1 00:00:15,316 --> 00:00:24,156 Speaker 1: Pushkin. My family is really into fruit. On my kitchen 2 00:00:24,196 --> 00:00:28,556 Speaker 1: counter right now is a bowl with apples, pears, bananas, 3 00:00:28,596 --> 00:00:32,756 Speaker 1: and a mango, also an avocado technically a fruit. The 4 00:00:32,836 --> 00:00:35,436 Speaker 1: avocado and the pears are hard. They're not right yet, 5 00:00:35,676 --> 00:00:37,516 Speaker 1: and I'm afraid that if I look away for like 6 00:00:37,836 --> 00:00:41,316 Speaker 1: five minutes, they're going to jump straight from hard to rotten. 7 00:00:41,676 --> 00:00:45,036 Speaker 1: The bananas are turning brown but still totally edible. And 8 00:00:45,116 --> 00:00:48,276 Speaker 1: the apples and the mango they are just right. But 9 00:00:48,476 --> 00:00:52,796 Speaker 1: somebody probably better eat that mango today. It's not gonna last. Now. 10 00:00:53,036 --> 00:00:55,956 Speaker 1: There is a reason that I'm talking way too much 11 00:00:55,996 --> 00:00:59,116 Speaker 1: about my fruit bowl, and that is because it is 12 00:00:59,196 --> 00:01:02,196 Speaker 1: really hard to get fruit right. This is not just 13 00:01:02,236 --> 00:01:04,836 Speaker 1: a me problem. It is a massive problem for the 14 00:01:04,836 --> 00:01:07,396 Speaker 1: whole supply chain, all the way from the time the 15 00:01:07,436 --> 00:01:09,916 Speaker 1: fruit gets picked to the time winds up in a 16 00:01:09,956 --> 00:01:14,596 Speaker 1: bowl on my counter. It's a huge, multibillion dollar problem. 17 00:01:16,636 --> 00:01:19,556 Speaker 1: I'm Jacob Goldstein, and this is What's Your Problem, the 18 00:01:19,596 --> 00:01:22,636 Speaker 1: show where entrepreneurs and engineers explain how they're going to 19 00:01:22,716 --> 00:01:26,316 Speaker 1: change the world once they solve a few problems. My 20 00:01:26,436 --> 00:01:29,436 Speaker 1: guest today is Katherine Sizza. She is the founder and 21 00:01:29,516 --> 00:01:33,276 Speaker 1: CEO of Strella Biotech, and the problem Katherine is trying 22 00:01:33,276 --> 00:01:36,436 Speaker 1: to solve is this a huge share of the fruit 23 00:01:36,476 --> 00:01:39,636 Speaker 1: grown in America goes bad before it even gets to 24 00:01:39,676 --> 00:01:42,636 Speaker 1: the consumer. She learned this spect four years ago when 25 00:01:42,636 --> 00:01:45,676 Speaker 1: she was a junior at the University of Pennsylvania, and 26 00:01:45,756 --> 00:01:48,516 Speaker 1: by the time she graduated in twenty nineteen, she had 27 00:01:48,516 --> 00:01:51,836 Speaker 1: both invented a device to detect when fruit is ripening 28 00:01:52,156 --> 00:01:55,956 Speaker 1: and started a company to bring that device to market. Today, 29 00:01:56,076 --> 00:01:59,236 Speaker 1: her customers include some of the biggest apple and pair 30 00:01:59,316 --> 00:02:02,796 Speaker 1: packers in America. To start, can you just sort of 31 00:02:02,836 --> 00:02:06,636 Speaker 1: tell me, tell me how you came to start this company. Yeah. 32 00:02:06,716 --> 00:02:09,716 Speaker 1: I read a paper that said, of all food is 33 00:02:09,716 --> 00:02:12,516 Speaker 1: wasted before it's consumed, which I thought was the most 34 00:02:12,596 --> 00:02:14,796 Speaker 1: ridiculous thing I've ever heard of in my entire life. 35 00:02:16,236 --> 00:02:19,196 Speaker 1: And then that was quickly followed by guilt because I 36 00:02:19,276 --> 00:02:21,156 Speaker 1: was like, I don't even know where the food in 37 00:02:21,196 --> 00:02:25,196 Speaker 1: my grocery store comes from. So just after reading that paper, 38 00:02:25,236 --> 00:02:28,716 Speaker 1: walk to the grocery store and went up to the 39 00:02:28,716 --> 00:02:32,516 Speaker 1: produce manager and said, Hey, where did those peaches come from? 40 00:02:33,276 --> 00:02:36,996 Speaker 1: And what did they say? She said, I don't really know. 41 00:02:37,156 --> 00:02:41,876 Speaker 1: You should probably talk to the growers and I was like, uh, okay, 42 00:02:41,916 --> 00:02:44,916 Speaker 1: I'll go and do that. Realized peaches don't really grow 43 00:02:45,156 --> 00:02:49,076 Speaker 1: in Pennsylvania, but what does grow in Pennsylvania is apples. 