How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work

Years before co-founding Glean, Arvind was an early Google employee who helped design the search algorithm. Today, Glean is building search and work assistants inside the enterprise, which is arguably an even harder problem. One of the reasons enterprise search is so difficult is that each individual at the company has different permissions and access to different documents and information, meaning that every search needs to be fully personalized. Solving this difficult ingestion and ranking problem also unlocks a key problem for AI: feeding the right context into LLMs to make them useful for your enterprise context. Arvind and his team are harnessing generative AI to synthesize, make connections, and turbo-change knowledge work. Hear Arvind’s vision for what kind of work we’ll do when work AI assistants reach their potential. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 00:00 - Introduction 08:35 - Search rankings 11:30 - Retrieval-Augmented Generation 15:52 - Where enterprise search meets RAG 19:13 - How is Glean changing work? 26:08 - Agentic reasoning 31:18 - Act 2: application platform 33:36 - Developers building on Glean 35:54 - 5 years into the future 38:48 - Advice for founders

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Majority of the work that we do today is not going to be done by us anymore in five years from now. And that applies to me, that applies to you, like, you know, we both do very different things. But still, like, I think, you know, we are knowledge workers. And I think a lot of our work is actually going to be done by these amazing AI assistants. That are actually, in many ways, you know, more powerful than us, like, you know, the they have access to all of our company's data, our knowledge, they have all the context from all the past conversations and meetings. They don't forget anything.

• 0:32Speaker 00

  And they can really sort of, and they have the, you know, on top of that, you know, they have the reasoning capabilities, you know, that allow them to be super helpful to you and like any task that you do. So that's sort of, you know, our core belief, like, you know, that majority of our work is actually going to be done by these AI companions or assistants. And we want Glean to be, you know, that, you know, that assistant, you know, in the workplace.

• 1:08Speaker 01

  Joining us today is Arvind Jain, co-founder and CEO of Glean. Earlier in his career, Arvind was instrumental in building Google Search and co-founder and CTO of Rubrik. Glean began life as an enterprise search company and today has evolved into a general purpose work assistant. Bringing AI into an enterprise context is notoriously difficult because of the integrations, the permissions, the ranking, the parsing, all the other magic that needs to happen to make AI work on your company data. Arvind joins us today to share how Glean is solving this problem where other companies have failed and what he's learned as one of the first successful AI-native application companies.

• 1:48Speaker 01

  Arvind, thank you so much for joining us. We have a lot of questions about RAG and agents and knowledge graphs and all of that, but before we do that, can you give us one or two minutes on what is Glean and what are you building?

• 1:59Speaker 00

  Yeah, first of all, thank you for having me. Glean, think of it as the Google or ChatGPT, but inside your enterprise. It's a place where your employees go and ask questions. And Glean answers all of those questions using your company knowledge. Regardless of where that knowledge is, brings it all back to you. So that's what Glean does. Glean is also an AI platform. So if you want to actually build, you know, AI applications inside your company, you can use the Glean RAG platform to build those applications quickly.

• 2:32Speaker 02

  Wonderful. And since you make the analogy of a Google for work. Google for work, I think, is something that every CIO has described as their holy grail. And we have two decades of failed attempts at building it. You were actually a star search engineer at Google before. And even Google never managed to crack this category before. Maybe, can you just say a word on why is this such a hard problem? And how did you do it?

• 2:58Speaker 00

  Well, I mean, search is hard because it's actually magic in some sense. Like you can come and ask any question that you have. And you expect the system to actually give you back the right answer. So expectations are always high. And it's a difficult problem, like especially in the enterprises. Because, you know, there's so much information inside the enterprises, spread across so many different systems. It's both hard to actually even get hold of that information. But then even harder to actually make sense of what information is actually good, what has become out of date. So, you know, there is like, you know, lots and lots of challenges around, like, you know, building that, you know, system. And, and in the past, like, you know, I would say, I would say that like, you know, there were no good attempts made.

• 3:43Speaker 00

  Because, you know, the problem was so, like, it was so hard. It requires so much R and D, so much investment, like, you know, it was not really startup friendly in many ways. And, and you also like couldn't even, you know, in the pre SaaS world, you couldn't even build a product. Because, you know, just getting, connecting with all of your enterprise data meant that you had to spend like a year sitting with an enterprise, trying to actually bring that data into your search system. And then actually, you know, solve the real problem, which is like, you know, make that information searchable.

• 4:12Pat Grady?

  Arvind, one of the things that I think is so interesting about Glean is you are probably one of the first and best examples of what an enterprise AI application company.

• 4:23Pat Grady?

  Can or should look like and and we're going to focus most of this conversation on the AI aspects of Glean. However, I know there are a lot of layers to your stack. You've got the infrastructure, you've got the connectors, you've got the governance engine, you've got the knowledge graph. Can you say a couple of words about all the stuff you had to build before you even got to the AI part to make the AI work?

