showcase what they built. And today, I'm ecstatic to welcome Rodrigo Coelho, who is the CEO of Edge & Node. So if you don't know, Edge & Node is the company behind The Graph, and they are building Amp, which is infrastructure that transforms blockchain activity into structured, verifiable data sets queryable by SQL. So if you stick around for today's episode, you'll get to meet Rodrigo, see a live demo of Amp, and learn how you can get started using it today.

All right, let's get into it. GM, GM. Welcome to the show, Rodrigo. I'm ecstatic to have you on today, man.

GM, GM. Happy to be here.

Excellent, excellent. Yeah, I'm really excited for our audience to get a chance to see you go over kind of the origin story of Edge & Node. I know you guys got really popular for The Graph. My actual first crypto conference was Graph Day, I think back in 2022.

No way, you were there? Wow.

I was there, yeah. It was actually so good that I haven't been to another crypto conference since.

We set the bar so high. We cannot be topped.

That's right. I set the bar, yeah. I didn't want to experience anything else.

Oh, that's so cool. That was a small venue. It was like a theater, and yeah, it wasn't a lot of people. So that's cool you were there. Wow.

Yeah, yeah, yeah, participated in the hackathon.

Was it '18 or '19?

I think it was '22, '22 or '23. I was there, but yeah, participated in the hackathon and won some prize money there. Yeah, it was a good time.

Oh, you went to the one at the Palace of Fine Arts, you mean?

That's the one, yeah.

Oh, okay. Yeah, there was a prior one that was even smaller than that, like the original one. But yeah, the Palace of Fine Arts, that was amazing. Yeah, that was like a really big venue, and yeah, the hackathon.

It was a great time.

Oh, amazing. Cool.

Yeah, but yeah, that that kind of aside, it'd be awesome if you just wanted to give an introduction about yourself before we get into all the great stuff you have prepared.

Yeah. Well, a little bit about me. I've been a tech serial entrepreneur for a couple of decades or more and started, you know, early days of the internet, kind of the dot-com boom, founded, you know, a few tech companies. And around 2016 era started to get interested in crypto. And out of kind of right place, right time, I was working at a co-working space in San Francisco and happened to become friends with Yaniv Tal, who is like the original founder of The Graph. And he was actually working on the pitch deck for The Graph at that time, and I was kind of fellow entrepreneur, kind of like, 'Hey, what are you working on?' He had like an Ethereum sweatshirt on, so we started talking. So he showed me the pitch deck, and I saw it was a really big idea and just wanted to be helpful as a fellow entrepreneur and friend. And so made introductions to investors. And then, yeah, a few months later, I was kind of looking for the next thing to do. And the timing was such that they had just gotten their first check in, and so he'd asked me to come join the team. So I ended up being the first hire back in 2018. So I was there from the very beginning. There were three co-founders. I was kind of like an adjacent ops person at the beginning. I was like, you know, helped out with like community, like with Graph Day, helped plan that. I was doing kind of all the things as we scaled. And yeah, so we we built The Graph protocol and launched it in 2021. So it took about three years of building and launched a decentralized network that does indexing of blockchain data and allows developers to query it. I'm not sure how much the audience knows, but yeah, it's thegraph.com. There was a token called GRT. It's still in use today. The infrastructure has served trillions of queries over the years and still serving billions of queries per month. And it acts as a major part of the back-end infrastructure of many protocols that are out there today, like DeFi protocols. And yeah, is really a key piece of infrastructure that is still utilized today. And so it's been a really fun journey. I can't believe it's been eight years. But yeah, so Edge & Node is the original team. The company originally started as Graph Protocol, Inc., and then we rebranded our name to Edge & Node in 2021 when we launched The Graph and the network. And then, yeah, so we've been, you know, working all these years in improving the protocol. And then more recently, we've shifted our attention to more of the enterprise use case with our product Amp that's working directly with financial institutions and getting blockchain data into their organizations.

Amazing, amazing. And yeah, I don't know if a lot of people realize, but indexing blockchain data at scale is very difficult. Not every chain behaves the same, and you have to make provisions on how to kind of adjust for that. So yeah, kudos for you guys for taking on that early challenge, kind of being the first movers there. I guess from being part of it since day one, is there any particular big challenges or anything like that that you remember kind of going through and you want to talk about? I'm sure there was.

Yeah. I mean, there's too many to name, right? Yeah, I mean, everything from, you know, back in that era, there was like the specter of like SEC regulation and operating within like these guidelines and frameworks that we weren't even, you know, weren't quite clear. And so the state of the art back then was to set up like, you know, a Cayman Islands foundation and, you know, launched the token out of there. And so yeah, there was those kind of challenges. We always operated under strict guidelines in terms of like, you know, working with non-U.S. people, for example, and everything was above board. So there were challenges there. And then yeah, like scaling challenges. So, you know, it really took off in usage immediately. And just like keeping the infrastructure up and running during that time was, you know, incredible challenges. Team scaling challenges. You know, it's part of the startup game is it's there seemed to be like every day another problem to solve. So yeah, we've been through yeah, quite a number of them over the years.

