Arvind Jain (2:34)

We had good timing, I would say. So we started thinking about building Glean in late 2018, started the company early 2019. And so, interesting thing is that transformers as a technology had emerged by them. Now 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 rag. 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.

Host 2 (3:22)

So you were super early to it, actually.

Arvind Jain (3:24)

Yeah. And look, the models at the time were not as powerful as today. We started with this BERT model that Google had put in 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'd actually build custom embeddings on their business content. And then that would sort of power the semantic part of the search. But remember, like 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, they have tons and tons of information spread across many, many different systems. A lot of that information has become obsolete now because it was written many years back. When you build a search product, it's not just enough to say that, hey, I want to understand somebody's question, and I'm going to match it with the right information. Semantically or conceptually matches what the user is asking. Well, you got to solve for other problems, too. You got to actually pick information that's correct today, that is up to date, that has some authority. Like, you know, somebody who's an expert on this topic has actually written that document. So you have to do all of those other things too, to actually truly sort of, you know, pick the right knowledge and bring it back to people. So we started with, you know, building the product in that shape and form. It was very different product actually. Like nobody had actually searched enterprise search as a problem before. In fact, like, you know, the interesting thing that I remember is that, you know, even though I was coming off of a successful company, like, you know, with rubric, we had good success. I don't think people really wanted to invest in enterprise search or me, you know, for that matter because, you know, this problem was not exciting.

Host 2 (5:09)

It was traditionally a very bad problem.

Arvind Jain (5:10)

Right.

Host 2 (5:11)

So there's all these search engines fast. I remember when the early Google days was sort of an enterprise search engine, I think, based in Norway. Like, there's lots of attempts at this.

Arvind Jain (5:18)

A lot of attempts and no successes.

Host 2 (5:20)

Why do you think it didn't work? Because it felt like an awful market.

Arvind Jain (5:23)

It was like a graveyard, like, you know, of all these companies that tried to solve the problem and it didn't. Part of it was just that I think search is a hard problem in an enterprise. Like even getting access to all the data that you want to search. It was such a big problem in the pre SaaS world. There was no way to sort of go into those data centers, figure out where the servers were, where the storage systems were, try to connect with information in them. It was a big challenge. So SaaS actually solved that issue. So like search products, like most of them, most of those companies started in the pre SaaS world. They failed because you just couldn't build a TurnKey product. But SaaS actually allowed you to, to actually build something, you know, which, which is my insight was that like, look, you know, the enterprise world has changed. We have these SaaS systems now. And SaaS systems don't have versions like everybody, all customers have the same version. You know, they're open, they're interoperable. You can actually hit them with APIs and get all the content. I felt that the biggest problem was actually solved, which was that I could actually easily go and bring all the enterprise information and data in one place and build this unified search system on top. So that was actually a big unlock.

Host 2 (6:30)

So is the rise of these connectors and APIs internally. So you're using Google Docs instead of older school systems, or using Slack or using these new tools that now provide you access to the data underlying content.

Host 1 (6:41)

You guys must remember Google search appliance.

Arvind Jain (6:44)

Yeah.

Host 1 (6:44)

The idea of like I need to slurp your data continuously into a hardware appliance in order to actually do search is ludicrous.

Arvind Jain (6:52)