• 4:42Speaker 00

  Absolutely. So as you said, like, you know, search starts first with the data and the knowledge that need to actually make searchable. So the first part of the Glean tech stack is these deep integrations that we've built with most common enterprise systems. So think of systems like Salesforce or Confluence, Jira, Google Drive, SharePoint, ServiceNow. Like your enterprise data typically lives in all of these different systems and you need to bring it all together in one place. So that's that's the first part of our technology stack is these integrations. But then if you think about like enterprise data, and this is one of the most unique things about enterprise search versus versus

• 5:24Speaker 00

  if you think about Google search on the web, is most of our enterprise information is actually private in nature. When you author a document in Google Drive, you know, like this document may actually be private to you, or you may share it with a few other people. And so you can't build a search engine which, you know, where you just dump all the all the company knowledge and make it accessible to everyone. You have to actually understand permissions of each content. So when you go and search, the system should understand who you are and only retrieve information that you actually have access to. So that's our governance layer, understanding governance across all of these hundreds of different systems, which is quite complicated. And then the third part, and this is where really most products have failed in the past, is search is not about just putting like

• 6:09Speaker 00

  a whole bunch of documents in an index and and then like, you know, when somebody comes and asks a question, take those words or take that question and just match it up semantically or with keywords, you know, with the right content. You've got to actually also understand who's the person who's asking a question. You know, I can come in and ask for an onboarding guide because I'm new as I'm a new employee. But then which onboarding guide should be actually, you know, given to me like it depends on like, you know, whether I'm in the marketing team or I'm in the engineering team. So so sort of understanding people and understanding knowledge and relationships between them. You know, that's a big part of actually making, you know, a search or a question answering service work inside an enterprise.

• 6:52Speaker 00

  So we we do that. So we actually build a deep knowledge graph, you know, where we all look at all the employees, understand what roles do they play in the company. Look at all the documents and then just sort of try to understand what documents are meant for what departments, what documents actually are popular. Um, you know, is there like, you know, relation, like, what are the relationships between, you know, and, you know, a particular individual and a particular document? And and that is what we used as a core foundation that sort of then governs like when somebody comes and asks a question. What are the most relevant pieces of knowledge for them? So we had to do all of that work. Um, and actually, like, interestingly, you mentioned like what came, you know, before AI even became relevant. You know, for us, the AI was actually part of the core search technology from day one.

• 7:36Speaker 00

  We were actually working with L L M's in twenty nineteen. Um, or I mean, at least language models, like, you know, these were, you know, bird-based language models that you could use. And so when glean.

• 7:46Speaker 02

  M L M's.

• 7:47Speaker 00

  That's right. Is that is that the name for them now? But yeah, you know, in in the in the in the search, like, you know, engineering sort of uh like a community, like, you know, we just called them language models at the time. And so the language models were actually part of the core search experience, you know, from day one because like it really allowed us to understand content content at a semantic, you know, level. So that we sort of already baked in like in our core search experience on day one, when we're actually trying to look at a question from a user. Um, we were never sort of limited by the actual exact keywords that, you know, users use. We were able to actually understand the meaning behind the question and actually match it up with the right documents.

• 8:27Speaker 00

  But but still, like, you know, that's sort of like, you know, all the work that you have to do before, like, you know, you can actually even, you know, do anything with L L M's.

• 8:34Speaker 02

  Can you say a word about rankings? Like, I think part of the what makes Google work so well is like, you know, I always get the answer I want at the top, top of the page. You know, in the case of the public internet, you have so much of web data and uh links and all that to to make the rankings really work. Um, how much, to what extent is that part of the magic for glean? And how do you guys do it?

• 8:55Speaker 00

  Yeah, so that's of course the core of the product is all the effort that we put in to actually build a really good ranking system for our search. The, I'll give you some examples of like, you know, the kind of things that go into determining what, what documents are the best ones to rank for a given question. So of course, if you imagine that there's a document that, you know, people in the company are constantly looking at. So that, you know, that gives you obviously a signal that there must be something about it, like, you know, it's actually important, people like to spend time on it.

• 9:29Speaker 00

  If there's a, if there's a document that was actually written in the last, you know, one or two weeks and there's some engagement around it, you know that, you know, this again, like, you know, is information that people care about, it's not actually become obsolete yet. Then if you think about the, a particular, you know, document that we see is not popular, like, you know, when you think, you know, look at a company level, but you look at a particular, you know, one individual team inside the company, we see there's heavy usage for that document inside, you know, inside that particular team. So that sort of tells us more about that, hey, this document may actually be relevant for this particular, you know, you know, set of people. Or the last thing, the last example, imagine that, you know, somebody had a question and

• 10:14Speaker 00

  they're not like bothering to a search, they go on Slack and ask a question, and then somebody else posts a, posts a link to a document as a response to that. And the, the person who asked the question gave, you know, gave a thumbs up to it. Just, you know, you know, imagine this interaction, like what it means. It actually means that, that particular, particular document was actually a really good answer for that question that the user had asked. And so if you keep that association in mind, like it's going to help you later when somebody else came and asked a similar question. So those are some of the signals. You have to sort of constantly look for all these signals, you know, they have to collect them differently in the, you know, in the enterprise setting than in, you know, on, on the web, like, you know, Google only has to look at all the activity that's happening

