A Product Market Fit Show | Startup Podcast for Founders
00:17:11 · Niklas Lindgren, Pablo Srugo
[00:17:11] S0: Oh, wow, we did a lot of things and I think that is the overall answer to it. You need to, like we tried a lot of things. What we really wanted to get to is, how are we building this product? Because there is an industry standard where everyone works on and we didn't want to replicate whatever went on that. Because everyone just hated it. So we wanted to get into something new. [00:17:35] S2: What is that standard, by the way? Just maybe not super deep, a little bit of context? [00:17:38] S0: That standard is everyone works on one platform called Revit. Like lawyers work in Word, these engineers work in a platform called Revit. Lawyers don't hate Word, but there's a lot of engineers across the globe that hate Revit, because it's so legacy. It was, as I said, built in '97. It's not back compatible with Word. It's not a modern software. We wanted to understand early on, what are we building? How should a five star user experience be? And being just a four man team, none of us being the actual ICP ourselves, we needed to get out and speak with users. So that's what we did early on. We wanted to speak to as many potential users as possible and we did this on. I think most of our conversations early on were on Reddit, with DMs on Reddit, in MEP engineering threads. [00:18:31] S2: And you got them on calls or are you mainly, literally mainly online?
00:18:35 · Niklas Lindgren
[00:18:35] S0: We got some of them on calls, and then we got some of them just chatting back and forth, asking them questions to verify different types of ideas or concepts or things like that. We did that really early on '25, like spring of '25, and then we had something that you could look at in the summer of 25. Like an actual product, right? Very prototype, but still something like a user interface you could actually look at and once we started showing that to users. Like, how it could work. It didn't have like a fraction of the feature set that we have today or a fraction of sort of the features that you needed in order to launch it. But they understood the concept of this and it was almost like a dream coming true for them. Because they spend so much time on tasks now that are just bullshit tasks. They have to move data from this program to this program and it's almost a copy paste type of task. And they spend so much time doing that simply because the systems are not interconnected with each other, and don't talk to each other. To give you an understanding of this, like if you're building a hospital today. You have engineers that are manually placing each and every unit that. When you walk into the hospital, each and every unit that you see has been placed in a 3D model before going in there. So every individual needs to place each and every receptacle, each and every lighting fixture, each and every lighting switch inside this hospital. And these are tens of thousands of units. And then once that is done, they need to calculate how many receptacles do I have and then they need to ingest that into an Excel file to then calculate like how much load do I have on this panel. And once I know how much load I have on this panel, I need to start calculating in another software how much metal is needed inside these cables and it's levels of complexity. And so once I saw that, oh shit, we can have an agentic experience where this is generating the design and I almost have a vibe coding experience. Where I can set my playbooks, I can set my constraints and I can give a context window to. And have Endra just build this system for me instead, and then I can start reviewing them.
00:20:47 · Pablo Srugo, Niklas Lindgren
[00:20:47] S2: In this case, add the fixtures for me. Like you tell it more or less, hey, this is the number or this is the spacing or these are the constraints and it kind of just does it. [00:20:54] S0: Exactly like that. [00:20:55] S2: OK. [00:20:56] S0: When engineers are starting to say like, holy shit. I know because they gave us their models that they previously completed. So once you saw like, once I ingest this into Endra, it's not perfect but it comes to eighty, ninety percent of whatever I did before and I spent four hundred hours doing this project. Endra created this for me in under ten minutes. [00:21:15] S2: This is an interesting point to draw on because a lot of the AI output is eighty, ninety percent. In some cases, eighty nine percent is almost as good as zero. It saves you no time because then you have to review, you double check, blah blah blah, and you're like, oh my god. But in this case, the eighty to ninety percent really was eighty to ninety percent. I just, I don't know how that plays out, but maybe you can speak a little bit about that.
00:21:15 · Pablo Srugo, Niklas Lindgren
[00:21:15] S2: This is an interesting point to draw on because a lot of the AI output is eighty, ninety percent. In some cases, eighty nine percent is almost as good as zero. It saves you no time because then you have to review, you double check, blah blah blah, and you're like, oh my god. But in this case, the eighty to ninety percent really was eighty to ninety percent. I just, I don't know how that plays out, but maybe you can speak a little bit about that. [00:21:34] S0: Yeah, definitely and the reason why we're saying eighty, ninety percent is because it goes back to how you design buildings. That's an iterative process. So eighty, ninety percent is one deliverable and it's fine that it's not a hundred percent in that stage. But that's the full deliverable that you charge your customer for and then as design develops, the architect comes up with a new building layout. Which has another room in the left wing or whatever. You sort of iterate, iterate, iterate. There's a lot of time being spent on both, but sort of the greenfield design is usually around eighty percent of then you iterate as design develops into a more and more detailed design. And that, also consumes a whole lot of time and that goes much, much faster once you do it in Endra. Because you don't need to do that in five, six different point solutions.
00:22:22 · Pablo Srugo
[00:22:22] S2: You're going into an industry you don't know that much about. You've experienced a subset of your problem yourself before. So that kind of pulled you in that direction and then as you said, your dad maybe pulled you more in that direction but you're not an expert in the space. How much work do you do up front, and how do you do that work to get to really know your ICP? Because it's one thing to deliver one feature, like the feature you just mentioned. Which is happening everywhere with AI automating things. My experience is, in many cases, for some subtlety that you missed, it doesn't land and you don't get the pull that you thought you would have given the amount of time saving. Obviously, you did get it. So I'm curious, how much work did you do up front and how did you do the work to really understand your ICP at that kind of detailed level?
00:23:00 · Niklas Lindgren, Pablo Srugo
[00:23:00] S0: We did a lot of work. We tried to scope everything down. So in our early days of product, we still have that philosophy, but we're trying to scope everything down to something viable. One point of value that we can deliver to the customers is good. We should like, let's just focus on one workflow inside Endra and do that extremely well. And I think that we nailed that early on to focus on. I'm not sure if it was three or four different workflows that they can do end to end at Endra and it actually worked. I think a lot of companies are getting very wide too fast in terms of their product offering and then, you know, you're not doing anything very good. That was sort of something that we did very good early on that we focused on, on three or four core workflows that we did extremely well. [00:23:52] S2: And did you do a lot of interviews, you know, structured or not, to know that these workflows. It's not just one or two people that are doing things like this. Everybody does things in this exact workflow sort of thing? [00:24:02] S0: Early days, we didn't have a thousand interviews. We had kind of few, but they were very similar to each other and they were kind of from the same ICP, which was from large MEP consultancies. So we took a bet at the end of the day. Because we didn't have the resources to do a very big research on the industry and we definitely didn't have the time in order to map out the full industry and the full aspects of what should we build. We needed to get something out fast and we nailed those for the first one.