It was a challenge. The, you know, search as a. And by the way, the origins of Glean is so at Rubrik, you know, we had this problem like, you know, we grew fast. We had a lot of information across 300 different SaaS systems and nobody could find anything in the company. And people were complaining about it in our surveys and I, and I was, you know, I always run it in my startups. And so there's a complaint that, you know, it came to me like I had to solve it. So I tried to buy a search product and I realized there's nothing to buy. I mean that's, that's really the origins of how Glean got started as a company. And so that was like, you know, one big issue. Like, you know, the SaaS made it easy to actually connect, you know, your enterprise data and knowledge to a search system. So that actually made it possible for us to for the very first time build a turnkey product. But there are a lot of other advances as well. You know, one is, you know, like, look, you know, businesses have so much information and data. One interesting, you know, fact, one of our largest customers, they have more than 1 billion documents inside their company. Now here, this, you know, when Elad and I, you know, when we were working on search at Google, you know, in 2004, the entire Internet was actually 1 billion documents. You know, there's a massive explosion of content like inside businesses. So you have to build scalable systems and you couldn't build like a system like that before. In the pre cloud era, I would spend all my time just trying to build that scalable distributed system which, you know, we don't have to anymore because of thanks to, you know, all the paid cloud technology. And then of course transformers, like, you know, that's really the real, the big unlock, you know, that we had was that we could actually understand enterprise information more deeply and was very necessary in the enterprise compared to on the web. On the web, even if you don't have good semantic understanding, there is so much that you can learn from people's behavior because, you know, you have a billion people, you know, coming and using your product. In the enterprise, you don't have that luxury. So you have to sort of like, you know, make, make up for that, you know, lack of signal from users, you know, with other techniques and transformers.

Host 1 (8:43)

It sounds like you feel a combination of, I'd call it like more traditional IR and search techniques and embeddings is, is relevant. Do you think that persists? Like, where would you want bespoke infra or you know, signals like freshness and authority or like how much do models just do in the end?

Arvind Jain (8:58)

Yeah, I mean I think there's always this thought of that like you know, the models will have linear infinite context windows and you can just give them everything and they can figure things out automatically. But I don't think, you know, like we're anywhere close to, you know, that happening. I'll give you an example. Let's say that models are mimicking human intelligence, right? So they're actually getting more and more capable of like how we work like humans, but as a human. Imagine if I were to actually give you, let's say I give you a question and then I say that here's everything in a completely non organized fashion. I give you a whole bunch of 1 million documents and let's imagine you have the memory powers and speed, but it still just feels like a very complicated thing. It's very hard to make sense sense of information that is for example being given to you out of order. I give you one document that is something from today, something from four months back, something from three years, then something again from two days back. If I give you information in a manner where it's not organized in any shape or form, then as a human you're going to have a lot of difficulty reasoning over it. We think about the models the same way. There is a good amount of work that you have to do and present the information to the model in some organized fashion. That's when they're going to actually do a much better job reading that information, reasoning over it and giving these answers. And sure you can actually give them more and more over time, but still it matters how you provide them with the right information.

Host 2 (10:28)

Now that you have this corpus of information, you basically aggregated all the internal documents of a company which in itself is incredibly useful just for search. But you've also go down the route of like enabling applications to be built on top of it in different ways. Can you talk a bit about that and what are some of the common use cases that you're seeing?

Arvind Jain (10:43)

So we started with this vision of building a Google in your work life. But then as models got better developed like these reasoning and generation capabilities. So first it changed our product and like our new product like Green Assistant, you know, it sort of looks and feels more like chatgpt. So instead of like you know, me going, asking questions and seeing, you know, a bunch of links going back to me, you know, now of course you converse with Glean, you ask questions and it works just like ChatGPT, you come and ask question is going to actually take all of the world's knowledge and also additionally, you know, it's going to take all of your internal companies, you know, data and knowledge and use that in a safe and secure manner like knowing who you are and what information you can really use within the company to answer questions back for you. So that's sort of like the first, first progression in terms of our product. Like we evolved from being a Google to something that looks more like ChatGPT, more powerful version of ChatGPT inside your company. As you build that, this Glean assistant, actually you can think of it more like a personal assistant that you're actually giving to every employee in your company. It's a tool, it's your sidekick and it's always available to help you with whatever questions or tasks you have. It's going to use all of your company's context and data to help you with, you know, with your work. But you know, businesses are actually a lot more, more interested in not in that, but in actually thinking about how they can transform their company with AI, how they can take specific business processes, you know, where they're spending a lot of money and how, how do they bring automation in that with AI. So we've been, we've been asked like before agents became, you know, the, the talk of the, like, you know, of the day and like, you know, everybody is of course building agents. But early last year when agents had not yet taken off, people were asking us for that, hey, we need to build more curated applications using this data platform that you have. So as an example, HR teams would come to us and say that, look, we love green assistant. People come in there, ask questions about benefits and BPO and vacation policy and whatnot. It works great, but sometimes it uses content that's not authorized or blessed by us. And if somebody's coming and asking questions on people related topics, we want Glean to only use the curated content that our people team has created and we want it to behave in a particular way, with a particular tone and all of that. That was a request that we started to get last year that can we create more specific curated experiences function by function for different use cases. So we started to build that and we were not calling them agents, we were calling them apps. Now of course, people think of them more as agents because it's no longer just asking questions and getting answers, but you want these specific functional experiences to actually replace a business process which also involves doing some work for not just answering Questions, but actually doing some work in those systems.