44 00:02:49,196 --> 00:02:52,076 Speaker 1: So went to the apple growers and you're in college 45 00:02:52,076 --> 00:02:55,076 Speaker 1: at this point, yep, I was in college. I guess, 46 00:02:55,196 --> 00:02:58,076 Speaker 1: uh yeah, I started, you know, taking the train to Lancaster, 47 00:02:58,196 --> 00:03:02,276 Speaker 1: Pennsylvania and stuff instead of going to class at some point, 48 00:03:03,556 --> 00:03:06,116 Speaker 1: and do you what do you learn? Well? I learned 49 00:03:06,196 --> 00:03:08,196 Speaker 1: that an apple at a grocery store can be over 50 00:03:08,276 --> 00:03:10,436 Speaker 1: a year old by the time I eat it, like 51 00:03:10,476 --> 00:03:13,116 Speaker 1: a year since it's come off the tree. Yeah, yeah, 52 00:03:13,116 --> 00:03:16,636 Speaker 1: how does that work? Apples are put into these rooms 53 00:03:16,676 --> 00:03:20,116 Speaker 1: that kind of are like outer space where they're deoxygenate 54 00:03:20,276 --> 00:03:23,276 Speaker 1: kept very very cold, and in those types of conditions, 55 00:03:23,556 --> 00:03:27,396 Speaker 1: the apples can actually survive for that long. And so 56 00:03:27,596 --> 00:03:31,636 Speaker 1: why does that become relevant for what you end up doing? Yeah? 57 00:03:31,636 --> 00:03:34,756 Speaker 1: So sometimes these rooms are kind of a black box 58 00:03:34,796 --> 00:03:38,356 Speaker 1: because they're deoxygenated. A person who stores them, which is 59 00:03:38,396 --> 00:03:42,396 Speaker 1: called a packer, they have dozens of these rooms. They're massive, 60 00:03:42,476 --> 00:03:45,116 Speaker 1: they're filled with millions of pieces of fruit, and they're 61 00:03:45,196 --> 00:03:46,916 Speaker 1: kind of playing a little bit of a guessing game 62 00:03:46,956 --> 00:03:50,236 Speaker 1: of behind which door is the most ripe fruit. So 63 00:03:50,356 --> 00:03:53,156 Speaker 1: you'll have a room filled with five million apples. That's 64 00:03:53,196 --> 00:03:56,636 Speaker 1: a huge, huge room. And then sometimes they'll crack open 65 00:03:56,716 --> 00:03:59,916 Speaker 1: that door and all the apples apple sauce. And that's 66 00:03:59,956 --> 00:04:02,956 Speaker 1: like a million dollars for that operation in one really 67 00:04:02,956 --> 00:04:05,396 Speaker 1: bad day, and that happens. That happens just because they 68 00:04:05,396 --> 00:04:07,436 Speaker 1: don't know. They don't know that inside that sealed room 69 00:04:07,436 --> 00:04:10,076 Speaker 1: the apples are going bad. Yeah, so you learn all this, 70 00:04:10,476 --> 00:04:13,436 Speaker 1: what do you do? So? Uh, you know, my backgrounds 71 00:04:13,436 --> 00:04:16,036 Speaker 1: in molecular biology, and I was thinking, well, these are 72 00:04:16,036 --> 00:04:19,116 Speaker 1: biological organisms. Maybe we should kind of treat them as such. 73 00:04:19,156 --> 00:04:21,916 Speaker 1: Maybe we should listen to them, see what they're telling us, 74 00:04:22,156 --> 00:04:24,356 Speaker 1: Like what does that actually mean? As you're learning about this, 75 00:04:24,476 --> 00:04:26,996 Speaker 1: like how do you listen to an apple? All the 76 00:04:27,076 --> 00:04:30,316 Speaker 1: fruit on a tree ripens at the same time, there's 77 00:04:30,316 --> 00:04:33,516 Speaker 1: some sort of evolutionary advantage to this happening. And the 78 00:04:33,556 --> 00:04:36,836 Speaker 1: way that produce do this is by communicating with each other. 