The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:20:21 · George Sivulka, Harry Stebbings
[00:20:21] S0: Yeah. It was Anne Anne at Floodgate. [00:20:22] S1: Okay. Anne and Floodgate and and Teal. And then we get back to work. Mhmm. And we've got a million or so. [00:20:28] S0: We've got a million. And then two months later, Mike actually hears about Hebiah from his daughter who was a Stanford student. And I think I've seen the product, was friends, and and then Mike actually comes in and is like, hey. This is completely different than Elastic or all these other search technologies that I've seen and invested. Obviously, he's on the board of Elastic today, so he's like, well, let's just go add some fuel to the fire. [00:20:49] S1: How much did he invest then? [00:20:50] S0: He, I think, invested, like, an additional 2 or $2,500,000 at [00:20:54] S1: the time. So where did the 130 come from? [00:20:58] S0: Well, as as years later. You know? This was [00:21:00] S1: years later? [00:21:01] S0: So this is all in 2020. We we end up building a product studio, which is the first to productionize RAG, retrieval augmented generation, also in 2020. We build the first semantic search engine. We actually go out and and search Can [00:21:16] S1: you just help us understand? What is RAG? [00:21:19] S0: RAG is an acronym that stands for retrieval augmented generation. If you look at large language models today, they're really good at at maybe thinking if you give it the right context, but they hardly ever have the right context. And so Rag was the first real attempt to give them the data to answer questions correctly. [00:21:38] S1: Okay. And so we are building on Rag to start?
00:21:38 · Harry Stebbings, George Sivulka
[00:21:38] S1: Okay. And so we are building on Rag to start? [00:21:41] S0: We were one of the first people to productionize the idea of putting a search engine behind an LLM. So you'd ask a question, and then instead of it just replying from its memory, it would actually go and do a search and then reply with that context. So in an enterprise where you have a lot of offline data, we were really the first people to hook up that offline data to large language models to answer that question. [00:22:01] S1: So you're one of the first. And we're we're seeing that now in action, and it's working?
00:22:01 · Harry Stebbings, George Sivulka
[00:22:01] S1: So you're one of the first. And we're we're seeing that now in action, and it's working? [00:22:05] S0: That's a bit of a plot twist over here. We... I actually don't think RAG works at all. It's one of the most used AI architectures in the world, pioneered at Hebia in a very meaningful way. I think every enterprise is experimenting with it, but it has a lot of different failures where a lot of the time the questions that people ask these systems aren't ever explicitly in the data. They're never explicitly stated. They're actually about the data. So for example, you know, if you're asking an AI system, is this company a good investment? Which is actually a very common thing that people ask Hebbio over marketing materials. Maybe it'll say in a pitch deck, yeah, this company is a great investment as a... Something that the CEO says or like a recording, etcetera. But what you actually want from that system isn't to search in the data, It's an answer about the data. Hey. What's the customer concentration? What's the strength of the management team? What are x, y, or z criteria that are are fundamental to our specific investing process? And that's a process. That's not that's not ever explicitly stated. Actually, the marketing materials are often like a load of crap. You have to actually distill what's true out of them. That's what Hebia does. So it's not actually finding something that exists already. It's taking all the things that exist already and starting to answer questions about that information.
00:23:16 · Harry Stebbings, George Sivulka
[00:23:16] S1: Take me to that transition then because we were building on Rack, and we're like, great. We're gonna productionize this, and then we move off. Yeah. And you realize that actually it's bullshit, and it's not as good. Yes. Take me to that realization. [00:23:28] S0: Yeah. So we actually deploy at some of the largest finance firms in the world. We we ended up going from zero to a million dollars of revenue sometime in 2021 or 2022 and raise our series a also from from index, from MICHA index, which is $30,000,000. And we start to see that all these customers... Okay. Now they know what, you know, ChatGPT is. They they know what LLMs are. Hebby has this, like, really mature enterprise product in the market, and we're by far the first to actually get there. And we just looked at all the queries that people were asking. And the questions that people were asking weren't ever, okay. Find me the quote or find me the, you know, command f questions. They were actually more, okay. Read all the documents and then tell me all the times they mention AI or what our exposure to Silicon Valley Bank is during the regional banking crisis. And so all of these questions... It was actually almost 90% of the questions that people were asking these systems weren't answerable by a search through the documents, but rather they had to be work done on top of the documents and then answered.
00:23:28 · George Sivulka, Harry Stebbings
[00:23:28] S0: Yeah. So we actually deploy at some of the largest finance firms in the world. We we ended up going from zero to a million dollars of revenue sometime in 2021 or 2022 and raise our series a also from from index, from MICHA index, which is $30,000,000. And we start to see that all these customers... Okay. Now they know what, you know, ChatGPT is. They they know what LLMs are. Hebby has this, like, really mature enterprise product in the market, and we're by far the first to actually get there. And we just looked at all the queries that people were asking. And the questions that people were asking weren't ever, okay. Find me the quote or find me the, you know, command f questions. They were actually more, okay. Read all the documents and then tell me all the times they mention AI or what our exposure to Silicon Valley Bank is during the regional banking crisis. And so all of these questions... It was actually almost 90% of the questions that people were asking these systems weren't answerable by a search through the documents, but rather they had to be work done on top of the documents and then answered. [00:24:24] S1: My question is, like, what the fuck happens to the rest of the landscape if you're like, no. Rag is not actually the right approach, and they're all loving Rag. Mhmm. Couldn't be hotter right now. [00:24:34] S0: I I don't think they're loving Rag. [00:24:36] S1: You don't think they [00:24:36] S0: are? I don't think they're loving Rag. No. [00:24:38] S1: What makes you say that?
00:24:38 · Harry Stebbings, George Sivulka
[00:24:38] S1: What makes you say that? [00:24:39] S0: I think, like, 90% of enterprise AI right now is almost like this vaporware. We swear it works. Like, look at this amazing demo where we ask, what does the CEO say about the investment? And the minute that they actually go to, you know, try to use it in a real world example completely just fails. And so I actually think that the majority of AI usage, a lot of these usage statistics are all kind of, one of my favorite phrases is fugazi fugazi. And one of things that Hebio really tries to put forth in the market is is to say, hey. Change will take time, but we have a system that that is actually starting to drive real measurable value over very specific defined use cases. And our tagline's always, hey. Stop experimenting with AI, which everyone's experimenting. They're all really excited about it. Start driving value, like, getting value out of it. [00:25:26] S1: To what extent is this RPA versus Ajanti?