A Product Market Fit Show | Startup Podcast for Founders
00:17:28 · Pablo Srugo, Arvind Jain
[00:17:28] S1: You mentioned taking a different approach. I mean, I can see it because if you go for two years building a product without necessarily charging for it, you're certainly not following the Eric Ries Lean Startup, fast iteration, MVP approach. Why not? Why didn't you just build the MVP, the lowest thing that somebody would pay for, put it out and just do what most startups tend to do? [00:17:45] S0: Yeah, because we had a fundamental belief in the problem that we were solving. We didn't feel we had to pivot. Our journey was not about that we are going to actually try something, we're going to learn something, we're going to change it and irate. Sometimes we talk about product market fit here. For me, I think there was something about this problem. I had no doubts that we will solve this problem, but I also was aware listening to customers, we can get a lot of different ideas. There are some decisions that you have to make, which are fundamental yourself.
00:17:45 · Arvind Jain, Pablo Srugo
[00:17:45] S0: Yeah, because we had a fundamental belief in the problem that we were solving. We didn't feel we had to pivot. Our journey was not about that we are going to actually try something, we're going to learn something, we're going to change it and irate. Sometimes we talk about product market fit here. For me, I think there was something about this problem. I had no doubts that we will solve this problem, but I also was aware listening to customers, we can get a lot of different ideas. There are some decisions that you have to make, which are fundamental yourself. [00:18:19] S1: That's pretty incredible. I'm trying to make sense of it. I mean, there is analogies, right? If you look at hardware as an example, whether you take the iPhone or now the Vision Pro or whatever, you just can't iterate at that level. You have to have – you of course talk to customers, but at some point, you got to have that leap of faith, that conviction that puts you in a room for two years, so to speak, and builds a thing that really delivers value. I guess the challenge is when you, let's say, go against the grain. In your example there, the market's saying one thing, you're like, no, I'm sticking on that vision. Obviously, you might end up two years later and the thing you built, nobody wants. I'm just curious how you think about that. How do you balance that? Even your advice to founders, because on the flip side, the Lean Startup method is so ingrained that almost everybody is just blindly running that motion. Maybe in some cases it just doesn't apply.
00:19:05 · Arvind Jain, Pablo Srugo
[00:19:05] S0: Part of it is actually what is the goal that this founder has. Is the goal to actually build a successful company or is the goal to solve a problem, something that you're passionate about? For me, it was the latter. I was not trying to get a win. I was actually trying to solve that problem. That's why we made those decisions. We want to actually bring help to every single person. We want to make work more exciting. We want to make work less frustrating for every single person in the world. For a first-time founder, they're actually trying to – their goal is actually succeed as an entrepreneur. They want to actually learn that art. It's personal in that sense. For me, that was not what I was looking for. For me, my focus and to date now, all the decisions that we make even today are driven by that thing, which is we want to actually solve this particular problem for people. [00:20:02] S1: As you're doing this, what's the threshold where you do launch? What are you trying to get to where you feel like, okay, this product is ready to be sold?
00:20:02 · Pablo Srugo, Arvind Jain
[00:20:02] S1: As you're doing this, what's the threshold where you do launch? What are you trying to get to where you feel like, okay, this product is ready to be sold? [00:20:12] S0: Yeah, so as we said, for the first two years we didn't charge. We wanted to see engagement. We wanted to see how much people are using the product on a daily basis. When we saw that it crossed ten searches per day on average, that's when we knew that, well, look, now people are dependent on this product. They are actually getting value from it. That's when we felt we were ready to charge for this product. In our metrics, we're showing that both that they are actually finding things that they need and also they're actually using the product a lot. They're finding it useful to actually come and search here as opposed to using their old methods of finding. [00:20:54] S1: By the way, what was so hard about this product? Do you remember as you went through that development cycle parts that just were really hard to make to work properly or even places where you might've thought I wonder if we can actually get this to the vision that you have?
00:21:12 · Arvind Jain
[00:21:12] S0: Well, I mean, if you think about search, it is like magic. You can come in and ask any question and now we have answered that question for you, doesn't matter what question you came and asked. It's a difficult thing. You have to now think about this, just imagine the process of how are you going to actually solve this problem. First you have to actually connect Glean with all of your company information and data across hundreds of these systems that you have, all of them with clunky APIs or interfaces and data models. You have to bring information from all these different systems. You have to normalize it. What information is stale? What is out of date? When you answer questions for people, don't show them stuff that's no longer source of truth anymore. You have to think about how do you semantically understand data and knowledge because people are not going to be – you want people to be able to ask answer questions very freely in natural language. They can use terms that don't exist in the documents. There's a lot of technology that goes behind the scenes to actually build a product this. We've seen this. We've seen how complicated the Google Search stack was. Most of it actually did come from Google. We worked on search there. We understood the complexity of it. Then we had to think about the security aspects of it, which is that – imagine this, I go to Pepsi or some large company and tell them that, hey, look, we have this great search product. What we need is we need you to give all of your data to us. Then we'll actually make it searchable for you. They're going to wonder, okay, who are you? No brand company and you want all my access to all my data? That's an issue. If you start to think about the problem, although the problem is actually very meaningful, there are a lot of challenges.
00:23:08 · Pablo Srugo
[00:23:08] S1: How much did the advances in AI, like LLMs and all these sorts of things, did that play into what you were able to build at Glean? Was that a tailwind for you guys?