AI is Making Enterprise Search Relevant, with Arvind Jain of Glean

**Sarah** (0:05)
Hi, listeners. Welcome to No Priors. This week, we're speaking to Arvind Jain, CEO and co-founder of Glean. Glean is an AI-powered enterprise search and knowledge management platform, which allows you to not only access all the different internal documents and slacks and other things that your company may have, but also allows you to enhance workplace productivity by using different applications on top of that. Prior to Glean, Arvind had a really storied career. He co-founded Rubrik. He was early at Google, worked on search there, amongst other things. And so we're very excited to have him here today. Arvind, welcome to No Priors.

**Arvind Jain** (0:34)
Thank you for having me.

**Elad Gil** (0:35)
So I'm really excited about this. I've known you for years and Elad's known you for maybe 15 more years than that. You're an amazing, repeat successful founder with Rubrik and Glean. I want to start by just asking you about search. You've been a search guy since before it was cool, for a long time when it felt like not solved, but not as dynamic. How broadly has search changed because of LLMs?

**Arvind Jain** (1:01)
I've been working on search for almost 30 years now, a long, long time. The paradigm has completely shifted. I think I would say that search had been static for a long time. It was this keyword-based paradigm, like people ask questions, you find words, and try to find them in documents and bring them up to the users. But LLMs have completely changed it. Like it has actually, the main thing it has done for search is that it has allowed us to really deeply understand a question that a user is asking. And similarly, it allows us to very deeply understand what a document is about. And you can actually match people's questions with the right information conceptually. And that gives us so much more powers. It's not brittle anymore. And I think it's been a foundational technology to really evolve search into these new experiences that you're seeing these days, where you can go far beyond just surfacing a few links to an end user, to actually deeply understand their questions and answering them for them directly using the knowledge that you have.

**Sarah** (2:02)
If I remember correctly, Glean got started in the more traditional search world. And then as these foundation models and these LLMs have come to the fore, you've really kind of shifted how you think about both the capability set that you provide and how you approach things. Can you tell us a bit more about how you started off building the systems and how that's shifted, and then how you've kind of mapped new use cases against it? Because you're now effectively like this really interesting platform that can be used in all sorts of ways inside of an organization around the purpose of information they have. I'd just even love to hear the technology transition. Like, how did you think about that? What did it happen? Yeah, I think you really lived through it in a really meaningful way.

**Arvind Jain** (2:34)
We know we had good timing, I would say. So, you know, we started thinking about building Glean in late 2018 I started the company early 2019 And so, the interesting thing is that Transformers as a technology had emerged by them. The whole world was not talking about it. But in search teams, like at Google, we saw the power of embeddings and how it could fundamentally change search. And so, we had that luxury to actually see this in action. So, the version, one of our product actually already used transformers for semantic matching. We didn't have these terms, nobody used to call it vector search. We didn't have that. These terms had not been invented yet or generative AI for that matter. And so, internally, we used to call it embedding search. And it was a core technology that we started out with.

**Sarah** (3:23)
So, you were super early to it, actually. Yeah.

**Arvind Jain** (3:25)
And the models at the time were not as powerful as today.
We started with this BERT model that Google had put in an open domain, which was trained on all of the Internet's data and knowledge. And we would then take those models, and then for every customer of ours, we would actually build custom embeddings on their business content. And then that would power the semantic part of the search. But remember, search as a technique, there's a lot of focus on embeddings and vector search over the last few years. But that's actually only one part of building a good search system. Because if you think about an enterprise, imagine a company that has been around for a few decades. There are tons and tons of information spread across many, many different systems. A lot of that information has become obsolete now, because it was written like many years back. And so when you build a search product, it's not just enough to say that, hey, I want to understand people, somebody's question, and I want to match it with the right information sort of semantically or conceptually matches what the user is asking. Well, you've got to solve for other problems too. You've got to actually pick information that's correct today, that is up to date, that has some authority, like somebody who's an expert on this topic has actually written that document. You have to do all of those other things too, to actually truly sort of pick the right knowledge and bring it back to people. So we started with building the product in that shape and form. It was a very different product actually, like nobody had actually searched enterprise search as a problem before. In fact, the interesting thing that I remember is that, even though I was coming off of a successful company, like through Brick, we had good success, I don't think people really wanted to invest in enterprise search or me, for that matter, because this problem was not exciting.