• 10:59Speaker 00

  right on Google itself, because that's the gateway to like, you know, any sort of knowledge quest. But if you look at In the enterprise, you know, not all the things are happening, you know, through the search paradigm. So you have to sort of go and look at all the activity around all the knowledge in different systems, like, you know, your communication systems, your document systems, and just try to learn from that human behavior. Because ultimately, that's how you learn. You learn from what people are doing inside the company. The more you, the more you can actually collect, you know, that information, the better your ranking systems are going to be.

• 11:30Speaker 02

  Can we spend a minute on RAG? As Pat mentioned, you were kind of in the right place at the right time. You had put together all the hard stuff so that when, you know, when the LLMs got really good, you kind of had all the infrastructure in place. And I think you've been one of the experts in, you know, using RAG to make these LLMs actually useful on your corporate content. Can you, can you explain RAG to me like I'm five years old? And like, what are the secrets to making it work? What are the things that people don't talk about? You know, what are examples of things that you can do thanks to RAG that you can't in a generic chat interface?

• 12:07Speaker 00

  Yeah. Well, so first of all, I think since you were talking about a five-year-old, let's first talk about what RAG is.

• 12:13Speaker 02

  Okay, ten-year-old, ten-year-old.

• 12:14Speaker 00

  So, yeah. No, let's keep it five. I need five. Let's start with five. Let's start with five, yeah. So, so, I mean, like, if you think about all these amazing models, you know, in GPT and Gemini and Claude, they're all, these models are all trained on the world's public knowledge and data. And so if you were to actually go and chat GPT and ask a question like, hey, how many days do I get off, you know, you know, with my PTO policy? It has no idea. Like, it can't answer that question because, you know, that's my company's private knowledge. You know, the answer is somewhere there and the model is not trained on it. So how do you bring your private enterprise data, you know, to, you know, to these models so that you can actually have AI create that magic

• 12:58Speaker 00

  for you? That's, that's, that's, that's, you know, that's what you, a RAG-based, you know, AI application architecture allows you to do. So the way it works is that, you know, you come and ask a question and you have a search engine or a retrieval engine, whatever you want to call it. And given the question, this retrieval engine, you know, finds, you know, potentially relevant, you know, documents, you know, that could actually answer your question. And then you're going to take those documents or those sort of content fragments and and make the model work on it. You'll tell the model like G P T that hey, I have this question and I have this company knowledge that I think is relevant. You know, in terms of answering that question, now you answer that question using this knowledge.

• 13:41Speaker 00

  So this is, this is how like most A I applications today are being built in the enterprise. The only way to actually connect, um, you know, your private enterprise data to to the power of these language models is basically a search engine that's sort of sitting and sitting, sitting, you know, you know, in the middle. So, so we like our given, you know, that, you know, at Glean also, like, you know, we of course built a search engine over all of your enterprise content. You know, in the last, you know, five years, it actually allowed us to actually be, you know, so become like, you know, one of the best, you know, RAG

• 14:19Speaker 00

  systems, you know, that allows you to now, not only like, of course, you know, we deliver our own end user application, which is a Glean assistant using this RAG based application architecture, but we're also allowing, you know, um, companies to actually build, you know, more and more applications using RAG.

• 14:37Speaker 00

  Now, I think like in terms of, while like, you know, this is the architecture that is sort of, you know, emerging as the canonical architecture for building A I applications. I think it is still like, you know, full of challenges. It's actually really hard to build great application, A I applications using RAG. Because one of the things, you know, like, you know that how models themselves are sort of while very powerful. They're also still an emerging technology, right? Models hallucinate, you know, they make things up. And what you're doing now is you're actually, you know, adding one more complex layer of technology. You know, you know, you know, in this application architecture, so think of it like you're chaining two things, you know, which are both not perfect.

• 15:18Speaker 00

  So oftentimes, you know, you will see a RAG based A I application not perform well because you asked. A question and the failure actually happens at the RAGS, you know, at the retrieval stage where you didn't even actually, you know, were able to find the right pieces of knowledge. Or maybe you found like, you know, stale information that then you're actually giving to the L L M to work on. And then, of course, it's going to give you bad results. So, so it actually, you know, you know, while the, while it's the only way to sort of bring knowledge together. It, you know, creates these, you know, interesting challenges for you as well.

• 15:52Pat Grady?

  Let me see a question. And just to paraphrase a bit, what you were saying at the start of this conversation, Act One, Enterprise Search, Act Two, Application Platform. For that Act One, which is Enterprise Search, how do the concepts of Enterprise Search and RAG relate? Is one a superset or a subset of the other? Are they similar but distinct? Are they the same thing? How does Enterprise Search and RAG, what, how do those concepts relate?

