For a startup you have one weapon always, which is that you have focus and you have the ability to move fast. We have to use those as our primary weapons to stay ahead. But I think partly it's also strategy. As I was saying, we don't want to reinvent the wheel here. We want to make use of all the great technologies that are available to us and then we focus on the rest where others are not wanting to spend time. Customers are looking for solutions to problems, they're not looking to buy great technology. It really allows us to focus and dedicate our engineering time on problems that others are not investing in. So I think that focus, that investment, and the speed and the agility like making the right strategic choices where we don't for example have to train our own models and we benefit from the industry's innovation. Those are some of the approaches that will help us stay ahead in this market.
My name is Arvind Jain. I'm the founder and CEO of Glean. Glean is an enterprise AI company. Think of it as a more powerful version of ChatGPT that's connected to all of your company's data and knowledge. So you can go to it and ask any questions or give it any task and Glean will use all of that enterprise context as well as the world's knowledge to answer those questions for you or complete those tasks. We started in early 2019, that makes us one of the first enterprise GenAI companies in the world. We have so much opportunity to transform how people work. Fundamentally change the nature of how humans are going to be working.
Well, I'm certainly an engineer by training. I studied computer science in college and since then I worked at five different startups over the last 25 years. I worked at Google when it was a privately held company and then we started Rubric in 2014. So I've been in tech industry for quite some time and since then I've been an engineer, I've been a programmer, and that's my core identity. I also never set out to start these two companies, Rubric and then Glean. These were things that happened as I was working through. But primarily I always thought of myself as a product builder. I love working on technology. I love taking complex problems and figuring out how software can actually go and solve those problems. In fact, the journey for Glean also started the same way. I was actually a founder of a successful company when I started Glean. Rubric had pretty good success. We were growing fast and as we grew fast, we started to see that our company struggled on the productivity front. All of our business metrics, how much code we were writing, how much business we were doing, all of those started to look much worse on a per person basis. And when we tried to understand what was happening, our employees complained the most about that they couldn't find information within the company and also they couldn't find the right people who could go and help them with their tasks. And I started to think, well, this is not a unique problem. We are like any other company. We have knowledge, we have data and every single person in our company struggles with it and their productivity drops. So for me it was obvious when I saw that, here's an opportunity to go help every single person in every single company in the world become more productive. If I can reduce their frustration, that actually means a lot to me. I feel very motivated to solve a problem like that. So when people complain that they're not able to find information inside the company, we had to go and solve that problem. I didn't have to think twice. I was super excited to actually go and build this product. So that led me to go and create Glean. It was not as if I was trying to start a company or I wanted to be an entrepreneur. The idea was more that I wanted to solve a technical problem and I wanted to build a product like Glean, and to build that well, maybe I should start a company.
So search as a product is quite hard to build. You have to bring a lot of data together in one place. Data is in different formats. Different types of data are there: images, tickets, documents. You have to build a deeper understanding of all of those different types of data. You have to understand activity and patterns inside the business. So it's a pretty complex piece of technology. Doing a good job is super important because most search systems in the past in enterprise were systems that people didn't like. Similarly, we were building a product that there was a fatigue in that category in enterprises. Enterprises that tried buying search products in the past had all failed, in fact universally failed. We had to really battle that perception that enterprise search products are all really really bad. We were building this product and we wanted to get feedback. I would actually go on LinkedIn and connect with a lot of people in the industry, leaders who I wanted to get feedback from. I wanted to learn if they would actually benefit from the product that I was going to build. When I tried to connect with people, most of the times nobody would respond back to me. I can't manage to get even 30 minutes of somebody's time, a stranger that has nothing to offer and just want to say time because I had not yet built a product, I couldn't show them a demo. So being a seller, being a business development representative sending cold outreach through email, through LinkedIn, that was a humbling experience for me. It helped me develop as a person and build that stamina and build that mental strength and fortitude. It was difficult, it was disheartening. Building a business is never easy. A lot of times you give up because there's so much struggle or so much negativity or lack of support.
