Alfredo Andere - LatchBio: Building Biotech Data Tools to Revolutionize Life Sciences (Part 3/4)

Intro - 00:00:00: This episode is brought to you by Excedr. Excedr provides life-signed startups with equipment leases on founder-friendly terms to accelerate R&D and commercialization. Lease the equipment you need with Excedr. Extend your runway, hit your milestones, raise your next round at a favorable valuation, and achieve a blockbuster exit while minimizing dilution. Know anyone who needs lab equipment? If so, join our referral program. Give your friends $1,000 and in return, earn $1,000 for each qualified referral. Start earning cash today by going to E-X-C-E-D-R dot com. and click the yellow button in the bottom right to get your unique referral link. Additionally, as a podcast listener, you can redeem exclusive discounts with a growing list of biotech vendors and get $500 off your first equipment lease by using promo code TBSP on excedr.com/partners. Welcome to The Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup, from pre-seed to IPO, with your host, Jon Chee. In our last episode, we spoke with Alfredo Andere about his experience joining an international incubator in the midst of a pandemic, and what the transition from Berkeley to Taiwan was like for him and his co-founders. If you missed it, be sure to listen to part two. In part three, Alfredo shares why a little blind faith was key in the early days of building LatchBio. He talks about how his team's chemistry kept them moving forward. What it took to fully commit to biotech, and how their early market research efforts gave them a competitive edge when raising their seed round.  

Alfredo - 00:01:56: Yeah, no, great question. So it's really funny because that actually happened until after we dropped out. And it's like, how is that possible? Because most of the stories you hear about kind of dropping out, it's like, oh my God, this project is popping off. Either this project is popping off and it's going so well, this technology is just like so encapsulating. But for me, it was really kind of going back to the people. Kyle and Kenny are two of the most special human beings I know in this world. And not only them as individuals, but then the three of us combined the way we work together and the way we iterated on ideas and then disagreed and then agreed on the fundamental values, but then disagreed on the core of the ideas and then could have yelling arguments at each other about an idea. But then when we were done and maybe we arrived at a higher truth, maybe we didn't solve it in that given fight. And then we can go and like get Chipotle and a beer and just like laugh about some dumb joke. It was like really, really special. And we knew we wanted to continue that. So we had kind of known each other since freshman year. I think Kenny's a year younger. So sophomore year, we had been really close friends. And so we had started working on projects. As I told you, we started working, Kyle and I, within a few weeks, Kenny joined. We started working on these projects. And then by the end of the semester. So one of the very special dynamics that Kyle, Kenny, and I have is we were in our senior semester. We were starting our senior semester. We had decided we were still going to do that semester. It was the middle of that semester. And we were already working on different projects. And we wanted to spend more and more time because we were like, hey, we want to figure out something that people actually use. And at this point, we were like starting to think a little bit more like a startup of like, oh, maybe we make money from it. But it still wasn't full on startup. But it was, encapsulating and obsessive enough. We're all very obsessive people that we kind of started just thinking about it where every hour just felt like it was taking away from that. And we started exploring different spaces. So we dropped the EEG stuff. And then we started exploring different spaces. And so Kenny was doing some contracting on biotech. And he was working on that. And then I was kind of working on some VC projects, exploring different technologies. And we were all coming together. I think we explored Micro-mobility. We explored micro-tipping. We explored healthcare and some problems in healthcare. And then we kept going back to biotech. Anyway, end of the semester is coming up. We're like very... It's very clear how much school is taking away from our ability to just go faster. And then I don't remember who was the first person to bring it up. But it's one of our greatest qualities as a team. We instigate each other like a lot. And so I think someone might have been like, hey, well, we would have way more time if we didn't have to go to school. And then next person is like, well, I would definitely consider like not going to school. And then at that point, maybe Kenny is like, oh, you would consider it? I would consider it even more. And then Kyle might be like, oh, you would consider it? I already called my mom and asked her what she thinks. And then Kenny's like, oh, well, I already called my counselor and I'm dropping out right now. And I'm like, oh, well, I'm not getting left behind. And I love this. So let's just drop out and just cascade. And it is funny. It is a funny story. It has a lot of truth to it. But it's also how a lot of other decisions that have turned out to be extremely positive have catalyzed. And it really comes down to a lot of decisions when you're doing a startup is just being naive enough to not know just how insane and hard it's going to be. And also just taking that leap of faith and trusting your own ability. And so we have a lot of that trust in our own ability. But just having that instigation between each other just ended up being extremely positive. And so from there, we ended up dropping out. And then it was when we were raising our pre-seed funding, which I'm sure we'll get to in a bit. But when we were raising our pre-seed, we were working on like three different ideas. And at the time, I remember us looking at each other. And Kenny was very keen on, Kenny was the most biologically knowledgeable by an order of magnitude compared to us. But I remember by being more naive, I was like, hey, we keep talking to all these biotech people when we're doing user interviews. Oh, by the way, when we decided not to work on EEG. And when we decided, hey, for all the projects we're going to work on, we're not going to go and code MVPs because that's easy. We know we can do that. We're going to go talk to people and see if they actually have the problem. This is when we started getting into the startup mentality. 