79 00:04:37,156 --> 00:04:39,676 Speaker 1: If you've heard the saying one bad apple spoils the bunch, 80 00:04:39,916 --> 00:04:42,876 Speaker 1: it's totally true, and it's because it's telling everybody else, 81 00:04:42,916 --> 00:04:45,356 Speaker 1: Hey I'm ripening, Hey I'm going bad, and the way 82 00:04:45,396 --> 00:04:49,596 Speaker 1: that they communicate with each other is through gas emissions. Okay, 83 00:04:50,796 --> 00:04:55,396 Speaker 1: ethylene right in particular, this gas ethylene, that's right in particular, 84 00:04:55,396 --> 00:04:59,156 Speaker 1: athlete ethylene is what just like carbon and hydrogen. It's 85 00:04:59,196 --> 00:05:02,876 Speaker 1: just like some boring gas. Yeah, it's uh yeah, I 86 00:05:02,916 --> 00:05:05,716 Speaker 1: guess it's kind of boring, but I don't know it's 87 00:05:05,756 --> 00:05:09,036 Speaker 1: a big deal, sad word. How would you characterize ethylene? 88 00:05:09,236 --> 00:05:13,996 Speaker 1: What's ethylene? Like? Symmetrical? Symmetrical? Very good? Okay, So basically 89 00:05:14,036 --> 00:05:17,396 Speaker 1: ethylene is a signal that a ripe apple puts out, 90 00:05:17,956 --> 00:05:20,956 Speaker 1: and all of the other apples understand that signal to mean, 91 00:05:21,036 --> 00:05:24,596 Speaker 1: oh I better get ripe now too. Yeah. And actually 92 00:05:24,636 --> 00:05:29,156 Speaker 1: it's a cross fruit communication as well. So I don't 93 00:05:29,156 --> 00:05:32,396 Speaker 1: know if your grandma ever put you know, a tomato 94 00:05:32,676 --> 00:05:34,916 Speaker 1: and a banana and a paper bag together to ripen 95 00:05:35,116 --> 00:05:37,876 Speaker 1: one of them faster. That's the way to do. Is 96 00:05:38,236 --> 00:05:40,636 Speaker 1: they communicate with each other using athlene and they ripe 97 00:05:40,636 --> 00:05:43,156 Speaker 1: and faster. Can you just list off the fruits that 98 00:05:43,796 --> 00:05:47,956 Speaker 1: communicate using ethylene? Yeah, so some of them are avocados, 99 00:05:47,996 --> 00:05:56,716 Speaker 1: apples obviously, bananas, Kiwi's pears getting more exotic, per simmons, tomatoes, Yeah, 100 00:05:56,836 --> 00:05:59,276 Speaker 1: a lot of tropical fruits. So you learn all this, 101 00:05:59,396 --> 00:06:04,476 Speaker 1: you're reading papers about fruit communication. What do you do? So, 102 00:06:04,556 --> 00:06:08,036 Speaker 1: thinking about fruits as a biological organism, let's listen to them, 103 00:06:08,076 --> 00:06:11,596 Speaker 1: Let's hear what they're saying through measuring ethylene gas, and 104 00:06:11,596 --> 00:06:13,836 Speaker 1: then figure out how ripe they are. I mean, when 105 00:06:13,836 --> 00:06:15,876 Speaker 1: you put it that way, it seems kind of obvious, right, 106 00:06:15,916 --> 00:06:18,036 Speaker 1: It seems like weird that some college student who got 107 00:06:18,076 --> 00:06:20,756 Speaker 1: interested in it would do a thing that nobody had 108 00:06:20,756 --> 00:06:23,276 Speaker 1: done before. Had somebody done this before? Why wasn't this 109 00:06:23,316 --> 00:06:26,236 Speaker 1: already happening? If not? Yeah? Absolutely, Ethylene has been known 110 00:06:26,276 --> 00:06:28,636 Speaker 1: about since the seventies actually, and there are a ton 111 00:06:28,676 --> 00:06:31,636 Speaker 1: of awesome universities that have published a lot of studies 112 00:06:31,676 --> 00:06:36,076 Speaker 1: on it. It comes down to the technology. So apples 113 00:06:36,116 --> 00:06:38,756 Speaker 1: and produce aren't a really messy environment, if you will. 114 00:06:38,876 --> 00:06:42,396 Speaker 1: There's a lot of pesticides, treatments, all sorts of things 115 00:06:42,916 --> 00:06:45,636 Speaker 1: in that environment, and so it's very hard for a 116 00:06:45,676 --> 00:06:49,676 