Arvind Jain, CEO of Glean AI and co-founder of the multi-billion dollar company Rubrik, is a veteran of Silicon Valley’s most demanding engineering environments. After a decade as a distinguished engineer at Google, he experienced firsthand the productivity ceiling that fast-growing companies hit when internal knowledge becomes fragmented and inaccessible. This pain point led him to create Glean AI, initially conceived as a “Google for your workplace.” In this conversation with Nataraj, Arvind discusses Glean’s evolution from an advanced enterprise search tool into a sophisticated conversational AI assistant and agent platform. He dives into the technical challenges of building reliable AI for business, how companies are deploying AI agents across sales, legal, and engineering, and his vision for a future where proactive AI companions are embedded into our daily workflows. He also shares valuable lessons on company building and fostering an AI-first culture.
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Nataraj: My wife’s company actually uses Glean, so I was playing around to prepare for this conversation. But for most people, if their company is not using it, they might not be aware of what Glean is and how it works. Can you give a pitch of what Glean does today and how it is helping enterprises?
Arvind Jain: Most simply, think of Glean as ChatGPT, but inside your company. It’s a conversational AI assistant. Employees can go to Glean and ask any questions that they have, and Glean will answer those questions for them using all of their internal company context, data, and information, as well as all of the world’s knowledge.
The only difference between ChatGPT and Glean is that while ChatGPT is great and knows everything about the world’s knowledge, it doesn’t know anything internally about your company—who the different people are, what the different projects are, who’s working on what. That context is not available in ChatGPT, and that’s the additional power that Glean has. That’s the core of what we do. We started out as a search company. Before these AI models got so good, we didn’t have the ability to take people’s questions and just produce the right answers back for them using all of that internal and external knowledge. In the past, I would call ourselves more like Google for your workplace, where you would ask questions, and we’ll surface the right information. But as the AI got better, we got the ability to actually go and read that knowledge and instead of pointing you to 10 different links for relevant content, we could just give you the answer right away. That’s the evolution of how we went from being a Google for your workplace to being a ChatGPT for your workplace. We’re also an AI agent platform. The same underlying platform that powers our ChatGPT-like experience is also available to our customers to build all kinds of AI agents across their different functions and departments and ensure that they’re delivering AI in a safe and secure way to their employees.
Nataraj: You started in 2019 as an AI search company. Now, it feels very natural to build a ChatGPT-like product for enterprise because the value is instantaneous. But why did you pick the problem of solving enterprise AI search back then? It was not the hot thing or an obvious problem. What was your initial thesis?
Arvind Jain: For me, it was obvious because I was suffering from that pain. Before Glean, I was one of the founders of Rubrik. We had great success and grew very fast; in four years, we had more than 1,500 people. As we grew, we ran into a productivity problem. There was one year where we had doubled our engineering team and tripled our sales force, but our metrics—how much code we were writing, how fast we were releasing software—were flatlining. We just couldn’t produce more, no matter how many people we had.
One key reason was that the company grew so fast, and there was so much knowledge and information fragmented across many different systems. Our employees were complaining that they couldn’t find the information needed to do their jobs. They also didn’t know who to ask for help because there was no concept of who was working on what. When we saw this as the number one problem, I decided to solve it. My first instinct as a search engineer was to just go and buy a search product that could connect to all of our hundred different systems. That revealed to us that there was nothing to buy. There was no product on the market that would connect to all our SaaS applications and give people one place where they could simply ask their questions and get the right information. That was the origin. I felt nobody had tried to solve the search problem inside businesses, even though Google solved it in the consumer world. That got me excited. At that time, we were not thinking about building a ChatGPT-like experience; nobody knew how fast AI would evolve.
Nataraj: I think almost pre-ChatGPT no one called AI as AI; it was called ML or some other technical term. I remember watching Google’s Pixel phone launches in 2020-2021, and they were doing a lot of work creating AI-first products very early on. But for some reason, the tragedy is Google is considered as not doing enough with AI. That was a narrative versus experience difference.
Arvind Jain: In 2021, we launched our company to the public and we called ourselves the Work AI Assistant. We didn’t call ourselves a search product because we could do more than search. We could answer questions and be proactive. But it was a big problem from a marketing perspective because nobody understood what an assistant was. Nobody had really seen ChatGPT. It was a big failure, and we rebranded ourselves as a search company. Then, of course, with ChatGPT launching, people realized how capable AI is and that it can really be a companion, which is when we came back to our original vision.
