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

Speaker 1 Hi, listeners. Welcome to NoPriors.
This week, we're speaking to Arvind Jane, CEO and co-founder of Glean.

Speaker 1 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.

Speaker 1 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.

Speaker 1 Arvind, welcome to No Priors. Thank you for having me.

Speaker 2 So I'm really excited about this. I've known you for years and a lot's known you for maybe 15 more years than that.

Speaker 2 You're an amazing repeat successful founder with Rubrik and Glean.

Speaker 2 I want to start by just asking you about search. You've been a search guy, you know, since before it was cool for a long time when it felt like not solved, but not as dynamic.

Speaker 2 How broadly has search changed because of LLMs?

Speaker 1 I've been working on search for almost 30 years now, long, long time. The paradigm has completely shifted.
I think I would say that search had been static for a long time.

Speaker 1 It was this keyword-based paradigm, like, you know, people ask questions, you find words, and try to find them in documents and bring them up, you know, to the users.

Speaker 1 But LLMs have completely changed it. Like, you know, it has actually, the main thing it has done for search is that it has allowed us to really deeply understand

Speaker 1 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.

Speaker 1 And that gives us so much more power. It's not brittle anymore.
And I think

Speaker 1 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

Speaker 1 to an end user to actually deeply understand their questions and answering them

Speaker 1 for them directly using the knowledge that you have. If I remember correctly, Gleen got started in the more traditional search world.

Speaker 1 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.

Speaker 1 Could 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?

Speaker 1 Because you're now effectively like this really interesting platform. that can be used in all sorts of ways and inside of an organization around the corpus of information they have.

Speaker 1 I just even love to hear the technology transition. Like, how did you think about that? What did it happen? I think you really lived through it in a really meaningful way.

Speaker 1 You know, we had good timing, I would say. So, like, you know, I started thinking about building clean in late 2018.
I started the company early 2019.

Speaker 1 And so, the interesting thing is that Transformers as a technology had emerged by then. Now, the whole world was not talking about it.

Speaker 1 But in search teams, like at Google, you know, 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.

Speaker 1 So the version, one of our products actually already used transformers for semantic matching. Like, you know, we didn't have these terms, like nobody used to call it vector search.

Speaker 1 You know, we didn't have RAG. These terms had not been invented yet, or generative AI for that matter.

Speaker 1 And so internally, we used to call it embedding search and it was a core technology that we started out with. So you were super early to it, actually.
Yeah.

Speaker 1 And look, the models at the time were

Speaker 1 not as powerful as today. Like, you know, we, you know, we started with this BERT model that Google had put in open domain, which was trained on all of the all of the internet's data and knowledge.

Speaker 1 And we would then take those models, and then for every customer of ours, we'd actually build custom embeddings on their business content.

Speaker 1 And then that would sort of power the semantic part of the search.

Speaker 1 But remember, like, you know, 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

Speaker 1 building a good search system. Because if you think about an enterprise, imagine a company that has been around for a few decades.

Speaker 1 They have tons and tons of information spread across many, many different systems. A lot of that information has become obsolete

Speaker 1 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'm going to understand

Speaker 1 somebody's question and I'm going 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.

Speaker 1 You've 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 had actually written that document.

Speaker 1 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.

Speaker 1 So we started with building the product in that shape and form.

Speaker 1 It was a very different product, actually. Like nobody had actually searched enterprise search as a problem before.

Speaker 1 In fact, the interesting thing that I remember is that even though I was coming off of a successful company, like you know, with Rubrik, you know, we had good success, I don't think people really wanted to invest in enterprise search or me, you know, for that matter, because this problem was not exciting.

Speaker 1 It was traditionally a very bad problem, right? 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.

Speaker 1 Like there's lots of attempts at this. A lot of attempts and no successes.
Why do you think it didn't work? Because

Speaker 1 it felt like an awful market. It was like a graveyard

Speaker 1 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.

Speaker 1 In an enterprise, like even getting access to all the data that you want to search was such a big problem. In the pre-SaaS world,

Speaker 1 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.