Jon - 00:06:33: Market research. 

Alfredo - 00:06:34: You talked about it earlier. So we started kind of talking to people in these different verticals. And we kept going back to bio. And it was like, these people have really bad data infrastructure. But Kenny was like, no, but we can't really do anything about that because we don't have PhDs in biology. And I was like, wait, but they have a really big problem. And he was like, no, no, trust me. And I was like, well, I guess I have to trust Kenny on this because he knows so much more. But naively, from my perspective, I was like, why not? Like we, if we dedicate enough future years to this, we can do it. And so it all came down to around the time that we incorporated in February 2021. And around the time where we got our pre-seed funding, I remember us all looking at each other. And then slightly catalyzed by me, but really it was something on everyone's minds. It was like, guys, I think we're going to end up spending the next 10 plus years of our lives doing this. Are these really all ideas we would be willing to spend the next 10 to 20 years of our life working on? And when we actually thought about it that way and thought about it, honestly, really biotech was the only idea that we wanted to spend all our time thinking about. For Kenny, it was clear because he had been in the wet lab since he's 14. He knew he wanted to spend his life in biotech. He just thought, hey, first I'll go do my SIP2. And then once that successful, then I'll go change and revolutionize biotech. And then for me, it was more like a decision for me and Kyle, because it was like, hey, biotech has not been my calling all my life. But now I cannot think about anything else. And for the past four years, I haven't been able to think about anything else. But really, just at that moment, we looked at each other. We were like, this is going to be a 20-year thing. It doesn't matter what our background is. It doesn't matter what our learning is. We will pick it up. Let's just go all in. And it was another one of those kind of instigation moments where we're like, we're going all in. Let's drop everything else. And so by the time we got to Taiwan a few weeks later, a whole other story. But by the time we got to Taiwan a few weeks later to our incubator, we were just all in on biotech. And we were only reaching out to biopharma people. And we had incorporated into what originally was Asteroid Inc.. Because it wasn't quite latched yet. We were kind of still figuring out the name. And I was like, I remember calling my co-founder. And I was like, Kyle, why didn't you incorporate Asteroid Inc., he was handling? And he was like, dude. Impact. 

Jon - 00:09:00: Yeah, I love that. 

Alfredo - 00:09:03: I was like, no way. 

Jon - 00:09:04: I love that. 

Alfredo - 00:09:06: Insane story. And we've since changed our name to LatchBio, but it was really a funny story.  

Jon - 00:09:12: I love that. And there's so many directions I want to go. And the first thing is kind of, you're mentioning the kind of like the entrepreneurial, like kind of necessity, have some sort of degree of naivete that is like, it is important. It is important. It can be seen as a weakness, but it is important. And also it kind of reminds me of you coming to Berkeley and then just being like, oh, all these people have taken multiple years of like calculus and I just need to catch up, but I can do it. Similar, but for bio, right? You're like, yeah, my co-founder has been in the labs since he was 14. Why can't I catch up? Right? Because I sometimes think people underestimate their ability to do that catch up. 

Alfredo - 00:09:56: Like, totally. 

Jon - 00:09:56: If you have a will, it sounds kind of, you know, trite, but if there's a will, there's a way, right? Like if you want to do it, like if you really want to do it, you can, but there's a sacrifice that comes with it. You got to be willing to make that sacrifice. And if you want to do it and you were willing to sacrifice and more likely than not, you can get it done. And I think with life science, broadly speaking, I think these are some of the hardest problems. Like they're just like some of the hardest problems because like nature is nature. And like, these are systems that we live in. We did not create these systems. So you're right, they're trying to like. Figure out like this system that was not controlled by you. Whereas like, I think what's, you know, in other industries, like we have made these systems like computers and software.  