But for me I had a surprisingly strong conviction in this. I knew how hard it is to find information at my work and how many hours I spent. I talked to a lot of people around me and everybody readily agreed every single time with me. There's not a single person who says that for them it was so easy to go and find information at work, get answers to their questions. So I knew that people needed it, but I knew that leaders were not used to buying it. But once we build a good product, ultimately the world will come to it. For us, we knew that we would only have one chance to succeed and we have to build a product with very high quality. Because remember the expectation that people have from a search technology is what Google delivered to them: you ask a question and instantly you get right documents in such a small time. So we were willing to wait. We were willing to spend the time, make the investment. Only when we started to feel that we have a Google quality search experience is when we wanted to launch. The problem that we're solving, while it may not generate an immediate business success, everybody understood that it's an important problem to solve, that it's high impact if somebody solves it. I think the conviction came from the fact that we used the product ourselves number one. So we know it was useful. Even in our own company we couldn't live without Glean after we built our first version. So we were actually offering the product for free for the first 2 years. I think you see usage even when we're not charging. Our design partners, we had 20 of them, and people inside those companies were raving about the product. They loved it. In fact, we had one or two situations where the security teams wanted this product to be turned off because it connects with a lot of systems. When they tried to turn it off, there was a revolt inside those companies. People just wouldn't let the product disappear. So I think these were powerful signs for us that we had built something that was useful to people. In 2022 invested in Glean and it was not that I had to go and convince them. Wingpack didn't go to them, they came to us because by that time everybody got to use the beta product for free and we had word of mouth. People had started to figure out that we had built a good product. So it was a natural expectation that was built in. We told our customers that now we are actually going GA, so everybody can now go and buy this product. They had the choice to go and buy it and each one of them bought it. Everybody's expectations were already set for a long time that at some point they'll start charging, so it went pretty smoothly.
For a startup, you have one weapon always, which is your focus and your ability to move fast. And we have to use those as our primary weapons to stay ahead. Fundamentally, our engineering philosophy at the company is, and it's a very obvious one, that we don't want to reinvent the wheel here. We want to make use of all the great technology that's available to us and we want to focus on innovating in areas where others are not wanting to spend time building products. You don't get to say that I'm just going to build some cool technology and hopefully people will go and buy it. When you think about models, we have so many companies out there building these foundational language models and they're all amazing and great. Instead of us making that same investment, we want to leverage these amazing technologies that companies like Google and OpenAI and Anthropic are building. In fact, for our enterprise customers, they love the fact that we are not building models. Customers are looking for solutions to problems, they're not looking to buy great technology. So that's been the working model for us at Glean: partner well and whoever is building great technology, try to see how you can incorporate that technology in your product and then we focus on the rest where others are not yet paying attention to. In our case, to make AI work in the enterprise, you know that these models don't really know anything about your enterprise. They don't have that context, they have no idea how your company works. To still make use of this technology inside your enterprise, you have to bring in that enterprise context for any task that you're trying to solve. If we don't have to build the same technology that others are building, if we can make use of it, for example we can make use of GPT-5 and Claude and Gemini, it really allows us to focus and dedicate our engineering time on problems that others are not investing in. Glean is now over a thousand people. So we're no longer a small startup. We're a fairly large company and we are investing more in making AI work in the enterprise than any other company out there in the world. We are a pure play enterprise AI company. We don't have any other product lines, no legacy products to support, and all of our attention goes into how do you make AI really really effective in the enterprise. So I think that focus, that investment, the speed and the agility, making the right strategic choices where we don't have to train our own models and we benefit from the industry's innovation. Those are some of the approaches that will help us stay ahead in this market.
I think the number one thing for any aspiring founder is belief and conviction. You start with an idea because you thought about a problem and it exists. Make sure that you talk to a good number of people so that you see that it's a problem that impacts a good number of people. But once you've done that, now it's your job to stay true to that idea. Because you're going to talk to more people, you're going to talk to investors. Some investors will be lukewarm on the idea, some employees that you're trying to hire may not believe in it as much as you do. But that's all okay. You got to keep that belief in it because ultimately that problem you started thinking about for a good reason and that reason still exists. Keep the conviction. Don't become the person who kills your own idea.