Yeah, it's just the name of the game. That's just how startups work. And you mentioned it, Amp being one of the latest products from Edge & Node. Looking through it, it feels kind of like an evolution of what The Graph was initially, at least from my lens. But yeah, I'd love to hear more about it, just what it is and why it was built.

Yeah. Yeah, so we originally built subgraphs on The Graph Network, which was like, you know, a decentralized network of nodes that are constantly monitoring different blockchains, indexing the data in, which is sort of like filtering and storing the data in a database format. And so what we started to see was that financial institutions were coming on to utilize blockchain data, like in, you know, 2021, '22, starting to experiment. Our code was open source, and yeah, we heard through the grapevine that like large financial institutions had downloaded the software and were running it internally. You know, it was MIT license, free to use. So they were using it inside their organizations. And so as we started to do more R&D, we started to see that what we had built was kind of purpose-built for a particular developer use case, and these larger financial institutions had a different use case, which is like they have existing data pipelines, they have data analytics teams, they're wanting to bring data into existing systems like Snowflake or BigQuery or like big analytics platforms.

And so yeah, we spent a couple years in R&D kind of building the state of the art in terms of being able to index, extract the blockchain data from different blockchains, to keep up with the chain itself and handle all the kind of complexity around dealing with blockchain data, and then making a simple tool and platform that like data engineers could just use SQL to filter the data down, create data sets, and then pipe it into—plug into existing systems. It might be an Oracle database, or it might be a Postgres database, or it might be a BigQuery or Snowflake. So you could see it as a pipeline. So view it as a means to like extract data from the different blockchains and then pipe it into where it needs to go within an organization.

Amazing. Yeah, I'd love to see it in action. As I understand, you got a little demo for us.

Yes. Shall we switch over to that?

Yeah, let's switch over to that, and then I can ask some of my other questions kind of while you're doing that.

Okay, great. Let's do it. All right. So this is actually a preview of what's forthcoming. So Amp itself is really you can utilize via like command line or via a desktop, you know, like a data engineer would be utilizing it more in like a text-based interface. This is something we're calling Amp Cloud. So it's yet to be released, so I'm giving kind of your audience a preview of what's forthcoming. But basically, this is a UI, a visual interface to view and interact with data, and you don't have to be a data engineer to do it. So we start with kind of this natural language search for like what data you want to look for. So here we can say like, 'Show me like USDC transfers on Base and Ethereum.'

And the LLM determines what tables it needs to look at and writes the SQL query. And so it generates the query. You can copy it, you can edit it, and then view the data that you want to see. And so it gives you a preview of kind of what data you're looking at. So here we're looking at, you know, Base and Ethereum. And again, we're looking at ERC-20 transfers. So it's a very simple interface, but it's doing highly complex things behind the scenes because obviously it's keeping up with two different chains, you're now joining that data using a SQL statement, you're getting it stored into a table. And then from this, you can then save it as a data set.

And so you could think of a data set as like a table, like a database table that's constantly keeping up to date. So every new block, a new row is added if the if the data matches the criteria you're looking for. And so this gives you a preview of saving the data set. You can look at the SQL, of course, and modify it that way. And so you can create a data set, and how this is how you'd query it. If you export, it will give you the information as far as like where it's going to go. You can run it once or keep it continuously running, so keeping it syncing block by block.

And reorgs, so reorgs are kind of like where a chain has to go backwards and then go to like an uncle or a fork. And so some time that happens periodically with chains where it needs to go back and then goes down a different path. And so Amp handles all of that too, and that's one of a major complexity in dealing with blockchain data because you might have had exported data, but then if the the chain went through a reorg, it had to go backwards, and then it's on a new chain, and now your the data you had before is no longer in sync. And that creates a lot of problems. So if you're dealing, for example, with like financial institutions that are doing like needing accurate data for like to make trading decisions, right? You can't have data be inaccurate and not match what's onchain. So that's a huge benefit to what we do with Amp.

And so yeah, you can export this, it creates a pipeline. And so like the pipeline, this is a cool feature. It gives you a visual representation, and I think the audience can help see this here or this helps visualize kind of what Amp is doing, right? Cuz you have a source, which on the left is like it's coming from two different places, it goes through a transformation, and then it gets sent out, and on the right here is the destination is like a BigQuery database, like a big data warehouse. And so you can create as many pipelines as you want. And so like if we look here back at the beginning, these are like data sets that you can create. And so derived data sets are ones that I've created myself, and so if I log in here I can see. Raw data sets is like all the data. Derived data sets are data sets I created using either SQL or I can use AI, and it creates a subset of table a table of information that I want to look at that's constantly keeping up to date.