Nataraj: One CEO I spoke with mentioned that when you pick a really hard problem to work on, a couple of things become easier. It’s easier to convince investors because the returns will be very high if you’re successful, and you can attract people who want to solve hard problems. What’s your take on picking a problem when starting a company?
Arvind Jain: I agree with that assessment. It’s not that you’re just trying to pick something super hard to solve as the main criterion. The main criterion still has to be that you add value and build a useful product. I’m always attracted to working on problems that are very universal, where we can bring a product to everybody. I like it both because of the impact you’re going to make and because building a startup is a difficult journey. You have to have something that makes you go through that, and for me, that something is impact—solving a problem that builds a product useful to a very large number of people.
Second, when you think about solving problems, you have to think about your strengths. If you are a technologist, it’s a gift if the problem you’re trying to solve is a difficult one because you’ll be able to build that technology with the best team, and you won’t get commoditized quickly. With Search, I knew how hard and difficult the problem is. That was definitely an exciting part of why I started Glean—I knew that if we solved the problem, it would be a super useful product and a technology that others wouldn’t be able to replicate quickly.
Nataraj: One thing I often see with tools like ChatGPT or Glean AI in the enterprise context is that when you’re working on certain types of data, it’s not enough to be 90% accurate. If I’m reporting revenue numbers to my leadership, I want it to be 99.9% accurate. Can you talk a little bit about the techniques you are using to reduce hallucination?
Arvind Jain: AI is progressing quite quickly. There’s a lot of work that the platforms we use, like OpenAI, Anthropic, and Google, are doing. The models today are significantly different from the models we had last year in terms of their ability to reason, think, and review their own work, giving you more confident, higher accuracy answers. There’s a general improvement at the model layer, which is reducing hallucinations significantly.
Then, coming into the enterprise, none of these models know anything about your company. When you solve for specific business tasks, the typical workflow is that you have a model that is thinking and retrieving information from your different enterprise systems. It uses that as the source of truth to perform its work. It becomes very important for your AI product to ensure that for any given task, you are picking the right, up-to-date, high-quality information written by subject matter experts. Otherwise, you end up with garbage in, garbage out. That is what most people are struggling with right now. They build AI applications, dabble in retrieving information, and then complain to their customers that their data is bad. That’s not the right answer because AI should be smart enough to understand what information is old versus new. As a human, you have judgment. You look for recent information. If you can’t find it, you talk to an expert. AI has to work the same way, and that is what Glean does. We connect with all the different systems, understand knowledge at a deep level, identify what is high quality and fresh, and ensure that models are being provided the right input so they can produce the right output. Our entire company is focused on that.
Nataraj: You mentioned an AI agent platform. What are the typical use cases for which enterprises are creating agents?
Arvind Jain: I’ll pick some key ones across a few departments. For sales teams, much of their time is spent on prospecting and lead generation. You can build a really good AI agent that does that faster and with higher quality than a human in many cases. People have built an agent on Glean where a salesperson says, “I would like to prospect these five accounts today,” and Glean will do a good amount of research, identify the right contacts, and generate personalized outreach messages. Our salespeople then review the work of AI with a thumbs up or thumbs down, and the messages get sent out. They can now prospect at a rate five times greater than before. Similarly, after a customer call, an agent can generate the meeting follow-up with action items and supporting materials, a task that used to take hours.
For customer service, the job is to answer customer questions and help with support tickets. AI is pretty good at that. People have built agents to auto-resolve tickets. For engineering teams, AI can be a really good code reviewer. The Glean AI code review agent is quite popular; it’s the first one to review any code an engineer uploads and can handle basics like following style guides. The use cases are exploding. Last year it was all about engineering and customer support, but now it’s all departments. Legal teams are using a redlining agent that automatically creates the first version of redlines on third-party papers like MSAs or NDAs. It’s a huge time and cost saver. The democratization is happening now.
Nataraj: It feels like a better way to describe agents is as ‘workflow agents,’ similar to Zapier but with an intelligence layer. This can only work if you’re integrated well with different apps, and today every company uses hundreds of SaaS tools. Can you talk about that challenge?
Arvind Jain: You’re spot on. Agents have to work on your enterprise data, use model intelligence to mimic human work, and take actions in your enterprise systems. There’s a strong dependence on your ability to both read information and take actions. The good news for Glean is that we’ve been working on that for the last six and a half years. We have hundreds of these integrations and thousands of actions we can support, which becomes the raw material for building these agents.
It’s interesting how hard it is to get that to work because enterprise systems are very bespoke. One major challenge is security and governance. You can’t have an agent platform where agents just read any data from any system. You have to follow the governance architecture and rules inside the company, like permissions and access control. You have to not only build these integrations but also work upwards from that to handle agent security and ensure you deliver the right data to these agents, not stale or out-of-date information.