Speaker 1 It was a big challenge. So, SaaS actually solved that issue.

Speaker 1 So, like, search products, like most of them, most of those companies started in the pre-SaaS world, they failed because you could just couldn't build a turnkey product.

Speaker 1 But SaaS actually allowed you to actually build something, you know, which is my insight was that like, look, you know, the enterprise world has changed.

Speaker 1 We have these SaaS systems now, and SaaS systems don't have versions. Like everybody, all customers have the same version.
You know, they are open, they're interoperable.

Speaker 1 You can actually hit them with APIs and get all the content.

Speaker 1 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.

Speaker 1 So that was actually a a big unlock. So it was the rides of these connectors and APIs internally.

Speaker 1 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 or your line content.

Speaker 2 You guys must remember Google search appliance.

Speaker 1 Yeah.

Speaker 2 The idea of like, I need to slurp your data continuously into a hardware appliance in order to actually do search is ludicrous.

Speaker 1 It was a challenge.

Speaker 1 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.

Speaker 1 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 North Pulse surveys.

Speaker 1 And I, and I was, you know, I always run ID in my startups. And so there's a complaint that, you know, it came to me, like, I had to solve it.

Speaker 1 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 Green got started as a company.

Speaker 1 And so that was like, you know, one big issue. Like, you know, the

Speaker 1 SaaS made it easy to actually connect your enterprise data and knowledge to a a search system. So that actually made it possible for us to, for the very first time, build a turnkey product.

Speaker 1 But there are a lot of other advances as well. One is like, look, businesses have so much information and data.

Speaker 1 One interesting fact, one of our largest customers, they have more than 1 billion documents inside their company. Now hear this.

Speaker 1 When Elar and I, 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 inside businesses.

Speaker 1 So you have to build scalable systems and you couldn't build like a system like that before in the pre-cloud era.

Speaker 1 I would spend all my time just trying to build that scalable distributed system, which we don't have to anymore because of thanks to all the great cloud technology.

Speaker 1 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.

Speaker 1 And was very necessary in the enterprise compared to on the web.

Speaker 1 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 have a billion people, you know, coming and using your product.

Speaker 1 In the enterprise, you don't have that luxury.

Speaker 1 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 transform is one of them.

Speaker 2 It sounds like you feel a combination of, I'd call it like more traditional IR and search techniques and embeddings is relevant. Do you think that persists?

Speaker 2 Like where would you want bespoke infra or signals like freshness and authority or like how much do models just do in the end?

Speaker 1 Yeah, I mean, I think there's always this thought of that, like, you know, the models will have near-infinite context windows, and you can just give them everything and they can figure things out automatically.

Speaker 1 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.

Speaker 1 So they're actually getting more and more capable of like, you know, how we work like humans.

Speaker 1 But as a human, like, you know, imagine like, you know, if I were to actually give you, like, let's say I give you a question and then I say that, hey, here's, you know, here's everything.

Speaker 1 Like, you know, in a completely non-organized fashion, I give you a whole bunch of 1 million documents.

Speaker 1 And let's imagine you have the memory powers and speed, but it still just feels like a very complicated thing.

Speaker 1 It's very hard to make sense of information that is, for example, being given to you out of order.

Speaker 1 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

Speaker 1 in a manner where

Speaker 1 it's sort of not organized in any shape or form, then as a human, you're going to have a lot of difficulty reasoning over it. So we think about the models the same way.

Speaker 1 There is a good amount of work that you have to do and present the information to the model in some you know in some organized fashion.

Speaker 1 That's when they're going to actually do a much better job reading that information, reasoning over it, and giving you the answers.

Speaker 1 And sure, you can actually give them more and more over time, but still it matters like you know how you provide them with the right information.

Speaker 1 Now that you have this sort of of corpus of information, you basically aggregated all the internal documents of a company, which in itself is incredibly useful just for search.

Speaker 1 But you've also got down the route of enabling applications to be built on top of it in different ways. Could you talk a bit about that and what are some of the common use cases that you're seeing?

Speaker 1 So we started with this vision of building a Google in your work life.