Host 1 (13:44)

Arvind, when you talked about access to the right data with the right authority and also it really begs the question of access control in a platform like Glean. When you have all of this unstructured data. This seems much more complicated. What is like your overall stance or how you think this is going to work in the future?

Arvind Jain (14:05)

Yeah, well, so look, enterprise information in some sense, it's governed and it's protected most of the knowledge. Actually say like 90% of the knowledge inside the company is private in some shape or form. Inside, within your company you'll have a document that maybe is private to you or you share with a few other people. So that's the nature of enterprise knowledge. That's the fundamental sort of way like it works. And you can't take, you can't actually build like for example a model inside your enterprise and dump all of your internal companies data and knowledge into it and then make that model available to everybody in the company. Because if you do that, you're leaking information like you know, inside a company, you're letting somebody in the engineering team, you know, see sensitive stuff, you know, which probably only HR team should be able to see as an example. So any AI experiences that you build inside the company, it has to think about security and governance and permissions at a fundamental level. And that's what we do in Glean. So when we connect with all these different systems inside your enterprise, if we index a particular document from Google Drive or a conversation from Slack, we also keep track of who are the users and actually access that information. And this is fundamental. Any access to data that's going to happen through our platform is going to actually match. Like you know, the users have to be signed in and we will actually only let them use information that they have permissions for. And this is, this is important as a problem to solve. Like you know, unless if you have infrastructure like that, you cannot roll out AI safely inside your enterprise.

Host 1 (15:36)

I learned a lot from people who work on search, especially like search with any sort of scale because you get all sorts of weird user behavior and so related to your idea of, you know, us with our personal assistant team, what are some behaviors you see from end users in terms of how they're using Glean or AI in general that you think we should just do more of. Right. Like I, you know, I'm always very surprised when I learned from Google people about just like the behaviors around navigational search and how many are one word queries or what the popular queries are and those sorts of patterns. And so I'm sure you, you see like glean and AI super users.

Arvind Jain (16:10)