Nataraj: We’ve seen a few form factors: the chat bar, then RAG on the engineering side, and now everyone is talking about agents. What is the next form factor or use case you see coming up?
Arvind Jain: One big shift from the initial ChatGPT-like experience, which is very conversational and reactive, is that agents are becoming more proactive. You can build an agent that runs every day or when a certain trigger condition is met. The next big thing I see is AI becoming even more proactive and embedded in your day-to-day life. You won’t think of AI as a tool you go to; it will just come to you when it detects you need help.
Our vision for the future of work is that every person will have an incredible personal companion. A companion that knows everything about you and your work life: your role, your company, your OKRs, your career ambitions, your weekly tasks, your daily schedule. It’s walking with you, listening to every word you say and hear. With all that deep knowledge, it’s ready to help proactively. For example, imagine I’m commuting to work. My companion detects I’m unprepared for my meetings. It knows the commute is 38 minutes, so it can offer to brief me as I drive, summarizing the documents I need to read so I feel prepared for my day. That’s where we are headed. AI is going to become a lot more proactive.
Nataraj: Does that mean Glean is going into cross-platform and cross-application to make us more productive? I can imagine a floating bubble on my mobile where I can just hit a button and narrate a task.
Arvind Jain: Absolutely. We already have these different interfaces. Glean works on your devices—we have an iOS app and an Android app—and it gets embedded in other applications. If you’re building the world’s best assistant or companion for everybody at work, you have to travel with them. From a form factor perspective, you’re going to see more interesting devices, whether it’s a smartwatch or a smart pen. Our goal would be to make sure we’re running on them.
Nataraj: I want to shift gears and talk about the business. You mentioned a marketing failure pre-ChatGPT, then a rebrand. Now that you’re a fast-growing company, with AI increasing productivity, does that mean you’re hiring less? If you had X salespeople at Rubrik, are you hiring fewer now for the same level of growth?
Arvind Jain: First, a company is a group of people building something together. I firmly believe the scale of your business is proportional to the number of people you have. I don’t personally believe I can have a five-person company and generate a billion dollars. The productivity per employee is going to grow at a relatively linear pace. It’s just that to survive as a company, you have to do 10 times more work than you did before with the same number of people, because everyone is benefiting from AI.
You have to be able to build products and experiences we couldn’t dream of before. You shouldn’t be thinking, “Can I have fewer people?” You have to think, “How do I achieve more with the number of people I can absorb?” You don’t have a choice. If you deliver the same kind of products as pre-AI, you won’t survive. We are growing very fast and investing in our people. We fundamentally believe the larger we are, the more we’ll be able to do. But at the same time, I’m a minimalist. I always try to ensure we are enabling every employee with the right tools and that they are fully capitalizing on AI to deliver way more than expected in the pre-AI world.
Nataraj: What does it mean to be more AI-first? Do you do more AI education or align incentives?
Arvind Jain: We started by just talking about the importance of AI in town halls. I don’t think we saw the results because people were too busy. Then we tried setting goals like “get 20% more productive,” which was a complete failure. Our third iteration was to just do one thing with AI. We don’t care about the ROI; just show that you’re trying to learn and get one meaningful thing done. That’s the top-down approach. From a bottom-up perspective, we allow people to bring in the right AI tools and we celebrate wins. We created a program called “Glean on Glean.” Every new hire, for their first month, ignores their hired role and instead plays with AI tools to build one workflow or agent. It’s been very effective, especially for new grads who don’t know the traditional way of working and are more well-versed with AI.
Nataraj: What are one or two metrics you consistently watch that tell you whether you’re going in the right direction?
Arvind Jain: For us, number one is customer satisfaction. We look at user engagement—how often our users use the product on a daily basis. That’s the most important metric. Number two, on the product side, we look at the type of things people are trying to do with it and if that set is expanding. For example, are more people becoming creators on Glean and building different sets of agents? From the business side, we look at standard metrics like retention rate and tracking our pipeline for demand. But as a CEO, probably the most important thing to watch is how our organization is feeling internally. What are the signs from the team? Are we ensuring we have mission alignment? Are people committed and motivated? Are we creating the right environment for them to grow and succeed? Those are the top-of-mind things for me.
This conversation with Arvind Jain offers a clear look into how enterprise AI is moving beyond simple chat interfaces to create tangible value through sophisticated workflow agents. His insights provide a roadmap for how businesses can leverage AI to solve core productivity challenges.
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