• 16:20Speaker 00

  So I think like, you know, I think of search and RAG as being, in some sense, you know, they're one and the same thing. The real core technology is taking all of your, all of your knowledge, enterprise knowledge, and putting them, you know, into this, you know, a search system where now you can actually, you know, ask questions. And the system is able to actually, you know, give you relevant pieces of information back. So that's, that's sort of the core technology. Now, you can actually use this technology either as a standalone product. So that's what the Glean search product is, for example, where, you know, people come in, they ask questions and we can give them the relevant documents, you know, that potentially

• 17:04Speaker 00

  are useful to, you know, to them based on that question. Or you could actually, you know, use this as an A P I layer in your overall A I application. So where, you know, the, the search system, the search module now is only one component of your overall, you know, A I application architecture. And the, so, so, so I think in that sense, you know, it's sort of similar. But, but the industry, on the other hand, like, I think like what we've seen, you know, like most of these. Like based applications in the enterprise today. They actually use a much more simpler version of a retrieval system, you know, in their RAG application. Typically like a vector search based system, which doesn't really have full enterprise context.

• 17:50Speaker 00

  And and so that's that I would say like, you know, that's the key difference. Like so for us, like, you know, our approach always has been that like really, you know, you know, think about like how to build a standalone search system, you know, something that is as good that you can actually put them in front of the users as a standalone product. That's really the real test for like how good the search is. And and that then actually, when you, you know, put it behind the scenes in a RAG based application, it's actually going to actually create, you know, better AI experiences.

• 18:23Speaker 02

  So is it fair to say that kind of the magic that you've done in terms of getting a good ranking of search results? That is exactly like you've made those that ranking good for people. It turns out making that ranking good for people is also what you need to make the ranking good for machines in order to get in order to get the best possible results. And that's why like, you know, what you've built is very different from somebody that's just DIYing a data pipeline and their own little retrieval system.

• 18:46Speaker 00

  Yeah, yeah, that's that's correct. I mean, I think I think it's really hard to to build these systems, you know, like yourself and build them in a matter of weeks. I think like I I think like you can build a great AI demo in, you know, in in one day, maybe like in two hours now, but but I think like, you know, to actually build, you know, like, you know, where it's sort of like, you know, it's robust, it's stable, it actually adds value to your, you know, within your enterprise, you know, like, you know, it's a hard problem.

• 19:13Pat Grady?

  So we've talked a little bit about how you've built what you've built. And we know that it's working. We know that the company is quadrupling year over year and we use it here internally. And and there are a lot of happy people out there who are customers of yours.

• 19:26Pat Grady?

  The real measure of success in some ways is how your product is changing the lives of its customers. And so I'm curious to hear from from you. When you look at your customers and sort of how they operate day to day, pre-Glean versus post-Glean, what are some of the changes you notice? How does this help people do their jobs?

• 19:47Speaker 00

  So Glean is actually a product that is used quite heavily by people.

• 19:53Speaker 00

  The

• 19:55Speaker 00

  Like there's, there's like, you know, many, many different types of things, you know, like we're often surprised by what, you know, what people are using Glean for. But I would, I would give you a few examples. So for engineering teams, you know, they find Glean super useful, like in terms of like troubleshooting. Whenever you run into any kind of a roadblock or an issue, like sometimes an error, like, you know, your, you know, programs are not working properly. And so Glean serves as a really good troubleshooting tool for them. Like, you know, it's a place where you go and debug because, you know, you, you post the issue more, more often than not, you're, you're not the first one like who's going to experience an issue, like somebody else has experienced those issues before. So just getting the context from all of those, you know, like all, all the other people

• 20:39Speaker 00

  and like how they solved that problem before, like, you know, sort of helps you move, you know, like solve that issue for yourself. So that's, you know, that's, that's a, that's a big, like, you know, big use case for, for engineering. The, for, for some roles, like for support, you know, their, their life, you know, day in, day out is about resolving, you know, answering, answering people's questions. And, and I think the tool like Glean actually fundamentally just change, you know, change, you know, like how they work now because, you know, they, by default, now they don't think about like given a question, like trying to go and look for like answers in different knowledge bases and whatnot, like, you know, instead like the first, you know, reaction,

• 21:22Speaker 00

  you know, that they have now is that, you know, there's a question that's coming in from a customer and then Glean on the side is actually already answering those questions for them. So, so there's sort of like model of working change, you know, changes from trying to find things to actually trying to validate like, you know, what AI is telling them as is the right answer and then just share that back with users. The, some, some teams are actually like, you know, really change their behavior, like salespeople, for example, they use Glean as a way to prep for meetings. So before like customer call is coming up, they will just ask Glean, like, you know, they can be lazy, they can ask Glean, like, you know, help me prep for this meeting and Glean is actually going to bring, bring that, you know, 360 view.

