Hey everyone and welcome to Generative Now. I am Michael McDano, a partner at Lightseed and this week we're revisiting a conversation with Arvind Jain from a few months back. Arvind is the founder and CEO of Glean, one of the most sophisticated AI workplace assistants that's now a massive player in the enterprise search space. Arvind is an alum of Microsoft and Google and he previously co-founded Rubrik which he took public in the high-stakes world of data security. Arvind and I had a great conversation about the early days of Glean, how he sees AI shifting the way we work in the future and what's next for the company. Check it out.
Hey Arvind, good to see you.
Glean has been, you know, an obvious success story in the world of AI and generative AI. The interesting thing about Glean is it seems to have started well before the current wave of generative AI and the AI explosion that we've now been going through really since kind of the launch of ChatGPT. I figure a good place to start would be to hear the story of Glean and how it got started in 2019, you know, from your journey at Rubrik to starting this and like really what you saw before others did before the AI wave took off.
So we started in early 2019 and at that time you know we didn't have you know that much of an idea of how AI was going to take over the world like it has today. But the problem that you know we wanted to solve was an important one. We started with this vision of building a really powerful search experience inside businesses for people in their work lives. Before Glean I was one of the founders of Rubrik which is an enterprise data security company and one of the things you know like at that company like we grew very fast we were very successful but as the company grew like you know as we sort of crossed thousand employees we started to see like you know big drop in productivity like across all of our different teams and part of it is like you know you sort of like take it for granted that yeah like you know as things you know as companies get bigger like you know like things become more complex and slower but I thought I felt there was more to it like you know we were actually seeing very steep drops in productivity and I think part of it was caused by this issue of the company has so much knowledge so much data you know spread across so many different systems and also like you know as the companies grow fast like you know people's roles like you know it's not always clear like you know who works on what who's an expert who can help me on any given topic and these were actually the largest complaints that we were seeing in our pulse surveys at Rubrik people were complaining about these like people were saying that you know I'm not enabled I can't actually get my work done because of you know these issues and so me being a search engineer like you know before Rubrik I spent over a decade of my career building you know Google search I said like you know well you know people are complaining about not being able to find things and well we should go and you know buy a search product like you know that can connect you know our enterprise data that's fragmented and spread across all these different systems so that you know it's easy for people to find things and also it's easy for people you know people to find other people who can help them you know do their work so we just had to you know that was sort of the origins of we're trying to solve a problem internally you know at our company and that's when I realized that there's no good search product that we could buy and so we wanted to go and solve that and one of the interesting things is that in 2018 the in the search industry like you know among the search engineers we were already seeing the power of the transformer based you know neural nets and these models like you know the there were some language models you know they're small like you know the BERT family of models that were already out there and in Google search we were seeing like you know how you could take a you know every like you know component of the search technology and you could actually build a new system using these transformer-based models and they would actually perform better than like you know these you know previous generation of models that we built over last 15 years. And so we knew that transformers was going to completely change how search works. You can actually do search at a much more conceptual and semantic level and so that was an opportunity to actually also leverage you know leverage and build a really good product in the industry. So that's how we sort of got started. In fact, like Glean became the first company, I believe, you know, that has brought the transformer technology to the enterprise in that sense.
It's so cool and it's such a great insight. I mean, you know, I even relating to my own experience. I was building a small startup, you know, got it to a couple dozen employees and then we sold to a much bigger company and we show up at this big company and there's thousands of employees and there's all this context that we're missing and you go and grab it in Slack or in email or in Google Drive or in any any number of these Confluence, Okta, but it's all disconnected and there's no way to sort of bring it back together and bundle it and it sounds like that's the opportunity you saw. I mean, even though you saw that this opportunity was coming for LLMs, I imagine that you weren't necessarily leveraging them from day one in the product. So, like how were you tying all these systems together that I just mentioned, like what was the actual technical approach to connecting all of these endpoints?
Yeah. So, so like if you think about the search stack, so number one, like to be able to search over information, you need to have access to that information. If you look at a modern enterprise like most of their and most of their systems where their knowledge and data is a SAS like cloud hosted system and typically like SAS systems interoperate well with each other. They have good APIs available to them. We started to build these integrations, you know, with products like Confluence and Jira and Google Drive, you know, SharePoint, whatnot, right? And all of these integrations were built using, you know, the already published APIs, you know, that these systems exposed to us. You can actually connect with these systems. You could see all the data inside them. And then you can bring it together in one common search system. So that was the first step of building the technology was building these integrations. But you have to also understand that like enterprise you know search is very different from search on the web on the internet we can all access all information you know everything is available to us in an enterprise like information is privileged it's secure like there are certain documents you can access but then there many others that you can't so when you actually build a search product you have to actually build a safe version of search you can't actually start to leak information to people from this search experience so you have to sort of think about the problem differently you to figure out how you going to actually solve you know secure you know permission aware access like you know within the search results. So we had to sort of build a lot of security infrastructure to actually handle that but to actually you know on your question about transformers actually the transformer models were part of our first version of the product like it was from day one.