One of the biggest surprises for me, I always felt that we're building such an intuitive product. It's like it's literally there's no ui, you know, there's one box and you ask question, you put in a search and what's the big deal? Like why do you have to learn how to use this? And we realized that as we added more and more of these natural language capabilities and the ability for you to actually ask a really long question, like paragraph long set of instructions that you're giving to us. And we realized that people won't do it. I think everybody has been trained over the last 20 years to actually type in one or two keywords. Like Google has sort of taught us on what search can do. So with search you never had a problem. We launched our product we immediate high usage, nobody was confused how to use the product. With assistant, people didn't know what to do with it. Some people with more curiosity and they will, they will ask all kinds of questions that we couldn't actually answer. For example, somebody says that hey what should I do in my life? So I think, but anyways, coming back to this, that was one of the key learnings is that AI is actually very unintuitive for most people. You have to actually really expose to them these capabilities in a sort of a incremental fashion. Some things which sort of are more meaningful to their day to day work. For example, if I'm an engineer, prompt the user sometimes that look, you can actually learn about a new piece of technology. I can actually create a two page tutorial for you right now. And you sort of have to understand what people, what their core work is and then you have to actually give them these sort of prompts, like prompts for them to sort of start experimenting and get excited about trying something out with AI. One thing in fact which I would also add here is a lot of time with AI businesses are excited, they have a lot of dollars to spend on AI, but they're all also asking for ROI that I'm going to make all this investment. What are the returns, what are the efficiency gains that I'm going to be getting or what are the top line improvements that I can make to my business? There's a lot of focus on that. One thing that often gets overlooked is education because you know, the world is changing. Imagine like you know, three years from now, you wake up, you're the CEO of a large enterprise. What do you want to see in your workforce? You actually want to see people like, who are trained and are AI. First they're experts. They know how to leverage the strengths of AI. Because this is a difficult technology. Like, you know, it's not perfect, it's not easy, it makes mistakes, it hallucinates, but yet it's powerful. And if you become an expert, you can get a lot done with it. That has to be like the objective today is as leaders think about AI, how do you sort of give people tools that sort of motivate and motivate them to bring AI in their day to day work?

Host 2 (19:12)

You had an amazing career between being early at Google starting Rubrik, now starting Glean and running it. What was unexpected about doing Glean because you'd gotten to so much scale, you've done such amazing things in the context of Rubrik. What was hard or unexpected or just very different about Glean that you didn't.

Arvind Jain (19:30)

Anticipate from a product side? One of the most interesting things for me was how hard was it to actually roll the product out to our customers. We had a very different journey Rubrik compared to Glean Rubrikri an established market, there were bitters, several dollars and you had to actually replace an old technology with a new technology. Here we were in a market where we had no budgets. There was no concept of buying a search product in the enterprise. And everybody thought that, yeah, this is an important problem, but it's not a line item in my business priorities. It's a vitamin, it's a painkiller, people are living without it. Well, yeah, that's true. I mean you live without something that you don't have. That's by definition true. So we had a lot of challenge. We had to do a lot of evangelism to actually get the right like, you know, folks like, you know, who wanted to be the innovators, like for them to actually make that bold call and actually buy a product that's, you know, they're not used to buying. So that's sort of the first part of it. Like, you know, you have to create the market for this, which actually was difficult. And second, which was actually very interesting one is, you know, we, our product was actually working well. Like, you know, it was doing good search, letting people find things. But then we started to hear from businesses that, oh, I'm scared of good search. I don't want a good search product in my company because I have all these governance gaps. I have like, you know, sensitive information all over the place. And you know, now people are discovering these things. And we launched like, you know, for example, you know, people found like salaries of other people. You know, there was like in one of our customers somebody found a sensitive M and a dog that was, you know, or something that was, you know, not yet happened. And you start like. So people like you actually were very, very scared of actually having good search. So we had to actually like that was an interesting challenge. We did some good work doing it safely and securely. But you know, you don't have good governance and now we can't sell because the product is so good.

Host 2 (21:30)

It seems like LLM should be able to help with that, right? In terms of classifying documents and servicing, hey, this one may be sensitive. Do you want to secure it? Et cetera.

Arvind Jain (21:37)

Yes, in fact that exactly right. So we actually were forced to build that. We were forced to actually go above and beyond respecting permissions in individual systems to knowing who you are, what you're asking, like should you have the right to even ask the question or when the information comes back, does it feel safe enough for us to show it to you? So we actually, in fact in that sense we actually ended up becoming a security product. A lot of companies actually buy us to fix governance in their data and systems and become AI ready. AI ready for the Green Search product, the Glean Assistant, but also for all the other AI products that you can buy inside the enterprise. So that was actually a very interesting journey. But then for personally on, you know, for me, you know, at Rubrik, you know, I didn't actually, I wasn't this, I wasn't the CEO. I ran R and D as one of the founders of the company. And here I had to actually learn how to become a CEO and I don't think I've learned it yet. And like, you know, that's a, that's a constant, you know, challenge and like you know, learning center that I go through because fundamentally like I'm still an engineer. Everything I do like, you know, like that's the mindset that I have so growing, growing, you know, out of that into like, you know, being able to run a large business. You know, that's a, that's a personal transformation that I'm going through.