Alfredo - 00:10:40: That's a great point, by the way, like worth kind of highlighting because people don't understand that when they go into bio, then it makes this so different from even a computer science, which is incredibly complex in a very different way. 

Jon - 00:10:53: Super different, super different. Like we made computers. Mother nature did not just spit out a MacBook, right? It's like we made this thing so we can kind of understand the parameters of it. Whereas in science, you're like, all right, well, there's this really weird way that mother nature seems to handle this thing. I don't exactly know why. And you're just poking and prodding and just like almost sometimes feels like you're feeling around in the dark, but it's very hard stuff, but it's not completely insurmountable, right? It's still a thing that if you put your mind to it, that you can get it done. And if you didn't have that kind of mindset and that like a little bit of naivete, like there'd just be far less companies created, right?  

Alfredo - 00:11:38: Yeah. 

Jon - 00:11:38: It's kind of like that Jensen Huang, kind of like when he was doing the Acquired Podcast interview, he was like, if I knew all the pain, suffering and embarrassment of like starting in NVIDIA, I would have never done it. Like I would have never done it. So you kind of need to just kind of go in and like take that leap of faith and be a little bit blind.  

Alfredo - 00:11:56: Yeah, very naive. 

Jon - 00:11:58: You have to, because this is like, this is like an unreasonable endeavor, generally speaking, but it's a lot of fun. It can be hard, but it's a lot of fun if it's just, but again, you got to make sure it's like exactly what I like that exercise that you did with your co-founders is can we spend 10 to 20 years on this thing? Because I don't think people ask themselves that question either. It's like, you are going to be like obsessing over this one thing for an excruciating amount of time. And if you can't see yourself doing that. Like, yo, like again, figuring out what you don't want. No, I want to do something else. Like, I think I'd rather be doing something else. So like be honest with yourself for anyone who's like contemplating starting a company. It's not an exaggeration. And especially if you take capital, like if you take capital, there's an expectation that you're going to ride that out. Like there's an expectation from someone else, not just you, like there's an expectation of someone else that you ride this thing out. So just understand for anyone's just like, and I'm putting a lot of weight, but like, you can do this kind of market research beforehand and figure it out before you just like take the leap. So tell me about Taiwan. I didn't even realize that this was part of the journey. So you and your co-founders like it's biotech baby, it's biotech time. We're going to Taiwan. 

Alfredo - 00:13:14: Yeah, it's a funny in-between kind of side. But I'll just, wait, I want to add one idea to what you said, because I actually find that idea fascinating on the biotech is not built for human understanding. And that actually, I would take it a step farther from what you said of like, so computers and knowledge, generally, when it's human created, there's a strong optimization factor when you're creating these computers to make them understandable to humans. I would even claim there are many concepts in computer science that are not the most efficient. They are not the best concept, but they still won out in the evolution of architectures because they were the most human graspable. So if you think about a very easy example of that is C versus Python. C is obviously faster. It's much better in many ways. But Python has, in most domains, won out because it's more human legible, not because it is better. Now, in biotech, evolution doesn't give a fuck about what is human readable, right? Like evolution cares about what is the most optimal and what it can get to in its evolutionary kind of iterations. It in no way is one of those variables, human legibility or human understanding. And that makes this two completely different domains in terms of what is required for the learning and how easy that understanding is. Yeah, actually. I don't know if we'll ever as humans fully understand biology. I think we will figure a lot of it out with AI and with different statistical modeling that will be able to replicate that understanding in a way that is human legible. But I don't know if we'll ever be able to understand because it's just not made for our brains to understand it. So I just wanted to because, I find that point kind of underscored in a lot of conversations. And I'm glad you brought it up.  

Jon - 00:15:03: It's also dynamic. It's dynamic. Like, here's the thing. Like, you feel like you understand it today. And then it changes tomorrow. Like, you know, on an evolutionary scale. Right. So like what you've known may not be like in the future, too. So it's kind of like almost feels like nature is like moving the goalpost on you based on evolutionary pressures, like whatever it may be. And like, I've always thought and again, not an X person, not an AI/ML person. So I might be overselling what AI and ML can do. But it's finding these patterns that humans just can't grasp naturally. It's like you can have the system like, your AI/ML system just like find the pattern for you that is not naturally human in nature. Or it's like it's impossible. It's kind of like it reminds me of when chess was solved. And like some of the grand masters who are like very close to a computer, they can just like prune the decision tree down to like gnarly kind of lengths. When it was whether it was chess or go, I can't remember. But basically the AI was just like did a move that was just so just like. Huh? For like all humans. We're just like the best at the game. We're just like- 

Alfredo - 00:16:14: Yeah, that was good. 