I'm sorry, I misspoke. 'Your datasets' are ones I've created. Derived datasets are like community-based. So actually like within your organization, or you can have contributors that are creating datasets, and so there may be a dataset that that was already created by someone, and so you don't need to necessarily reinvent the wheel and and create your own dataset. And so yeah, you would see like the data in terms of what's there. You can see pipelines and you know, see the ingestion rate, and see what is going on there. And then this is where you can look at like SQL statements themselves and kind of run a query. And one cool thing here is this 'verified.'

This verified stamp, so the verification piece is a big one. So this is where something that Amp offers is a cryptographic proof that the data matches what's onchain, and it uses like the hash proofs that, you know, that it matches like block headers, etc., and the Merkle roots of the the blockchain. So so for an audit purpose or a compliance purpose, like someone, like an audit team within a big organization, could come here and spot check or pull a report that verifies that, you know, the data that is being looked at for, you know, the data sitting in the database or the data warehouse is is fully verified and matches what's onchain.

So that's like a the ability for, you know, audit teams or compliance teams to make sure that the data at rest, you know, is it's sort of garbage in, garbage out thing, and so it ensures that the data is correct and complete, which is a big problem with blockchain data because nodes can drop data. You might be missing like a block, for example, out of say it's every 100,000 or. But even then, like when you're summing a total and you're trying to get a total and if it's off, and say there was like a large transfer in a block that was missing, your number's not going to match. And so, you know, for financial information, it's got to be 100% accurate and complete. So yeah, this is one of the another cool feature about it.

So really, this is like a visual representation of what you can do in sort of like a command line for Amp. And most data engineers would utilize like, you know, kind of notebooks in Python and and what that interface. We've added this that's forthcoming so people can start to play with it, see what they can do as a stepping stone towards moving Amp internally. And one of the main differences with Amp is that this is this is run as like an on-premise licensed software. So Amp Cloud will be something that you could, you know, create a login and like leverage it like on a monthly basis, kind of like a SaaS, which is yet to be launched. But Amp itself, as the engine, is designed to operate as like a licensed software. Like you'd license Oracle, you know, and you basically run the code within your secure cloud environment.

Which is important for these like larger financial institutions that have like highly secure data needs, that have highly secure infrastructure, and have existing data engineering teams. And again, like they're running data lakes or data warehouses in-house already. So this is where it's an enterprise that's gotten to a scale where data paying for data on a consumption model is getting very expensive. And it's like where the when they ask the 'build versus buy' question, like, 'Do we want to build what Amp does ourselves, or can we just bring this in and use it as part of our data infrastructure stack?' Then we'll do this.

Also, though, Amp Cloud, what I'm demoing, is is meant for like maybe smaller users, and so you'd be able to create a login, you'd be able to have access to it at like a on a monthly basis at like a lower utilization point, and then you could start to export data into a database or a system that you might be running in the cloud for yourself. So it's meant to be a sort of like a a stepping stone. So you might not be ready for like running—and even large institutions, many of them go into like POCs first or like we'll run it and then do a full implementation to bring it in-house. So this is also meant to be able to get up and running, like time to value is immediate so that you can get access to the data immediately, get value, and then start utilizing it, and then maybe you're preparing for like a larger project where you're going to like install and run it on-premise. So yeah, that's Amp, Amp Cloud that's forthcoming, and we're really excited to to share with the world.

Yeah, and thank you so much for for sharing that alpha with us in the audience today. We love we love our alpha on this show. And yeah, it looks really neat. And I don't know if a lot of the audience caught because like a lot of the audience is technical, but very subtly, like you you guys basically transformed some you transformed like blockchain data that like blockchain engineers basically mostly understand into SQL that most like most engineers, regardless of whether you're Web2 or Web3, can understand and query. And that that's that's a very powerful construct. Yeah, did you want to just talk about the the thinking and decision-making that kind of went into that?

Yeah. So The Graph itself and subgraphs were built on top of GraphQL, which was a language utilized for more like web application access. You know, Facebook developed it, and it was an improvement off of like using REST APIs. And then, yeah, we went with SQL because, yeah, it's it's kind of the most broadly known language in the world, I think, that most everyone knows and understands. And again, it allows you to like write a a simple SQL statement to form a table of what you're already familiar with of like blockchain data, and then store it in a table format.