Host 2 (22:52)

One thing that I think is striking is that from a go to market perspective you all have really focused on big enterprises, right? And you mentioned some of these enterprise data needs. A lot of people always just want to do PLG and you've really done sort of the top down sale and it's been incredibly successful. And you've done it twice now, right, Because Rubrik was that as well. Could you talk a little bit more about when it makes sense to do big direct enterprise deals versus a PLG motion and how you think about that as you build businesses? Because I think it's very differentiated and most people just can't pull that off. So I'm curious about how you think about when to do it and then how to do it.

Arvind Jain (23:22)

Just to be candid. Clean the. When we started, my dream was to do plg. I'm an engineer and I wanted the company to have engineers and then product should sell itself, you know, on the web. Who doesn't want that? It was, it was something that, you know, was a desire for us. But the problem is like, you know, with our product, the. It is by definition a company wide product. Like it's not like, you know, we cannot offer the product to one individual inside a company. Even one person. You know, their search needs require us to actually search over the entire company's information for them. So it's, it's expensive. You have to actually index, you know, all of your company's data and knowledge. And so we never had that a concept of that we could make it available to one or two or 10 people inside the company. So it's not a force just structurally to actually build in that fashion. Where it is like enterprise, it is. We roll the product out company wide, every employee. That's what makes it cost effective. But coming back to your question, the standard approach I think that companies prefer now is that they think of PLG as basically lead gen, as a funnel. You sort of nurture and expand using you know, enterprise sales motion. So the right recipe for me, like, you know, if I had a choice, I would, I would actually start both the motions simultaneously. Like I won't actually say that. Look, you know, for the first three years I'm. I would actually focus, you know, on just being PLG and then bring enterprise states later. Because you're actually leaving a lot. You're leaving a lot on the table. Timing. Timing matters always. And, and so you have to sort of like start the. Start the motions. I think at the same time.

Host 1 (24:54)

Arvind, one thing that we have talked about that I feel like must have been, I mean hard. The priors on this market were not great. Right? And we talked a little bit through the rationale of like, you know, you feeling like you, you really solve the problem internally anyway and understand that there are these sort of architectural, foundational things that had changed in terms of movement to SaaS and API based integrations and such. But still I think it's a really question of advice for founders or maybe people joining startups, like when should you agree with the priors on like something is a bad market or how should you think about that question?

Arvind Jain (25:30)

So I'll share a few things on this. Number one, I think as engineers, like, you know, first of all there are always doubts like, you know, the more you look at priors, the more you're going to actually likely you're going to actually ultimately kill your own idea. There is a lot like you know.

Host 2 (25:44)

Sometimes everything's been tried.

Arvind Jain (25:46)

Yeah, yeah, everything has been tried. Like so, you know, a lot of things have failed and, and I think there are for any given Idea There are 10 reasons why it won't work. As you start to go into details, sometimes a more simpler approach is helpful which is, well, there's a problem. You talk to people they have and they feel this pain which clearly means that nobody is actually yet solving that because the pain exists. And so then don't go into details anymore, just do it. Things will just get figured out over time. So at least for me it was actually unusual for me. I'm an engineer by training myself and I'm like, you know, I'm naturally trained to question and like there's a lot of self doubt in my mind. So I don't know what happened to me when we started clean because there were all these people saying like no, not do it. And somehow they couldn't like, you know, they couldn't actually discourage me. Like, you know, I just felt that this was exciting problem. I knew everybody in the world, you know, has this issue like you know, even at Google, like it was a big joke, you know, always we had internally like all of us were spending all of our time making it easy for people to find things, but not us. Internally at Google it's just super hard to find anything inside the company. So I think I somehow found that conviction. I was sort of being lazy, not willing to go into the details and look at all those priors and just do it, just solve it. I mean I think that's what I think worked for us in this particular case.