Speaker 1: typical sensor to pick up just that tiny signal that 117 00:06:49,916 --> 00:06:53,116 Speaker 1: produce is emitting. So it's hard to measure it. It's 118 00:06:53,116 --> 00:06:55,556 Speaker 1: hard to actually do at some level. Yep. But you 119 00:06:55,596 --> 00:06:57,276 Speaker 1: set out to do it. You were like, well, maybe 120 00:06:57,276 --> 00:07:00,356 Speaker 1: I can figure it out. Yeah, well, I mean coming back, 121 00:07:00,436 --> 00:07:03,156 Speaker 1: I only know molecular bio at the end of the day. 122 00:07:03,196 --> 00:07:05,516 Speaker 1: So it was like, okay, you know what fruits have 123 00:07:05,596 --> 00:07:08,596 Speaker 1: been sensing and signaling athylene for millions of years. Why 124 00:07:08,636 --> 00:07:12,436 Speaker 1: don't we is that mechanism that they use? And so 125 00:07:13,116 --> 00:07:14,476 Speaker 1: at the end of the day, what we did was 126 00:07:14,596 --> 00:07:18,956 Speaker 1: essentially hack a fruit, if you will, and digitize its output, 127 00:07:19,356 --> 00:07:21,756 Speaker 1: and then we just convert that into a digital signal 128 00:07:21,876 --> 00:07:24,836 Speaker 1: that we can use to predict the ethylene concentration. So 129 00:07:24,916 --> 00:07:29,116 Speaker 1: the key idea that you had that maybe nobody had 130 00:07:29,116 --> 00:07:32,156 Speaker 1: had before was let's use the same protein that fruit 131 00:07:32,316 --> 00:07:36,556 Speaker 1: uses to perceive ethylene in our sensor to pick up ethylene. 132 00:07:36,676 --> 00:07:40,476 Speaker 1: Totally evolutionists smarter than we are. Like, is there a 133 00:07:40,476 --> 00:07:42,596 Speaker 1: moment there if you figuring it out? Was it easy? 134 00:07:42,796 --> 00:07:45,996 Speaker 1: Was it hard? You know? I wish there were kind 135 00:07:45,996 --> 00:07:47,796 Speaker 1: of light bulb moments. I think a lot of the 136 00:07:47,876 --> 00:07:51,956 Speaker 1: times it's the lightbulb moment is taken over by some 137 00:07:52,076 --> 00:07:54,716 Speaker 1: big failure that you have to then co and solve. 138 00:07:55,276 --> 00:07:57,676 Speaker 1: And then also when things work, you're like okay, and 139 00:07:57,756 --> 00:07:59,716 Speaker 1: you don't realize that. You go eat a burrito or 140 00:07:59,756 --> 00:08:02,596 Speaker 1: whatever and don't realize that this was a big moment. Yeah, 141 00:08:03,876 --> 00:08:06,036 Speaker 1: were there any eating a burrito moments that you didn't 142 00:08:06,036 --> 00:08:08,396 Speaker 1: realize that the time, we're big moments, but that looking back, 143 00:08:08,396 --> 00:08:11,316 Speaker 1: you're like, oh, that was actually a big moment. Yeah. Absolutely, 144 00:08:11,316 --> 00:08:14,636 Speaker 1: it was. So the first year that we put sensors 145 00:08:14,636 --> 00:08:18,116 Speaker 1: in the real world just to see what happened. We 146 00:08:18,116 --> 00:08:20,756 Speaker 1: were getting really nice data and I had kind of 147 00:08:20,756 --> 00:08:22,756 Speaker 1: downloaded it and put it in Excel and looked at 148 00:08:22,756 --> 00:08:25,916 Speaker 1: it and saw like really nice ethylene curve. I was like, 149 00:08:26,116 --> 00:08:27,836 Speaker 1: that looks great, and then I went to go eat 150 00:08:27,836 --> 00:08:33,156 Speaker 1: a burrito. And then a couple of weeks later, the 151 00:08:33,276 --> 00:08:36,196 Speaker 1: person whose room we had put the censor and said, 152 00:08:36,556 --> 00:08:39,436 Speaker 1: you know what, you saved me six hundred thousand dollars 153 00:08:39,436 --> 00:08:41,916 Speaker 1: this year. I would have totally opened that room way 154 00:08:41,996 --> 00:08:44,636 Speaker 1: late and had apple sauce apple. So it's like like 155 00:08:44,716 --> 00:08:47,676 Speaker 1: six hundred thousand dollars worth of apples would