Speaker 1 But then as models got better, developed these reasoning and generation capabilities.

Speaker 1 So first, it changed our product and our new product, like Glean Assistant, it sort of looks and feels more like Chat GPT.

Speaker 1 So instead of like, you know, me going, asking questions and seeing, you know, a bunch of links coming back to me, you know, now, of course, you converse with Glean, you ask questions, and it works just like ChatGPT.

Speaker 1 You come and ask a question. It's going to actually take all of the world's knowledge.

Speaker 1 And also, additionally, you know, it's going to take all of your internal company's 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.

Speaker 1 So that's sort of like the

Speaker 1 first progression in terms of our product. Like, you know, we evolved from being a Google to

Speaker 1 something that looks more like ChatGPT, a more powerful version of ChatGPT inside your company.

Speaker 1 As you build that, this green assistant actually, you can think of it more like a personal assistant that you're actually giving to every employee in your company.

Speaker 1 It's a tool, you know, it's your sidekick, and it's always available to help you with whatever questions or tasks you have.

Speaker 1 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 uh and how how do they bring automation in that with ai so we've been we've been asked like before agents became uh you know the the talk of the like you know of the day and like you know everybody's of course building agents uh but early last year when agents had not yet taken off, people were asking us for that, hey,

Speaker 1 we need to build more curated applications

Speaker 1 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

Speaker 1 benefits and PPO and vacation policy and whatnot.

Speaker 1 And

Speaker 1 it works great, but sometimes it uses content that's not authorized or blessed by us.

Speaker 1 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.

Speaker 1 And we want it to behave in a particular way, particular tone, and all of that. So

Speaker 1 that was the request that we started to get last year: that, like, you know, can we create more specific curated experiences, you know, function by function for different use cases?

Speaker 1 So we started to build that, and we were not calling them agents, we were calling them apps.

Speaker 1 Now, of course, like, you know, the people think of them as more as agents because it's no longer just asking questions and getting answers, but you want

Speaker 1 these specific functional experiences to actually replace a business process, which also involves doing some work for,

Speaker 1 you know, not just answering questions, but actually doing some work in those systems.

Speaker 2 Arvind, when you talked about, you know, access to the right data with the right authority and also like it really begs the question of like access control

Speaker 2 in a platform like Glean. When you have all this unstructured data,

Speaker 2 this seems much more complicated. What is like your overall stance or how you think this is going to work in the future?

Speaker 1 Yeah. Well, so look, enterprise information, in some sense, you know, it's governed and it's protected.

Speaker 1 You like most of the knowledge, I should say, like 90% of the knowledge inside the company is private in some shape or form inside within your company.

Speaker 1 You'll have a document that maybe is private to you or you share with a few other people. But that's the nature of

Speaker 1 enterprise knowledge.

Speaker 1 That's the fundamental sort of way like it works.

Speaker 1 And you can't take, you can't actually build, like, for example, a model inside your enterprise and dump all of your internal company's data and knowledge into it and then make that model available to everybody in the company.

Speaker 1 Because if you do that, you're leaking information like, you know, inside your company, you're letting somebody in the engineering team see sensor stuff, you know, which probably only HR teams should be able to see as an example.

Speaker 1 So any AI experiences that you build inside the company has, it has to think, you know, about security and governance and permissions, like, you know at a fundamental level and that's what we do in glean so when we connect with all these different systems um you know inside an enterprise we you know if if we index you know a particular document from google drive or a conversation from slack we also keep track of you know who are the users can actually access that information and this is fundamental like 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 and this is this is important as a problem to solve.

Speaker 1 Like, you know, unless if you have infrastructure like that, you cannot roll out AI safely inside your device.

Speaker 2 I learn 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.

Speaker 2 And so related to your idea of us with our personal assistant team.

Speaker 2 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?

Speaker 2 Like I, you know, I'm always very surprised when I learn 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.

Speaker 2 And so I'm sure you see like glean and AI super users.

Speaker 1 One of the biggest surprises for me,

Speaker 1 I always felt that, you know, we're building such an intuitive product.