• 22:04Speaker 00

  View of all of the data from that customer, like what happened in the last meeting. Who's, you know, like what opportunities you know are open, you know, with them and things like that. So it sort of really helps them, you know, prep for a meeting, then actually run a meeting well, you know, because, you know, customers always have lots of questions and so the salespeople feel more confident. You know, like running that meeting because like, you know, if if somebody throws a curve ball at them, you know, they can just like, you know, like just ask clean like right right in the meeting, you know, get the answer and quickly sort of, you know, get the responses back. So in fact, like, you know, you know, in our company of like, you know, we don't allow salespeople to actually bring in sales engineers in the call, like they have to answer those questions themselves.

• 22:46Speaker 00

  So that's like, you know, one change in behavior that we drive, like in our in the in the in the first few calls.

• 22:53Speaker 00

  Yeah, so those are some of the things, but overall, the use cases are unbounded, like the one that I that I think is the the one that's universal across everybody, you know, inside the company is, you know, finding other people who can help you. That is, that is one of the things, you know, that clean makes it really easy for people, like, you know, we we help you connect, you know, you know, with the right subject matter experts, you know, based on what questions you have. So that's that's one thing that I that we see every, you know, everybody in the company, you know, make use of a lot.

• 23:19Speaker 02

  Is there a North Star metric you track? Like, these are these are wonderful kind of stories of of customer impact. I guess, how do you benchmark yourselves objectively?

• 23:28Speaker 00

  Yes, our our key metric is how many questions people ask on a daily basis and actually get successful, like, you know, we were successful in answering those questions correctly for them.

• 23:40Speaker 02

  So similar to like Google's like search sat metric, then like, okay.

• 23:45Speaker 00

  Yeah.

• 23:45Speaker 02

  Can you can you share anything about those numbers or you prefer to keep it private?

• 23:48Speaker 00

  Well, we have, so yeah, so we have this technical metric. I don't know how how much sense it's going to make, but they we tend to actually keep that number at eighty percent. So I think it's a proxy for that the eighty percent of the sessions, you know, that users had with us, they were actually successful in, you know, getting what they needed.

• 24:04Pat Grady?

  And is it, do you measure that success? Is that explicitly they thumbs up this was good? Or is that implicitly they take action on the basis of the results that you served and you can see that action taking? How do you, how do you actually measure the success?

• 24:19Speaker 00

  It's actually implicit. So the, we will track like, you know, their actions. For example, in search, when, when you, you know, come and ask a question, and then you click on one of the top two or three results, and, you know, go to the destination and then stay there for a long time. So that sort of gives us an indication that, you know, you are happy. Or you didn't come back and like ask another question quickly or refine your search. So that's sort of how we track, like, you know, whether somebody is successful or not.

• 24:46Pat Grady?

  Got it. What are, what are some of the top things that are not in the product yet that you think will make people more successful?

• 24:52Speaker 00

  I think the, like I was, I refer to this, you know, when we started that building a product like ChatGPT or Glean, it's sort of like magic. Like, you know, the expectations are infinite. And because, you know, like supposed to basically not just answer any questions that people have, but also like, you know, perform like any task that they actually ask you to, you know, to do. And so, so for us, it's not so much about what features are missing. Like, you know, like the, the big thing that we have to actually keep working on is actually be successful at this, at this core feature, which is like, you know, answer people's questions correctly and answer questions of like higher and higher complexity correctly for them over time.

• 25:34Speaker 00

  So, so we, we feel like, you know, like us or anybody else, like, you know, out there today, we're all very, very far away from that true vision for our product, which is that we want Glean to be that AI assistant that can actually, you know, answer any questions that you have using your company knowledge, that can actually do half of your work for you in the future. And so I think, I think like I would say, like, you know, maybe 2% of the way there. Like, you know, AI, AI for all said and done, like, you know, we're still in very, very early stages of like making, you know, that impact.

• 26:08Speaker 02

  So we're only 2% of the way there. I'd love to ask you actually about agentic reasoning. It's something that's been on our minds a lot as a partnership at Sequoia, and I know it's been on your mind as well as a founder. And one of the results that I was really impressed by in the coding space was that, you know, with RAG, I think these coding agents can get to 3%, 4% completion rates, but if you give them more agentic reasoning capabilities, they can get to 14%, 15%. So like a multi-fold improvement and, you know, it's as simple as. You know, go reflect on what you just said, or best event, or, you know, what are the techniques are. I'd love to, I'd love to understand how you guys are thinking on incorporating more agentic reasoning into your products.

• 26:55Speaker 02

  And, and anything else to kind of get us from that 2% where you said we are today to, to what you hope to build one day.

• 27:01Speaker 00

  Yeah. And, and I want to clarify that 2% is something that I'm, I was making up, like as, you know, it's not a measured number.

• 27:08Speaker 02

  Yeah, yeah.