Host 2 (27:11)

I feel like Glean had three big components to it that all came together that you mentioned earlier. There was the need that you identified just as somebody running it for your own company. And to your point it goes back to Google that this was a need. And every company that I've talked to has always wanted to build search and directories and all this stuff. The second thing is this rise of connectors and APIs in the context of existing enterprise software that everybody's using so you can extract the data more easily. And the third thing was the big shift in terms of the underlying technology, the shift in terms of what is capable of search, these foundation models, these embeddings, et cetera. Given the latter two, are there other big opportunities that Gleed isn't going to work on that you've kind of identified as really interesting areas that suddenly are tractable again?

Arvind Jain (27:51)

I think for us right now the focus remains on the two core products that we have. So the way we think about our company is that we have this really powerful end user AI assistant that helps every person work differently in the future. And then we have this Asian platform that you can use to actually bring AI, inject AI into every one of your business process, make them better, make them more efficient. And I think we have been making big promises on both to our customers. The way I describe and pitch our product to our customers is the following. Come to glean, ask any questions or give it any task. Glean will use all of the world's knowledge and all of your internal companies data and knowledge in a safe and secure way and answer those questions for you or complete those tasks. But actually I just promised to you that Gleaned is everything. You don't have to work anymore. We're long, long ways from actually even solving that. The pitch that I just mentioned to you, I think we have to understand knowledge properly. We have to pick the right, the correct information, throw away the old information. There's so many challenges there, there's so many issues. People talk about hallucinations as a big problem with AI models and you know, we feel like, you know, a bigger problem for us is not even hallucinations. It's about like, you know, most of the times you can't even, you know, find the right information. Sometimes it's not there, people asking questions, but nobody wrote it down. Sometimes, you know, we are not able to actually do the needle in the haystack. You know, we pick the wrong thing. And so there are like a lot of challenges. And I think we will be working on this problem for a long, long time. And I don't see us having any need by the way, like, you know, wanting to do something different like, you know, like just solving this one problem itself is a big, is a big, big success. So, so we're going to stay focused on these two, you know, these two products, but then they're also like, you know, talk. Talk to you about a little bit about the vision for the future. So I think the way we all work is sort of well accepted that AI is going to change everything. AI is going to change how people work. AI are going to actually change how businesses actually even look and feel, you know, what kind of, you know, workforce you have in the future. And one thing that's going to fundamentally happen is that each one of us is going to have this amazing team of, you know, call it assistants, coworkers, coaches, that are truly personal to you. And, you know, you're always surrounded by that team. And this team knows everything about you, your work life, what you need to do today. And it proactively helps you, does 90% of your work for you, and also, like, you know, help you get better. Like, you know, at your, you know, like, upskill, you be a coach. And that's the world that we want to be living in, like today. You know, there are some people who have already, who already live in that world. Like, you know, for example, being a CEO, you get the luxury to actually have all of that. You have assistants, your chief of staff, you have an exec team, you have a coach. But in the future, that's going to be something that all of us are going to have, like, you know, regardless of how senior we are, you know, maybe, maybe new grad joining the workforce. That's what we are trying to actually go and solve for. We're trying to actually build that amazing personal team around every individual that's going to make us all a 10 XL. And that's just a natural extension of, like, just keep evolving our clean assistant product. Make it better and better over time. Yeah.

Host 2 (31:06)

Arvind, thanks so much for joining us today.

Arvind Jain (31:08)

Yeah, that's excellent. Yeah, fun, fun questions. It's always nice to see it. Yeah. Likewise.

Host 1 (31:15)

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