And then also, AI's natively speak SQL, so that's another benefit. So when you can you ask natural language questions like that, the AI itself can write the SQL statement, understand the tables very easily, and then generate the code for you that you need to to look at that data and do analysis on it.

Absolutely, absolutely. And what you demoed, the this the utilization of AI and Amp data together, is that also available programmatically, or are you just planning to have, yeah?

Yeah, it will be. Yeah, I think there's like MCP server access and so yeah, you can point LLMs at it and, yeah, do analysis on the data itself. So yeah, it's really built to be AI native and compatible. Excellent.

Excellent. And yeah, as we know, like AI is AI is on everyone's timeline, like regardless of what industry you're working in. But agents themselves might want to query this data as well. Are there any plans to say make it available for pay-per-request via like x402 or or some other payment protocol?

Yeah, potentially. Once we launch Amp Cloud, we're we are looking at all kind of models there. So that already I think that already exists on The Graph itself, like they created a Graph MCP, and so yeah, we'd likely follow suit with with Amp Cloud. Yeah, we were very early on the x402 path. We we actually started researching micropayments back in 2021 for The Graph itself because, as you imagine, you're doing billions of queries, and you're paying sub-cent amounts for each query. And so we saw, okay, paying the gas fees for each query wasn't going to make sense because it was like the gas itself was more than the query itself.

So we looked into micropayments and developed like a a batching system with that's utilized within The Graph. And so yeah, we were researching agents in late '24 and then and how agents might pay, and then x402 came out. And so we we were early adopters of x402 and contributed with the Coinbase team and met with the Google team and with their work with A2A. Yeah, and we developed a project called Ampersand that was like that utilizes Amp behind the scenes, actually. And so it's a a platform for observability and control over agentic commerce and payments with like the ability to have like a rules and policy engine and all of that.

So yeah, AI we're very AI-forward, and yeah, we've obviously leveraged AI in the development. And I did a talk recently at like a data data management summit here in New York, and was specifically about how AI is creating like a massive amount of new data, and so like the amount of data is increasing, so like storage costs go up. But at the same time, the ability to create kind of bespoke or purpose-built data infrastructure that can cut your costs dramatically offsets that. And so we did that ourselves at Edge & Node. We cut like our cloud bills by like 84% over time. And Amp is kind of the first example of like a purpose-built piece of data infrastructure, and we believe that's that's going to be a megatrend going forward is now now AI lowers the cost of developing kind of custom infrastructure. And so you can have the AI, you work with the AI, and it will give you feedback on what trade-offs you can make.

So the AI might say, 'You can cut costs by like 80%, but your latency's going to go up by 10%,' and you decide, 'Okay, I'm willing to make that trade-off if we make this change here,' and you kind of go through that iterative process. And so if you're looking at like a large enterprise and they have like an increasing cost of data because of what AIs are producing, like more logs, more transactions, and more storage costs, but then the data infrastructure costs fall, what that means is on the same budget, like say the annual IT budget or AI and data and infrastructure budgets, like to say 10 million, say they've cut their costs by like 75% on infrastructure, but the costs have increased here, what that leaves is more there's on the same budget there's more ability to spend on AI and tokens and everything else.

So by leveraging and pulling levers, you can sort of find more budget for like additional AI, and you know, lower costs on what you're spending on like cloud bills or infrastructure or yeah, like costs that really could be cut.

Yeah, absolutely. I drink the same Kool-Aid. And yeah, absolutely, it's some wild times we're living in, but yeah, I'm here for it. Just before we let you go, as we're approaching time, just wanted to give you an opportunity to share how people can get started with Amp if they're interested in playing around with it, perhaps if there's like a beta program they can sign up for to play around with what you demoed.

Yeah, the best way is go to edgeandnode.com. It'll probably be linked with the show, but yeah, if you go there, there's just a 'Talk to us' button. And yeah, we can set up a demo, and then once, yeah, Amp Cloud is out, there'll be like kind of a self-serve option where you can like register and have like a free tier and play with it. But for the time being, for those that are interested, the best way is reach out to us via the website, and yeah, we'll set up a call and walk through everything and show you how it all works.

Amazing. And yes, indeed, all those resources will be in the description of the podcast below. So definitely check that out if that is something that interests you. And with that, Rodrigo, just want to thank you so much again for taking the time out of your busy day to come chat with us. Yeah, I think what you guys are building is really cool, and yeah, wish you guys the best of luck as you as you continue to build it out and release it to the wild.

Fantastic. Appreciate the time. Yeah, my pleasure being here, and happy to have this chat with you.

Amazing, amazing. Likewise. And with that, I just want to wish everybody a very happy Thursday or or Friday, wherever you may be, and we'll catch you back here tomorrow actually for another great episode of DevNTell. Till then, have a good one, folks. Cheers.