• 27:08Speaker 00

  I just wanted to sort of, sort of, you know, you know, express like, you know, like how, how early, you know, things are today and, and, and how much, how much amazing things we're going to see in the future. Like, I was just basically, you know, trying to, you know, talk more about that. But in terms of like, you know,

• 27:25Speaker 00

  agentic,

• 27:29Speaker 00

  sort of like, you know, behavior. One of the things, you know, that we are doing on that front is first, try to actually get a lot of input from our users. So we have, we have a concept of building a workflow

• 27:49Speaker 00

  inside Glean to solve, to actually answer a complicated question. And, and today, we actually seek a lot of help from our users in sort of, you know, completing, you know, that, you know, that workflow. We'll actually, like, say, for example, if you come and ask a question, like, you know, help me write a weekly status report of all the work that my team did. So this is your question. Now, if you think about this question, like, you know, it's, it's, it's complicated, like, you know, there are, there are a few things you need to do to actually go and really figure out the answer to it. The first thing is you have to actually understand, you know, what do you mean by your team? Like, who's your team? You have to maybe go in your, you know, HR system, try to figure out, like, you know, who are the people who report to you?

• 28:32Speaker 00

  Then we are talking about work, like, you know, like, what, like, so where does work happen for each one of these team members? You have to sort of build an understanding of that, like, you know, and then go sort of pull, pull a bunch of knowledge from all these different systems. So, so I think right now what we're doing is we're actually trying to actually get help from our users.

• 28:52Speaker 00

  And we will sort of create a plan for a complicated question, try to actually get the user to actually input and tell us like, you know, are we getting it right? Sometimes, you know, users can actually explicitly like they can completely ignore what we do and just build a workflow on their own. And, and, and I think that's going to be essential for us to like build that, you know, fully agentic behavior for the future. I think some, you know, you can build agentic behavior for like specific narrow set of problems. But in Glean, like, you know, since our, like, you know, our sort of footprint is so wide, like, you know, you know, there is the range of questions that people can have. The range of tasks that they want to perform is so broad that we feel like, you know, first we have to learn, we have to learn from like, you know, workflows that people are going to actually create manually.

• 29:38Speaker 00

  And, and then like, you know, build these models which can then sort of take complicated questions in the future and automatically build those, convert them into these sort of like agentic loop or, or a complicated, you know, workflow. So that's, that's the approach that we are taking, you know, on it.

• 29:55Speaker 02

  So you're saying since you have such a broad surface area, you can't build agentic reasoning for every single possible task. And so instead you're exposing a workflow engine for your users to individually be able to build different automations and different agents.

• 30:09Speaker 00

  Yeah, and then you learn from it. And then you learn from it. So once, you know, you see people building these workflows, that sort of, you know, then fits as, you know, goes into like, you know, a training data set to sort of allow you to actually automatically build like new workflows, like based on complicated questions that people have. So, so those agentic capabilities are coming, you know, but, but I would, again, like, and I would say the, like, you know, when it, when it is, you know, hard for you to answer simple questions, then if you want to do complicated tasks, like, you know, it's equally hard because, you know, you're going to make a mistake and imagine an agent that actually, you know, breaks down a complex task into ten, into a series of ten individual tasks.

• 30:53Speaker 00

  Then your error rate is going to compound like, you know, if you have, you know, if each, each step, you know, is 90% accurate. So, so there is, there is like, you know, it is, it is incredible, but I think it is still, you know, something that, you know, we are like, I, I, I feel like, you know, the human assist is actually critical, like, you know, in building these like complicated workflows.

• 31:18Pat Grady?

  It's also, Arvind, it might be worth saying a word, maybe this is obvious to people who are listening, but just to state explicitly, how Act One, which is the enterprise search business, gives you the moral authority or the unfair advantage to get into Act Two, which is the application platform or the platform for agentic behavior. It may not be totally obvious to people how Act One leads to Act Two. Can you just say a couple of words about that?

• 31:44Speaker 00

  By building the search product, which immediately adds value to our customers, to our users, you know, we are able to actually solve, you know, a bunch of complicated problems that you will typically run into in enterprise. The first part of that is, you know, security. So if you think about like the Glean product, you know, we are actually telling our customers that, hey, give us all of your data and, you know, and we're gonna hopefully do something useful for you after you give that data to us. Like, and that's a, that's a, that's a big demand, like, you know, that's, you know, it's not easy, you know, for companies to actually trust, like, new, you know, a new

• 32:28Speaker 00

  product company, a startup, and with all of their data, when they're actually not, you know, getting any immediate value from it. And so that's, that is one of the things, you know, that, you know, we've seen to be, you know, super helpful to us, because we actually have this search product that people understand that people want, and they want to deploy. So it's already deployed now. And so Glean is running and it's already connected with all of your enterprise data inside the company. And so then, like, you know, helping, you know, like, you know, us going to our customers and then saying them that, like, look, you know, use that as your core, core AI data platform is a much easier sell, because we don't, we're not actually have to convince them again to, to actually, like, you know, to, you know, give all of their data to us. It's already there.