have gone bad, 156 00:08:47,756 --> 00:08:50,796 Speaker 1: would have spoiled. Yeah, that's right. This is around the 157 00:08:50,796 --> 00:08:54,116 Speaker 1: time you started the company. You started the company before 158 00:08:54,276 --> 00:08:57,556 Speaker 1: your senior year of college, and then did you finish 159 00:08:57,556 --> 00:09:00,796 Speaker 1: school as you were building the company. I did finish school, 160 00:09:01,236 --> 00:09:05,116 Speaker 1: namely because the school I went to had an entrepreneurial 161 00:09:05,156 --> 00:09:08,236 Speaker 1: prize that if I was still a senior I could 162 00:09:08,276 --> 00:09:11,076 Speaker 1: apply for and get money. So basically, you stayed in 163 00:09:11,116 --> 00:09:13,956 Speaker 1: school in order to fund your business. That's right. I 164 00:09:13,956 --> 00:09:15,596 Speaker 1: mean I was paying a lot of tuition. Got to 165 00:09:15,636 --> 00:09:18,476 Speaker 1: make them money back somehow, And did it work? Yeah? 166 00:09:18,476 --> 00:09:22,156 Speaker 1: It did. By the time I graduated, we had received 167 00:09:22,276 --> 00:09:26,076 Speaker 1: over half a million dollars of just money through pitch 168 00:09:26,116 --> 00:09:28,396 Speaker 1: competitions and things like that. Well, if you got half 169 00:09:28,396 --> 00:09:30,316 Speaker 1: a million, I mean, I know tuition is expensive, but 170 00:09:30,356 --> 00:09:31,876 Speaker 1: I feel like you must have made a profit on 171 00:09:31,916 --> 00:09:34,996 Speaker 1: college if you got five hundred thousand. Yeah, I think so, 172 00:09:35,156 --> 00:09:40,316 Speaker 1: I hope, So, I hope. So Okay, So this isn't 173 00:09:40,316 --> 00:09:42,076 Speaker 1: that long ago. This is like a few years ago, 174 00:09:42,356 --> 00:09:45,396 Speaker 1: Like where are you today? Tell me about the company today. 175 00:09:45,596 --> 00:09:49,636 Speaker 1: So today we are fifteen folks, fifteen the smartest people 176 00:09:49,636 --> 00:09:53,036 Speaker 1: I've ever met. We work with seventy two percent of 177 00:09:53,076 --> 00:09:55,516 Speaker 1: all the Apple and Pair folks. And now we're expanding 178 00:09:55,556 --> 00:09:58,956 Speaker 1: into other parts of the market, whether that's different commodities 179 00:09:59,036 --> 00:10:02,476 Speaker 1: or different parts of the supply chain like retail and imports. 180 00:10:02,596 --> 00:10:04,716 Speaker 1: And how does it work as a business, Like do 181 00:10:04,796 --> 00:10:07,356 Speaker 1: you sell them your devices? Or is it like a 182 00:10:07,876 --> 00:10:10,916 Speaker 1: subscription and like more or less how much do they pay? 183 00:10:11,836 --> 00:10:14,796 Speaker 1: Frankly speaking, our customers are way too busy to worry 184 00:10:14,796 --> 00:10:17,556 Speaker 1: about something like ethylene, and they certainly don't have time 185 00:10:17,556 --> 00:10:20,796 Speaker 1: to interpret a sensor or a reading. So the most 186 00:10:20,836 --> 00:10:23,036 Speaker 1: important thing for us is the decision that we help 187 00:10:23,116 --> 00:10:25,516 Speaker 1: them make, which is which room to open first? So 188 00:10:25,676 --> 00:10:28,676 Speaker 1: our business model reflects that. So we just charge a 189 00:10:28,716 --> 00:10:32,596 Speaker 1: subscription service where you get access to Stralla monitoring per 190 00:10:32,676 --> 00:10:36,116 Speaker 1: room per year. Also in this industry, usually instead of 191 00:10:36,116 --> 00:10:39,156 Speaker 1: looking at a dashboard and making a decision, our customers 192 00:10:39,196 --> 00:10:40,956 Speaker 1: call us and say, hey, I'm about to open a 193 00:10:41,036 --> 00:10:43,276 Speaker 1: room full of galas, which one should I do? That 194 00:10:43,476 --> 00:10:46,476 Speaker 1: is old school. They call you like