Speaker 1 You know, the 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?

Speaker 1 And we realized that as we added more and more of these natural language capabilities and

Speaker 1 the ability for you to actually ask a really long question, like paragraph long

Speaker 1 set of instructions that you're giving to us.

Speaker 1 And we realized that people won't do it.

Speaker 1 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

Speaker 1 on what search can do. So with search, we never had a problem.
Like you launched a product, we had immediate high usage. Nobody was confused, like, how to use the product.

Speaker 1 With assistant, people didn't know what to do with it. Some people with, you know,

Speaker 1 more curiosity and

Speaker 1 they will ask all kinds of, you know, questions that we couldn't actually answer. For example, somebody says that, hey, what should I do with my life?

Speaker 1 So we, 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

Speaker 1 expose to them these capabilities in a sort sort of a incremental fashion. You know, like some things, you know, which sort of are more meaningful to their day-to-day work.

Speaker 1 For example, if I'm an engineer,

Speaker 1 like, you know, prompt the user sometimes that, like, look, you can actually learn about a new piece of technology. Like, I can actually give, you know, create a two-page tutorial for you right now.

Speaker 1 And you sort of have to understand what people's, you know, people, like, you know, what their core work is.

Speaker 1 And then you have to actually give them these, you know, sort of prompts, like prompts for them to sort of start experimenting and get excited about like, you know, trying something out with AI.

Speaker 1 One thing, in fact, which I would also add here is

Speaker 1 like a lot of time, you know, with AI, businesses are excited. Like, you know, they have a lot of dollars to spend on AI.

Speaker 1 But they're all like also asking for ROI.

Speaker 1 That, well, like, you know, I'm going to make all this investment. What are the returns? Like, what are the efficiency gains that I'm going to be getting? Or what are the top line?

Speaker 1 you know, improvements that I can make to my business. There's a lot of focus on that.
And I think one thing that often gets overlooked is education. Because the world is changing.

Speaker 1 Imagine 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 who are trained and are AI first.

Speaker 1 They're experts. They know how to leverage the strengths of AI.
Because this is a difficult technology. It's not perfect.
It's not easy. It makes mistakes, it hallucinates, but yet it's powerful.

Speaker 1 And if you become an expert,

Speaker 1 you can get a lot done with it. That has to be the objective today is like, you know, with like as leaders think about AI, how do you sort of give people tools

Speaker 1 that

Speaker 1 sort of motivate and motivate them to bring AI in their day-to-day work? You had an amazing career between being early at Google, starting Rubrik, now starting Glean and running it.

Speaker 1 What was unexpected about doing Glean? Because you'd gotten to so much scale. We'd done such amazing things in the context of Rubrik.

Speaker 1 What was hard or unexpected or just very different about Glean that you didn't anticipate? From a product side,

Speaker 1 one of the most interesting things for me was

Speaker 1 how hard was it to actually

Speaker 1 roll the product out to our customers. We had a very different journey

Speaker 1 rubric compared to Glean. In Rubrik, we're an established market.
There were budgets, there were dollars, and you had to actually replace an old technology with a new technology.

Speaker 1 Here, we were in a market where

Speaker 1 we had no budgets. There was no concept of buying a search product in the enterprise.

Speaker 1 And everybody thought that, yeah, like this is an important problem, but I'd like, you know, it's not a line item in my business priorities. You know, it's a vitamin, it's a painkiller.

Speaker 1 People are living without it. Well, yeah, that's just true.
I mean, you live without something you don't have. Like, you know, that's by definition, you know,

Speaker 1 true. So we had a lot of challenge.
Like, we had to do a lot of evangelism to actually get the right

Speaker 1 folks 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.

Speaker 1 So that's that's sort of the first part of it. Like, you know, you have to create the market for this, which actually was difficult.

Speaker 1 And second, which was actually a very interesting one is, you know, we our product was actually working well. Like, you know, it was doing good search, you know, letting people find things.

Speaker 1 But then we started to hear from businesses that, oh, I'm scared of good search.

Speaker 1 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.