• 33:10Pat Grady?

  I see. This, this might not be a perfect analogy, but hopefully it's not a terrible analogy, that, you know, Tesla had an advantage in self-driving because they're already selling cars. You guys have an advantage in delivering AI agents, because you're already selling a data platform that organizes all the enterprise information, makes it accessible, makes it secure, kind of puts people in a position where they're already asking questions of it, and it's kind of a logical next step to ask it to start taking actions.

• 33:33Speaker 00

  Absolutely.

• 33:34Speaker 02

  I think you also announced a set of APIs that let developers build on Glean. Maybe say word on that, like I think that was in response to customer demands. What makes developers want to build on Glean versus directly access, access their own data? I think it's probably a similar effect to what you just talked about.

• 33:49Speaker 00

  Yeah, so like a lot of like AI applications that our customers are wanting to build, they, they need to actually tap into data that lives in like, you know, multiple different like SaaS, you know, cloud-based SaaS systems. And, and like, I think it's quite tedious for them to like first go and actually bring that data, like, you know, in one place, you know, build a, build a search or retrieval layer using that. The, you know, the integrations are hard. Understanding permissions and governance is really, really hard on that. And, and I think like when people, like when, like as, as sort of these models actually became accessible

• 34:31Speaker 00

  and, you know, and developers started to actually develop AI applications, they realized that the, well, like they were really excited about building these new cool AI apps, but, but basically they realized that, you know, building an app, you know, the 90% of the work was actually, you know, you know, this sort of boring infrastructure work that they didn't want to do. Like, you know, bringing data from all these different systems, running these ETL and data pipelines, you know, and, and then sort of building a good search over it. And like, you know, so you'll spend like so much time before you actually even get to play with AI. And, and so that's, that's, that's the thing that, you know, that they find, you know, very useful with Glean because, you know, we're actually solving all, you know, all the problems around ETL, building a great search, properly obeying governance within your company.

• 35:16Speaker 00

  All of that stuff is done for you, like, you know, you just have a, your search API and, and you can sort of focus all of your attention on, on the business problem that you're working on and how AI can help you sort of like, you know, achieve that automation that you're looking for.

• 35:31Speaker 02

  In some ways, all the hard work you've done to ETL and put all the data together with data government governance reminds me a lot of Snowflake. And you're really doing it with like text data and unstructured data, but kind of just that central data platform that companies can build around, build apps on top of, reminds me a lot of the Snowflake story.

• 35:52Pat Grady?

  Yeah. Arvind, can we ask you a question about future state? If you allow us to dream for a few minutes, five or 10 years from now, how do you think Glean is showing up inside of a business? And maybe more importantly, if you're the typical knowledge worker five or 10 years from now, and you are equipped with Glean, what is your life like then?

• 36:14Speaker 00

  That's a great question. I think, let's keep it five years instead of 10. And I think the, well, I mean, like, you know, one belief that I have is that majority of the work that we do today is not going to be done by us anymore in five years from now. And that applies to me, that applies to you, like, you know, we both do very different things, but still, like, I think, you know, we are knowledge workers. And I think a lot of our work is actually going to be done by these amazing AI assistants that are actually in many ways, you know, more powerful than us. Like, you know, they have access to all of our company's data, our knowledge, they have all the context from all the past conversations

• 36:55Speaker 00

  and meetings, they don't forget anything. And they can really sort of, and they have, on top of that, you know, they have the reasoning capabilities, you know, that allow them to be super helpful to you and like any task that you do. So that's sort of, you know, our core belief, like, you know, that, like, you know, majority of our work is actually going to be done by these AI companions or assistants. And we want Glean to be, you know, that assistant, you know, in the workplace. You know, we want Glean to be, you know, the place, you know, where most of your work happens. One of the things, you know, that we also think, you know, is going to change is today, a lot of AI is about, is about, like, you know, you go and seek help from these AI agents.

• 37:41Speaker 00

  The you you you know, like, for example, you go and ask questions, you get answers back. But but the future is sort of future is where, you know, this assistance is going to be proactive. It's sort of like, you know, if you think about like, you know, if you have an, you know, an executive assistant, you know, they actually help you a lot.

• 38:02Speaker 00

  A lot of their help is, you know, when you go and ask them for help, but a lot of their help is actually proactive in nature. They tell you what to do next, you know, they manage your day, they know everything about your work life and and and they guide you like, you know, to be effective, you know, throughout the day. And and I think like AI is going to actually allow that luxury. You know, you know, like regardless of, you know, who you are like in today, like, you know, some executives in the company, you know, have that luxury, but in the future, like, you know, everybody's going to have these really powerful, you know, AI based assistants, you know, that are going to actually help them do their work. So so that's how they know. So we're really excited about bringing that change to the workplace and and and hope that, you know, Glean can be that, you know, the the the world's most successful AI assistant.

• 38:46Pat Grady?

  Love it.