on your cell yeah, 195 00:10:46,516 --> 00:10:48,836 Speaker 1: and like more or less? How big is your business now? 196 00:10:48,956 --> 00:10:51,996 Speaker 1: Just like order of magnitude? How much revenue do you have? 197 00:10:53,516 --> 00:10:56,316 Speaker 1: Can I not say? Yeah? You can not say that? Like, 198 00:10:56,396 --> 00:10:58,796 Speaker 1: what's another way of talking about it? Yeah? So we 199 00:10:58,916 --> 00:11:02,116 Speaker 1: charge five thousand dollars per room per year, and today 200 00:11:02,156 --> 00:11:06,916 Speaker 1: we've monitored over two billion pieces of fruit, more than 201 00:11:06,996 --> 00:11:12,316 Speaker 1: two billion pieces of fruit, mostly apples and papers. After 202 00:11:12,356 --> 00:11:16,836 Speaker 1: the break the next frontier avocados, I'm gonna be honest, 203 00:11:16,956 --> 00:11:18,836 Speaker 1: I got a lot of skin in the avocado game. 204 00:11:19,116 --> 00:11:29,396 Speaker 1: Steaks are very high from me on this one. That's 205 00:11:29,436 --> 00:11:31,436 Speaker 1: the end of the ads. Now we're going back to 206 00:11:31,436 --> 00:11:34,196 Speaker 1: the show. The place where you started is apples and 207 00:11:34,276 --> 00:11:37,196 Speaker 1: pears when they're in this kind of long term storage 208 00:11:37,196 --> 00:11:39,076 Speaker 1: in a sealed room, right, Like, that's the sort of 209 00:11:39,316 --> 00:11:42,476 Speaker 1: problem you have solved so far. What is the next 210 00:11:42,476 --> 00:11:45,916 Speaker 1: problem you're trying to solve? So the next problem is 211 00:11:45,956 --> 00:11:48,116 Speaker 1: the further you go down the chain, the less information 212 00:11:48,196 --> 00:11:50,916 Speaker 1: you have. So by the time you get to our retailer, 213 00:11:50,996 --> 00:11:53,956 Speaker 1: they're not experts in apples or keyways or pairs. And 214 00:11:54,036 --> 00:11:56,476 Speaker 1: that's the most kind of saddest part of it, right, 215 00:11:56,556 --> 00:12:00,676 Speaker 1: All this time, money, resources were spent to get this 216 00:12:00,796 --> 00:12:02,956 Speaker 1: piece of fruit to the shelf and then it goes 217 00:12:02,996 --> 00:12:05,836 Speaker 1: bad there. That's like the worst part. Almost made it. 218 00:12:05,956 --> 00:12:09,556 Speaker 1: We went so far totally, And obviously that's a huge 219 00:12:09,556 --> 00:12:12,116 Speaker 1: business impact too, right, because the retailer is spending a 220 00:12:12,196 --> 00:12:14,196 Speaker 1: huge amount of money getting this stuff there and then 221 00:12:14,196 --> 00:12:17,236 Speaker 1: it's going bad right at the last minute. Yeah, So 222 00:12:17,876 --> 00:12:20,036 Speaker 1: how do you solve that? It's super hard. I mean 223 00:12:20,076 --> 00:12:21,996 Speaker 1: there's a lot of there's a lot of things we're 224 00:12:21,996 --> 00:12:26,636 Speaker 1: still working on like implementation. So for example, we've sat 225 00:12:26,716 --> 00:12:29,636 Speaker 1: with apples for six to eight months sometimes before they 226 00:12:29,676 --> 00:12:31,796 Speaker 1: got to the retailer, and so we can use that 227 00:12:31,836 --> 00:12:34,556 Speaker 1: same data that we collected using that sensor to inform 228 00:12:34,596 --> 00:12:37,636 Speaker 1: the retailer. But the question is, you know, is a 229 00:12:37,676 --> 00:12:41,116 Speaker 1: supplier cool with that? What if you know what's in it? Like, 230 00:12:41,196 --> 00:12:44,116 Speaker 1: how do we integrate into a retailer? Right, We're working 231 00:12:44,196 --> 00:12:49,276 Speaker 1: with huge, massive dinosaur systems that are sometimes not really 232 00:12:49,316 --> 00:12:53,036 Speaker 1: conducive to adding information. So you're saying the problem in 233 00:12:53,316 --> 00:12:55,556 Speaker 1: sort of moving your business to different parts of the 234 00:12:55,596 --> 00:12:58,636 Speaker 1: supply chain is not the technical problem, like not sensing 235 00:12:58,636 --> 00:13:00,596 Speaker 1: ethylene when it's not a sealed room or whatever. It's 236 00:13:00,596 --> 00:13:03,796 Speaker 1: just dealing with like the supplier's relationship with the retailer 237 00:13:03,836 --> 00:13:06,716 Speaker 1: and the retailer software. It's like the non technical piece 238 00:13:06,756 --> 00:13:09,916 Speaker 1: that's actually the problem. Oh, technical pieces problems too, Right. 