So from day one well because you know the technology was in front of us you know we saw these models were so powerful and you can build a much more smarter search you can actually come in and you can ask questions in natural language and we could actually match it much better at a conceptual and semantic level you know to those documents in Confluence or Google Drive as opposed to doing the brittle keyword based search. So it was a great technology and it was available to us. So what we did there was we took BERT which was you know a model that Google had put in open domain for startups like ours to use. And then we would take that model and then for every enterprise customer of ours we would actually build a model you know customized to that company. We would actually train we would actually train you know retrain these models on the enterprise corpus on that data in a safe and secure way again so that you know the model starts to understand your company your lingo your code names you know acronyms you know all of that stuff and then in our core search you know stack we would use both traditional like search techniques to figure out what's the best information to return given a question but we'll also use you know this transformer-based semantic matching technology. So it was a hybrid search stack you know that we built you know from day one.
So what was sort of finding product market fit for that? Like you know I think about all the startups that have launched over the past couple of years they almost have this truly magical experience the first time you experience them because the AI is just so powerful right it's like the first time you use ChatGPT or the first time you use one of these image generators going back to 2019 or 2020 when you started to go to the market. The concept of transformer must have been so new for all these enterprise companies. Did you find that same sort of magical experience where you're like, 'Oh, hey, enterprise, let us show you how you now can, you know, prompt semantically against your entire organization and just have it, you know, come back to them like a human being.' I mean, that must have just been really really powerful for them.
Yeah. Actually, you know, it's interesting that we didn't actually talk about Transformers as a cool thing. It was not a hot thing like people were not talking about it. I think what they cared about was what you could do with it which was that well like you know in Glean you can ask questions in natural language and it will conceptually match the information with your question. You don't have to like remember the exact keywords you know and that was sort of one of the big pain point of the previous generation search systems is that well I know I'm looking for this email like it's in my Outlook but I still cannot you know get to it because I don't remember the exact words. So I think it appealed to people but you actually ask a very interesting question. We don't think you know we had a product market fit for a long time like in the sense that somehow like you know like the industry felt that search was a vitamin and not a painkiller like you know well yeah like you know everybody would acknowledge that yes you know we have a problem like you know we have lots of information it's you know spread and fragmented across many different systems and people do struggle like you know with finding information but well ultimately they find it and like you know I guess things are working for us and there was not a product that they were actually buying like you know they're not buying a search product that we would say that hey look you know we have a better one than what you buy there was no concept of them buying a technology like this so we had to create a market and it took us time we had to do a lot of evangelism we had to talk about why it is important like you know it reduces frustration it makes your employees happier you know they spend a third of their time just looking for information so you'll save a lot of time and so it took us some time you know before like you know we started to gain that traction and get and gain that word of mouth like you know from our initial customers.
And what was the moment that it all flipped? Was it just, you know, by you reached some moment that you had enough people talking about it in the market that it just sort of snowballed from there or was it a key customer or a key sort of product unlock like what really did it?
Yeah, I would say there were two distinct moments like one is where we actually had about 30 or so companies and we were first focused on the tech sectors. So we got most like you know the most iconic tech companies you know they started to use our product at scale and the word of mouth started to happen from there which sort of created like you know that further momentum and inbound like you know to us in terms of more interest in the company and then there was another big moment which was ChatGPT in some ways you can think of you know Glean as you know the enterprise version of ChatGPT like you know it does everything that ChatGPT does but it does it like you know with that knowledge and context of your company. When the world saw ChatGPT and the power that it has as an enterprise leader you were thinking about well what if you know I had something like this inside my company something that knew everything about my employees you know my internal data and it could also answer questions for me using all of that that would be fascinating right so that actually started to create that organic demand you know was a second wave of like you know massive momentum for us.
Yeah that's really really interesting thing. It's almost like ChatGPT kind of did a lot of the external marketing for you.
But all the things you had previously been working on like security and you know safety and like you mentioned permissioning between different users obviously like so important to bring that technology into an enterprise. So that's fascinating. You mentioned early on that you know you started out the first transformer-based model you're using was BERT. Since then like how do you manage the sort of model component of the product? Do you train your own models now? Are you, you know, you hot swapping things under the hood between, you know, OpenAI, Anthropic, etc. Like, how's that all work?