• 38:47Speaker 02

  Arvind, can we change gears a little bit? And I'd love to step back and hear your advice for other founders. You are one of the most successful application level AI companies. I think probably number two behind Copilot and Scale, and you did it as a startup, as an independent startup.

• 39:03Speaker 02

  I think you've also had to navigate some unique challenges, right? Like OpenAI, for example, is one of your providers and also one of your competitors, one of your top competitors. Maybe just tell us what it's what that dynamic is like.

• 39:15Speaker 00

  Well, I mean, first of all, the from a point of view of like building a startup.

• 39:22Speaker 00

  In fact, like, you know, I've been actually quoting you guys, you know, in many places, like, you know, Pat, I remember, like, you know, the slide where you talk about like the overall software market being $600 billion. But then AI sort of is, you know, expanding that market to like, you know, 15 trillion or 12 trillion, something massive. And and that's actually like, you know, the the reality of, you know, where we are today, you know, which is that, you know, everything that we do is going to actually change, fundamentally change. AI is going to be a key component, you know, that's going to drive that change. So, so first thing that I like, you know, as a founder, like, you know, I don't actually worry about what other people are actually working on. Because even if all of us are working on a lot of great things, you know, it'll still not be enough.

• 40:07Speaker 00

  It'll still not be enough to actually solve all the problems that need to get solved. And so that's the first mindset. So like, you know, like, I think from a advice to other founders, that's, that's the, that's the thing I want to tell them. Like, if you found a problem, you know, just go, you know, work on it and don't worry about like if somebody else is solving it. Because like the chances are that other people are not, and they won't solve it the same way as you will. But for, for us, like, you know, and, you know, coming back to like Lean specifically, like, you know, you know, the dynamics for us. You know, we felt the same way, like, you know, for, for the first four years of our existence, we were working on a problem. You know, where we had no competition, you know, nobody, nobody was actually interested in solving the problem that we were solving.

• 40:49Speaker 00

  It was, it was a dead market and, and we had to create a category, we had to actually generate interest, you know, be evangelical. And, but, but we knew that we were working on a, an important problem. But then, you know, suddenly, you know, chat G P T happened and, and searches become hot. And now, in fact, every company that you go and talk to wants to build a product like Lean. And so is that good news for us? Is that bad news for us? Like, how do you think about it? You know, from, from, from our perspective, like, it doesn't matter, like, you know, the way we feel is that it's actually great news for us. Like now, everybody is interested, everybody wants to buy our product. And yes, we have to compete with many, many other vendors.

• 41:29Speaker 00

  But, but that's, that's the place where we think, you know, we'll win because, you know, we will, you know, like, we have the desire to actually solve this problem. And, you know, stay focused on this problem, keep working on it, and there's no reason for us to not do a better job than others.

• 41:44Pat Grady?

  Part of what I heard in there was that building an AI company is just building a company. Find an important problem and solve it in a compelling way. I'm curious, particularly because this is not your first rodeo. You know, Rubrik was obviously wildly successful and of course you were pretty core to some of the early days of Google. How much of building an AI company is just building a company versus things that are AI specific in some way?

• 42:09Speaker 00

  So, good question. I, I think like, like I think of AI mostly as a tool, you know, in your arsenal. And one of the tools, I don't think, you know, you suddenly become a different company because you are, because you know, you're doing something with AI. I mean, in, in reality, I think there's going to be no new company that is not going to use, you know, AI technologies in some shape or form. So my point of view is that like, look, you have to actually find a business problem that, that you're planning to solve. And hopefully, like, you know, the, you know, that, you know, like you can actually solve that problem in a much better way.

• 42:52Speaker 00

  Because of like, you know, the technology that AI is actually providing to you now. So I don't, I don't think it actually changes. I don't think it actually feels different. Like, you know, whether you are, like, we don't think of ourselves as an AI company for that matter either.

• 43:07Speaker 02

  Would you ever train your own models? Like, and I guess maybe more broadly, how do you think about where Glean's core competencies start and stop? And, you know, if you have a hundred R and D chips, where do you want to place them?

• 43:17Speaker 00

  We, we don't have plans to train super large models. The, but at the same time, you know, we've actually, you know, we, we do train models which are smaller in size. You know, for every, you know, individual customer of ours.

• 43:34Speaker 00

  The, you know, the, these language models that we train for an individual customer, it sort of goes through all of their, their own, their, their own enterprise corpus and sort of starts to understand, like, you know, the sort of the lingo, the speak, you know, the acronyms, the code names, all of that stuff, you know, you know, within their corpus. So, so model training is actually, you know, like a core part of the Glean core technology, but, but not in the sense of like, you know, you know, training a model like GPT-4. You know, we don't do that. We don't have plans for it. We plan to partner with a lot of other great companies, you know, that do, that, that build models of that scale.

• 44:09Speaker 02

  Wonderful. Arvind, thank you so much for joining us today. This was a wonderful conversation. We really appreciate it.

• 44:16Speaker 00

  Thank you for having me.