239 00:13:09,956 --> 00:13:12,316 Speaker 1: You know, we're going from environments that are literally called 240 00:13:12,436 --> 00:13:15,156 Speaker 1: controlled atmosphere. You know, that's the best place to put 241 00:13:15,156 --> 00:13:18,116 Speaker 1: a piece of technology, right because because the sealed room 242 00:13:18,156 --> 00:13:22,276 Speaker 1: is so controlled, like, it's really easy, relatively speaking, to 243 00:13:22,396 --> 00:13:24,956 Speaker 1: perceive a change in the level of ethylene. Right, It's 244 00:13:24,996 --> 00:13:27,116 Speaker 1: like the perfect place to do it, and then you're 245 00:13:27,116 --> 00:13:28,716 Speaker 1: out in the real world and there's weird things happening 246 00:13:28,716 --> 00:13:30,476 Speaker 1: and there's different kinds of fruits and they're opening the 247 00:13:30,476 --> 00:13:32,876 Speaker 1: box and they're closing the box and whatever, and like 248 00:13:33,436 --> 00:13:36,756 Speaker 1: does that mess up your sensing system? Yeah? Absolutely, I 249 00:13:36,796 --> 00:13:39,556 Speaker 1: mean we have to put a lot more math behind it. 250 00:13:39,596 --> 00:13:41,676 Speaker 1: We have to have way bigger data sets that we 251 00:13:41,756 --> 00:13:44,396 Speaker 1: collect to make sure that we're actually making the right decision. 252 00:13:45,116 --> 00:13:47,356 Speaker 1: And is that something you haven't quite cracked yet, the 253 00:13:47,476 --> 00:13:50,116 Speaker 1: sort of beyond the sealed room piece of it. So 254 00:13:50,156 --> 00:13:53,876 Speaker 1: we're working in Kiwi's right now, So we're cracking Kiwis 255 00:13:53,996 --> 00:13:57,476 Speaker 1: and then we're looking right now into bananas and avocados. 256 00:13:57,676 --> 00:14:01,916 Speaker 1: Let's talk about avocados. I feel like avocados are perhaps 257 00:14:01,996 --> 00:14:05,036 Speaker 1: unique in the fruit world in the amount of frustration 258 00:14:05,156 --> 00:14:08,836 Speaker 1: they generate for consumers. So I think if you could 259 00:14:08,876 --> 00:14:12,356 Speaker 1: crack avocados, that would be that would be a huge 260 00:14:12,396 --> 00:14:15,436 Speaker 1: breakthrough for me. I spend a lot on avocados, and 261 00:14:15,956 --> 00:14:18,796 Speaker 1: often they're not good even if I get them under ripe. 262 00:14:19,556 --> 00:14:22,596 Speaker 1: Somehow I miss the magic moment when they are ripe, 263 00:14:22,796 --> 00:14:25,796 Speaker 1: Like they go from hard to black on the inside, 264 00:14:25,796 --> 00:14:29,116 Speaker 1: and I never even knew. So tell me where you 265 00:14:29,156 --> 00:14:33,796 Speaker 1: are on avocados, Like, what's happening with your business and avocados. Yeah, 266 00:14:33,836 --> 00:14:36,276 Speaker 1: So what we're doing in avocados is they have a 267 00:14:36,316 --> 00:14:39,036 Speaker 1: little bit of a different supply chain than apples. So 268 00:14:39,076 --> 00:14:42,396 Speaker 1: apples are picked basically ready from the tree. Avocados are 269 00:14:42,436 --> 00:14:45,716 Speaker 1: picked underripe from a tree, and then they're shipped into 270 00:14:45,756 --> 00:14:49,236 Speaker 1: the US, for example, and they're artificially ripened actually using 271 00:14:49,276 --> 00:14:51,876 Speaker 1: that ethylene gas. So you put a bunch of underripe 272 00:14:51,876 --> 00:14:54,676 Speaker 1: avocados into a room and then you dose them with ethylene. 