Jon - 00:16:15: It was go. Yeah. It's like, what the heck was that? Like, what was that? And then it ultimately was the optimal by far. And then they got decimated, right? The human player got decimated, right? 

Alfredo - 00:16:26: Right, right, yeah.

Jon - 00:16:28: But just imagine that outside of Go, right? Like nature is probably doing that all the time.  

Alfredo - 00:16:33: Yeah.  

Jon - 00:16:34: Like the evolutionary like path just did not seem legible to a human. And so sometimes we'll just be like, well, that doesn't make sense. But with these tools that we now have, you can surface up these like not intuitive kind of like human intuition patterns. And then maybe that you can glean insights from there. Or at least you would open a door that you probably would have left shut without the help of the tooling. And that's at least for me is why I'm so passionate about life science is because it's like this big, hairy, audacious kind of like problem to solve. Biology is not the problem. It's just like the system that is like an infinite puzzle that is one intellectually, just like so just like fulfilling for me. And also just like I never, I'm having a lot of fun and learning about it. It's hard shit. It's like still hard shit, but it is incredibly fulfilling. And so anyways, that's the only thing I would add there. 

Alfredo - 00:17:22: No, yeah. There's so... We could have a whole podcast just on that conversation, honestly. But I just kind of going into Taiwan. 

Jon - 00:17:31: Yeah, yeah, yeah. 

Alfredo - 00:17:32: I remember we got invited to this incubator. So at the time it was mid COVID. So we were working out of our house. Kyle and I were working out of our house. We lived together in Berkeley. I think it's 2609 Hillegas. And then my co-founder lived like two blocks away, just across People's Park. 

Jon - 00:17:50: Yeah, yeah, yeah. 