Speaker 1 And, you know, now people are discovering these things. You know, we launched like, you know, for example, you know, people found like salaries of other people.

Speaker 1 You know, there was like in one of our customers, somebody found a sensitive MA dog that was, you know, or something that was, you know, not yet happened.

Speaker 1 And you start like, so people like, you know, actually were very, very scared of actually having good search. So we have to actually, like, that's an interesting challenge.
We just did some good work.

Speaker 1 We're doing it safely and securely, but, you know, you don't have good governance. And now like, you know, we don't, we can't sell because the product is so good.

Speaker 1 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?

Speaker 1 Yeah, so in fact, that's exactly right.

Speaker 1 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.

Speaker 1 Should you have the right to even ask the question? Or when the information comes back, does it

Speaker 1 feel safe enough for us to show it to you? So, we actually, in fact,

Speaker 1 in that sense, we actually ended up becoming a security product.

Speaker 1 A lot of companies actually buy us to fix governance in their sort of data and systems and become AI-ready, like AI-ready for the green search product, the green assistant, but also for all the other AI products that you can buy inside the enterprise.

Speaker 1 So, that was actually a very interesting journey. But then, personally, for me,

Speaker 1 at Rubrik,

Speaker 1 I wasn't the CEO. I ran RD 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.

Speaker 1 And like, you know, that's a, that's a constant, you know, challenge and like, you know, learnings that I go through because fundamentally, like, you know, I'm still an engineer.

Speaker 1 Everything I do, like, you know, like, you know, that's the mindset that I have.

Speaker 1 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.

Speaker 1 One thing that I think is striking is that from a go-to-market perspective, you all are really focused on big enterprises, right? And you mentioned some of these enterprise data needs.

Speaker 1 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?

Speaker 1 Because Rubrik was telling you that as well.

Speaker 1 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?

Speaker 1 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.
Just to be candid, lean,

Speaker 1 when we started, I mean, my dream was to do PLG. I'm an engineer and I wanted the community to have engineers and then product should sell itself, you know, on the web.
Who doesn't want that?

Speaker 1 It was something that was a desire for us.

Speaker 1 But the problem is like, you know, with our product,

Speaker 1 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.

Speaker 1 Even one person, you know, their search needs require us to actually search over all the entire company's information for them. So it's expensive.

Speaker 1 You have to actually index, you know, all of your company's data and knowledge. And so we never had concept of that, you know, we could make it available to one or two or 10 people inside the company.

Speaker 1 So, we're sort of forced just structurally to actually build in that fashion where it is, it is, you know, like enterprise, it is like, you know, we roll the product out company-wide, you know, to every employee.

Speaker 1 Um, that's what makes it cost-effective.

Speaker 1 But, like, you know, coming back to your question, the standard approach I think that companies prefer now is that like they think of PLG as basically lead channel as a funnel you sort of nurture and expand using you know enterprise sales motion.

Speaker 1 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'm gonna actually focus you know uh on just being plg and then bring the enterprise sales later because you're actually leaving a lot you're leaving a lot on the table uh timing timing matters always and and so you have to sort of like start the start start the motions i think at the same time arvin one thing that we have talked about that i feel like must have been um i mean hard the priors on this market were not great, right?

Speaker 2 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.

Speaker 2 But still, I think it's a really big question of advice for founders or maybe people joining startups, like,

Speaker 2 when should you agree with the priors on like something is a bad market or how should you think about that question?

Speaker 1 So I'll share a few things on this.

Speaker 1 Number one, I think as engineers, like, you know, there's, there are, first of all, there are always doubts.

Speaker 1 Like, you know, the more you look at priors, the more you're going to actually, likely, actually ultimately kill your own idea. There is a lot, like, you know, sometimes.
Everything's been tried.

Speaker 1 Yeah. Yeah.
Everything has been tried. Like, so, you know, a lot of things have failed.
And I think there are, like for any given idea, like, you know, there are 10 reasons why it won't work.

Speaker 1 Like as you start to go into details. Sometimes like a more simpler approach is helpful, you know, which is,

Speaker 1 well, there's a problem. Like, you know, you talk to people, they have and they feel this pain, and which clearly means that nobody is actually yet solving, you know, that because the pain exists.

