Transcript
Logan (0:00)
Foreign.
Host (0:05)
Welcome to Reshaping Workflows with Dell Pro Precision and Nvidia, where innovation meets real world impact in high performance computing.
Logan (0:19)
Welcome back to another episode, well, bonus episode of Reshaping Workflows with Delpro Precision and Nvidia RTX GPUs. I'm Logan, your host. You already know me. This is day three of gtc. We're in the Nvidia Inception area, probably one of my favorite areas, because you get to hear about the newest, latest, greatest kind of startups that have partnered with Nvidia. So I'm with Surreal DB with Toby. So Toby, real quick, give kind of an overview of your position at Surreal DB and then we'll get right into it.
Toby (0:46)
Awesome. Okay, so Surreal DB is a multimodal database that is kind of revolutionizing the context layer. And what that means is the data that gets pushed into an agent or into an LLM. Right now a lot of companies are working with just one type of data that's typically vector. But we go beyond that. We go from vector to full text search, to graph, to document data. And by combining all these different data modalities together and then using that data before you push it into an agent or an LLM, you can get better accuracy and a better response from the LLM.
Logan (1:18)
Okay, so first kind of question, you're right. Data really powers AI, whether it's locally in the data center, et cetera. We're about vector DVs all the time. You know, they're now GPU accelerated. But when we talk graph, other things, what's the real advantage of, you know, Surreal DB that you kind of bring to the table? Like you talk about agents, right? Give me like a, you know, I'm sure customers or anything like that, but like a use case, right, where you, where your platform kind of solves a problem that's, you know, maybe was previously unsolvable.
Toby (1:46)
Yeah, it's a good question. I think if you, if you think about the data that goes into the AI agents right now and then you compare that to how humans think, it's very different. Right? Humans don't just think around semantic matching or similarity of the meaning in a word. We think about things in terms of relationships and understanding and meaning. And with Surreal to be, that's what we do. We bring the similarity search in, we bring that full text search in. But actually we go beyond that and we have. You can build this kind of relationships between your data sets, between the meaning of words, the meaning of entities inside your data. That becomes very relevant when you're not just doing, let's say, a coding task, but when you're trying to map organizational wide data, maybe it's data that looks at organizations and emails and events and people, and that can't just be matched based on the similarity of a word. So it really know whether you're dealing with accuracy of agents at a small scale or whether you're dealing with large data sets which cross an entire organization. That's where SERUTBI benefits.