Alfredo - 00:17:51: It was no longer there. Yeah, no longer there anymore. But we... We were working kind of like on COVID. So we really couldn't do much apart from just work in our rooms. And then sometimes we would have to go and pick up food with a mask and just staying six feet away from people. And then we get into this... One day I see my co-founder kind of talking to someone. And he's like, hey guys, I just talked to someone organizing an incubator. If we get in, they'll give us $50K and they'll also pay for all our flights and all our living in Taiwan. I was like, why Taiwan? I was like, well, apparently Taiwan, had a SARS breakout before COVID. So by the time COVID came around, they were incredibly good at locking down their country. So apparently Taiwan right now, at the time, Taiwan does not have COVID. You have to like isolate for 14 days when you're getting in. But after those 14 days, you're just free to just go roam the country. No one's wearing masks. People are going to the gym, hot tub, whatever you think would be incredibly illegal during COVID times. It was just a normal country. And we're like, well, we don't have school. That sounds like a great experience. This incubator will just pay for us to go there and get all the golden visas. And so we applied and we ended up getting in. And within a week we had gotten our visa. And within a few more days, I remember we probably incorporated in the 22nd. We got in the 23rd and by the 27th, we were flying out to Taiwan. We had no responsibilities. We shut down our three person WeWork, which was funny enough, smaller than this conference room I'm sitting in right now. And we shut it down. And then we flew to Taiwan and we isolated for 14 days. That was actually one of the most bizarre experience, like isolating in a country where you don't speak the language in the suburbs, because the hotels that were you isolated were in the main city, they were in the suburbs. So the outskirts and then isolating for 14 days in a hotel room felt like a cheaper hotel was one of the most bizarre experiences I've ever had. Also some of the most productive days of my life. But yeah, we were just pumping out emails while we were in the hotel room. And then after that, we got to this incubator and we just spent the next two and a half months interviewing people in biopharma, our input goals for when we would meet in the, in the afternoons, it was like, did you max out your LinkedIn outreaches for the week? You know, LinkedIn for a lot of people don't know this, but LinkedIn actually doesn't let you send unlimited connections. You get, you get tapped after a certain point. Did you max out? How many emails have you sent? How many meetings have you booked? And so, we were just doing that and reaching out to people in biopharma and learning from them. We ended up with hundreds and hundreds of pages of notes on data infrastructure. What two people were using like notes that we ended up referencing years after that initial incubation period. And at the end of the incubation period, we went out with these notes and with six people willing to pay for something. And that was the most magical thing because we had six people willing to pay us for a part of the data infrastructure. That was really messy for them, which was the workflow orchestrator. And we went out to raise a seed. And I remember thinking like, oh my God, I wonder if we're going to be able to raise any money, let's go out and try to raise 2 million. And we end up, we show up to these calls and we have 200 pages of notes, six people willing to call out of the investors. And for me as a student, I was like, well, it's still $2 million. Nowadays I look back and I'm like, man, most seed rounds I see, being raised these days don't even have like a deck. And like, here we were with like 200 pages of notes, 300 user interviews and like six people willing to call up a VC like, yeah, I will pay $60,000 when they build this product. And so we went from thinking whether we would be able to raise $2 million from having a way over subscribed round, having to like negotiate people down and then ended up signing with our now super deep and loved partner Lux Capital. Brandon Reeves ended up coming in for this seed round pretty quickly after we met him, honestly. And it's been one of the best decisions we made. And at the time it was extremely bizarre. Imagine we had just dropped out three months ago from school and we were now racing a $5 million seed round. And I was telling my parents, my parents were like, are you crazy? What is this? Like, who are these, who are these dumb people? What do you mean? What do you mean a $25 million valuation? Like, do you guys have a product and revenue? And I was like, no, we don't. And so it was a wild time. And when we did that, after we went out and it was so easy to race. You would think that for many people, they'd be like, oh yeah, let's go like, okay, let's like slow down a bit. But for me and my co-founders, like when we just had such an easy time racing for this idea, a, this idea is probably like correct direction. B, a lot of these people, VCs that we said no to are now going to go be looking for who else to fund in this space and see, they're going to get funded because, like we got funded so easily. We need to go, go, go. And so moved back to San Francisco, we set up a, we work in San Francisco, we ended up and that's kind of when the, the rush really started to build a product as far as, as fast as possible and then start iterating with the initial customers. 

Jon - 00:23:05: Very cool. And I think I love that you did an incredible amount of market research and getting customer feedback. That is the thing, right? It goes back to the cardinal sin. It ends a lot of companies, like a lot of companies. And even not just like in a software lens, this even happens in, in the therapeutic side, right? My roots are in preclinical and the end patient is such a distant thought that like, I'm just working on this problem that I think is like going to work downstream. What is the reimbursements? Like, how are the patients going to actually get this, you know, all that, all that stuff that I'm not an expert on. And it is hard. I'm not, you know, these, these like getting a drug to market is a very, very long time scale. So it is super hard to do that forecast. Right. But you see that too. Like, I think we're seeing in the CRISPR space right now is like at the end, the way that the patients are like, how is this going to be reimbursed? How the patient's going to actually take the medicine. It ends up just like being just not economically viable. 

Alfredo - 00:24:09: Right. 

Jon - 00:24:10: Yeah. It's hard. It's hard because these problems are worth solving. Absolutely worth solving, but it just like, dang, like the economics of it and just like how this. The nuts and bolts of getting this into patients just isn't really working. Right? But like, I think it was incredibly prudent of you to do all that work. He's like, here are all the people we talked about. Here's all the notes. Here's all the feedback. Here's like, these are real people with real problems and they're willing to pay. Like you kind of did that foresight. You looked into the crystal ball and did that work upfront such that that's what every investor's dream is. It's just like, oh, like there is a pot of gold at the end of the rainbow. We're not just like pretending that the pot of gold at the end of the rainbow exists. 

Alfredo - 00:24:51: Right. Yeah. No, I think we had, we had kind of failed enough where we needed to do that for our own sake and ended up being, I think the career, yeah, I very much think that the correct thing to do. 

Jon - 00:25:02: Yeah, spot on. And so, you know, it sounds like you're back in San Francisco, you know, Lux is bleeding your seed. Can you talk a little bit about just like these 200 pages? Can you talk about what was your findings? What were the state of the market? And how did you and your co-founders in Latch seek to disrupt this kind of like status quo? 