Speaker 1 And so don't go into details anymore, just do it. Things will just get figured out over time.
So like, at least, you know, for me, like, it was actually unusual for me.

Speaker 1 Like, I'm engineered by training myself, and I'm, I'm like, you know, and naturally trained to question. And like, there's a lot of self-doubt in my mind.

Speaker 1 So I don't know what happened to me when we started clean because they were all these people saying like, no, not do it. And somehow they couldn't like, you know, they couldn't actually discourage me.

Speaker 1 Like, you know, I just felt that this was an exciting problem.

Speaker 1 I knew everybody in the world has this issue. Like, you know, even at Google, like it was a big joke, you know, always we had internally.

Speaker 1 Like, you know, all of us were spending all of our time making it easy for people to find things, but not us internally at Google. It was super hard to find anything inside the company.

Speaker 1 So I think I somehow like found that conviction. I was sort of being lazy, not willing to go into the details and

Speaker 1 look at all those priors and just like, you know, just do it, just solve it. I mean, I think that's what I think worked for us in this particular case.

Speaker 1 I feel like Glean had like three big components to it that all came together that you mentioned earlier, right?

Speaker 1 There was the need that you identify it just as somebody running IT for your own company. And to your point, it goes back to Google that this was a need.

Speaker 1 And every company that I've talked to has always wanted to build search and directories and all this stuff.

Speaker 1 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.

Speaker 1 And the third thing was the big shift in terms of the underlying technology, right? The shift in terms of what is capable of search, these foundation models, these embeddings, et cetera.

Speaker 1 Given the latter two,

Speaker 1 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?

Speaker 1 I think for us right now, the focus remains on the two core products that we have. So we, you know, the way we think about our company is that we have this really powerful end user

Speaker 1 AI assistant that helps every person

Speaker 1 work differently in the future.

Speaker 1 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.

Speaker 1 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.

Speaker 1 Come to Glean, ask any questions or give it any tasks.

Speaker 1 Glean will use all of the world's knowledge and all of your internal company's data and knowledge in a safe and secure way and answer those questions for you or complete those tasks.

Speaker 1 But actually I just promise to you that Glean does everything. You don't have to work anymore.
We're long, long ways from actually even solving

Speaker 1 the pitch that I just mentioned to you.

Speaker 1 I think we have to understand

Speaker 1 knowledge properly. We have to pick the right, the correct information, throw away the old information.

Speaker 1 There's so many challenges challenges there there's so many issues uh people talk about hallucinations as a big problem with ai models 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 uh 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 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, of wanting to do something different.

Speaker 1 Like, you know, like, just solving this one problem itself is a big, is a big, big

Speaker 1 success. So, so we're going to stay focused on these two, you know, these two products.

Speaker 1 But then they're also like, you know, talk to talk to you about a little bit about the vision for the future.

Speaker 1 So I think the way we all work, it's sort of well accepted that AI is going to change everything. AI is going to change how people work.

Speaker 1 AI are going to actually change how businesses actually even look and feel, you know, what kind of workforce you have in the future.

Speaker 1 And one thing that's going to fundamentally happen is that each one of us is going to have this amazing team

Speaker 1 of,

Speaker 1 call it assistants, coworkers, coaches that are truly personal to you. And you're always surrounded by that team.
And this team knows everything about you, your work life,

Speaker 1 what you need to do today. And it proactively helps you, does 90% of your work for you.

Speaker 1 and also like you know help you get better like you know at your at your you know like upskill you um be a coach um and this and that's the world that we're going 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 or chief of staff you have an exec team you have a coach uh 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 a new grad joining a work joining the workforce that's what we are trying to actually um go and solve for we're trying to actually build that amazing personal team around every individual um that's going to make us all a a 10xer and that's just a natural extension of like just keep evolving our clean assistant product make it better and better over time yeah arvin thanks so much for joining us today yeah that's excellent yeah fun fun questions it's always nice to see you yeah likewise

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