Yeah, more of the latter. So let me explain the architecture of this. We're not a foundation model company. We don't train these super large models when you think about like the product like how does Glean actually work. So it looks and feels like ChatGPT, right? You come in, you ask questions and Glean will actually use either all of the world's knowledge and context to answer those questions for you or it's going to actually use you know your internal company's data and knowledge to answer those questions for you. Now the way like the models themselves like you know when you think about you know GPT or Claude or Gemini or X or you know DeepSeek and whatnot like you know none of those models are trained on your enterprise information like so they don't know anything about like you know how work happens inside your company and so how do you actually like answer questions make these models answer questions you know using data and knowledge that's inside your company you know the architecture for that is you know RAG right where you actually you know it's a two-step process like you know given any task or any question that a user comes and asks you know first we use our core retrieval and search technology to assemble like some relevant pieces of information from inside our company and then we give all of that to the model so that the model can actually reason over it and actually generate you know the appropriate you know answers or responses back to you. So that's the architecture that's typical like you know most enterprise agents or applications are built in this you know RAG style architecture where search is a first leg I mean not search or retrieval whatever you want to call it and then the model reasoning and inferencing is the second part. Now our architecture you know is so we do build models and they're still built on like you know BERT or modern BERT like you know these small you know I guess these are SLMs you know is what the industry calls them now. So we actually still train models on your enterprise corpus but that is done mostly to actually do semantic matching of people's tasks with your enterprise knowledge. So it's used for that you know retrieval task but then for reasoning and generation we use the foundation models and our architecture is like we're not tied to one particular model like what we're seeing in the industry is that there are many many model providers they're all in different models are getting better at different things. For example like you know these days you'll hear a lot of people say that for code generation like Claude is actually doing better than any other model for reasoning GPT is doing better than others and so they're all like all these models are getting better at different things and our goal you know at Glean is to make sure that for our enterprise customers like we can bring all the amazing innovation that's happening in the industry you know to our customers they don't they should not have to choose you know what model to use and so that's how architecture is that behind underneath you know we have access to all these different models and given you know given the task given you know we can actually you know help an enterprise choose what's the right model to use for that.
Yeah and what's really cool about that is as these models keep getting better and you know and I'm sure everyone in the audience knows like feels like every week these things are getting better your product just keeps getting better and better as a result right anything these models can do you get to now offer to your customers, your enterprise customers. What must that be like? Because I have to imagine that as these models improve and capabilities, they probably unlock like completely new feature sets for you, you know, like one thing I keep thinking about with Glean is like as these models become truly agentic and they move from you know they move from individual sort of retrieval of information to more sequential task oriented things like that. That's got to open up a huge service area for Glean and your customers. Is that how you think about it?
Absolutely. I mean I think the and these features are actually coming every day like you know like you saw in the last month you know with you know operators like you know is a new thing you know which actually like really unlocks you know our ability to work with many many more enterprise applications. One of the important things like the way I think about AI and its impact in the enterprise is that like if I'm a business user and I have a question or I have a task which requires me to play with or work with some data inside my enterprise like AI is supposed to make it super seamless for me like you know like I don't want like you know this we don't we're not going to be in a world anymore where we know that the data exists but accessing it is hard like you know AI is going to make it super easy like you know a lot of it you know we do through our integrations but a lot of it we can also do through these you know like new LLM capabilities like computer use you know operators where you don't even need those systems to have APIs you can still sort of go and drive you know a browser and sort of you know like you know get that data that you need to work on you know for your agents so the innovation is going to you know keep happening at the fast pace like you know our strategy at Glean is always very clear you know everything that the model providers that the cloud hyperscale retailers all the technology that they're building you know we basically don't compete with them you know we leverage that technology and we do the remaining part which is like you know how do you make it easy to access that like you know all that technology and make it work on your data in your enterprise. The third thing that you can do with Glean like so we have a Google-like search we have a ChatGPT-like AI assistant but then we also have an agent building platform so you can actually build all kinds of agents to transform your business processes using AI. And in fact, I think the best thing with AI is that it's actually bringing that power to you know to every business user. You don't have to be a developer anymore to be able to build something you know complex or interesting like you know you don't need developers you know for everything anymore. Like you know there are a lot of like you know agents that you can actually just build by expressing you know a business process in natural language to AI and then AI sort of like you know does you know things like what you mentioned like you know it'll you know do the reasoning it will actually take the business process it will create a multi-step workflow that you can then go and review and execute so I think yeah we are definitely you know going towards a world where a lot of like you know how work happens is going to fundamentally change. And yeah.
Yeah. So, so, so this agent builder which you mentioned, like what does that require from the models to work? Like does that require models that are specifically capable of reasoning or being able to browse the web like operator or computer use or is Glean really the product and the system that enables to take these LLMs and sort of piece them in a way together that they're actually executing on tasks on behalf of your enterprise.
Yeah, it's a combination of that like today like you know if you just work with an LLM that's not enough for you to actually build an agent and automate a business process with it. You do need you know some more work you know that needs to happen on top of it like for example you need to figure out how to actually bring the right data to these models that's in your enterprise and like over time like you know there's going to be more and more features that you will get from the LLM providers like you know computer use for example is an example of that you know this is the feature this is not like really the model but it's a feature that the LLM companies are actually providing to you. You know, it's a mechanism for you to sort of, you know, bring more data into the models into the LLM reasoning. The way things will evolve is today I think that you still need you know agent building frameworks and platforms that will actually sit on top of LLMs. The way we are helping our customers build agents is you know you get this agent builder UI you know where you can just talk in natural language you know converse with Glean to build an agent together behind the scenes you know we're using you know both our you know API based integrations to enterprise data sources but like that the overall orchestration is being done by us.