Alfredo - 00:25:21: Totally. Yeah. So we have 200 pages of notes and the clear conclusion was biotech and biopharma is becoming incredibly important over the next 10, 20 years, but even short term is just exploding in importance, but also the data generated by the industry is exploding in size. Yet the tools being built to meet this supply are not very good. And I'm not talking not very good. I'm talking they're actually really, really bad. And meanwhile, thankfully, going back to my experience as a data engineer at Facebook and just being really interested in the data architecture and data infrastructure space, I knew what the state of the art looked like, which was actually very special at the time, even though for me, it was just kind of an interest, something I enjoyed reading about. It ended up being super important because I kept going to these conversations and then hearing like, hey, what are you doing to process your FASTQ files, your omics data, your genomics data? And they would tell me these tools that, hey, we have a hard drive and I send it to our bioinformatician and our bioinformatician downloads it to his Windows XP, that part I'm kidding, but we have seen it, but to their Windows computer. And then they run this desktop application and they send me back a CSV. And I'm like, what are you talking about? Imagine I'm coming off of Google's machine learning infrastructure. I'm coming off of Facebook's Di Query that I was telling you about, which is predecessor to Snowflake. And I'm hearing these things and I'm like, Google and Facebook are like, optimizing advertisements. And they have some of the best tools in the world. And meanwhile, you're trying to cure cancer. You're trying to cure heart disease and aging. And you have some tools that look like they're from 2005. What is going on here? And what we found is that the reason the tools for this category were so bad is because the data was so recent. If you think about NGS, Next Generation Sequencing, that was invented, really, and start exploding exponentially around 2011. 2010, I think, sequencing by synthesis by Illumina was released publicly around 2011. Like 2011, Google had about a trillion queries a year. Facebook had about a billion user graph. And meanwhile, biotech was still looking through microscopes and counting cells with their thumb. And suddenly, data starts exponentiating. And as with all exponentials, it started 10x-ing every two years. So 10 to 100, okay, I can still count more cells. 100 to 1,000, it's like, okay, it's tedious, but I can do it. 1,000 to 10,000, it's like, okay, this is getting out of hand. Two more years, 100,000 data points, a million data points, 10 million, 100 million. Someone just relieved 100 million cell data set. Well, not someone, actually. It was the Tahoe data set by Vivo Therapeutics. And it's like 100 million, right? And it just keeps 10x-ing every two years. And the market was not ready for this. They were still doing things in pen and paper. They were doing things in little scripts in their laptop. And suddenly, this exponential starts, they're not ready. And so the tools to meet that demand are just not up to par to the seriousness of the task to humanity and to just economically to these biopharma companies. And so we just saw that over and over again. And when we started figuring out what we could build to meet it, initially, it was like, well, we're going to have to start by processing that initial data into more downstream results, right? If that doesn't give you the final result, we're going to need to build some kind of workflow orchestrator to process these large files into more human-readable stuff. And so that was the original product that originated as the actual concrete first product. But really, it was always about that mission of, A, building better data analysis infrastructure for biopharma, and then, B, so that we could make biology radically easier to engineer and thus improve people's health and improve people's lives. And so that was the thesis that we came out of those three months with and that we started building around. 

Jon - 00:29:38: Badass. Badass. And it's so funny hearing your description of like, what do you mean you're just like downloading a CSV and then, you know, porting things over just like suit? And it's kind of like reminiscent of just like, you know, the lab note. It's still a thing, like a physical notebook. And then you just use that. It's very analog. As much as like, and I talk to my parents who are not in life science, they think that life science is like super, it's kind of like, they think it's kind of like Facebook, Google, kind of like super polished. But they just don't know. It's like, no, no, no, no, no. It's pretty low tech. Like, it's pretty low tech and pretty analog. So I can see that, like, you're just like, what is going on? Especially from like, coming from that perspective, it's like, what is stark contrast? But that means there's a lot of work to be done and the opportunities there. 

Jon - 00:30:30: Thanks for listening to this episode of The Biotech Startups Podcast with Alfredo Andere. In part four, we'll explore how LatchBio's product evolved after launch, how Alfredo and his team uncovered a new growth opportunity, and why niching down helped them to build faster. He also shares his approach to enterprise sales, what it means to be a trusted partner to customers, and how they're building for the next generation of life science researchers. If you enjoyed this episode, subscribe, leave a review, and share it with your friends. See you next time. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for The Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website, www.excedr.com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or Jons. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.