273 00:14:54,756 --> 00:14:58,596 Speaker 1: So similar to where the apples are stored, avocados are ripened. 274 00:14:58,876 --> 00:15:01,196 Speaker 1: And so what we do is we put our technology 275 00:15:01,196 --> 00:15:04,196 Speaker 1: inside these ripening rooms and we're able to better control 276 00:15:04,236 --> 00:15:09,076 Speaker 1: that ripening process. So the fruit is dosed awakened with ethylene, 277 00:15:09,156 --> 00:15:11,316 Speaker 1: if you will, and then it starts producing athlene on 278 00:15:11,356 --> 00:15:13,396 Speaker 1: its own. And so we capture those signals and we're 279 00:15:13,396 --> 00:15:16,036 Speaker 1: able to tell, hey, this guy is ready to go. 280 00:15:16,236 --> 00:15:17,996 Speaker 1: You know, it's time to send them down the chain. 281 00:15:18,196 --> 00:15:20,036 Speaker 1: But this one isn't quite there yet. Maybe we do 282 00:15:20,076 --> 00:15:22,356 Speaker 1: another treatment. Maybe we expose it to a little bit 283 00:15:22,356 --> 00:15:25,876 Speaker 1: of heat or temperature to get that ripening process going 284 00:15:26,356 --> 00:15:28,516 Speaker 1: and that way create a more consistent product at the 285 00:15:28,596 --> 00:15:30,076 Speaker 1: end of the day. Is that like the dream or 286 00:15:30,116 --> 00:15:32,556 Speaker 1: is that what you can already do? No, that's the dream. 287 00:15:32,596 --> 00:15:36,316 Speaker 1: That's what we're working on right now. Tell me why 288 00:15:36,316 --> 00:15:40,396 Speaker 1: it's hard. So avocados they ripen a lot faster. So 289 00:15:40,436 --> 00:15:43,516 Speaker 1: we're sitting with apples and pairs for a very long 290 00:15:43,516 --> 00:15:46,636 Speaker 1: time months, but avocados ripen in a matter of days. 291 00:15:46,756 --> 00:15:48,596 Speaker 1: And so that's kind of a really big challenge that 292 00:15:48,636 --> 00:15:51,036 Speaker 1: we're working on addressing right now, is having a sensor 293 00:15:51,036 --> 00:15:54,236 Speaker 1: that reacts super quickly to those gas emissions and is 294 00:15:54,276 --> 00:15:58,956 Speaker 1: able to quickly predict out what's going on. And so 295 00:15:59,036 --> 00:16:03,796 Speaker 1: it's basically you have to make predictions with less data, 296 00:16:03,956 --> 00:16:07,756 Speaker 1: which is hard for kind of obvious statistical reasons. Yeah, 297 00:16:07,796 --> 00:16:10,836 Speaker 1: and also, our whole sensor technology is designed around like 298 00:16:10,836 --> 00:16:13,436 Speaker 1: a slow, long process and now we're going into a 299 00:16:13,516 --> 00:16:15,836 Speaker 1: quick opening process. Like what part of it do you 300 00:16:15,836 --> 00:16:19,876 Speaker 1: have to improve to solve avocados. One of the problems 301 00:16:19,956 --> 00:16:24,796 Speaker 1: is sensitivity, so upping our sensitivity and the algorithms as well. 302 00:16:24,996 --> 00:16:27,876 Speaker 1: So in our storage rooms we collect once an hour, 303 00:16:27,996 --> 00:16:30,876 Speaker 1: for example, and we have to absolutely increase the amount 304 00:16:30,916 --> 00:16:33,716 Speaker 1: of data that we're ingesting in order to be able 305 00:16:33,716 --> 00:16:35,756 Speaker 1: to make a prediction in such a short period of time. 306 00:16:35,876 --> 00:16:38,196 Speaker 1: So in control the atmosphere, rooms is kind of sitting 307 00:16:38,196 --> 00:16:41,276 Speaker 1: there and be only measuring things. And that's cool when 308 00:16:41,316 --> 00:16:43,956 Speaker 1: you have months, but when you're dealing with a really 309 00:16:44,036 --> 00:16:46,956 Speaker 1: quick turn avocado, you have to kind of change that 310 00:16:47,036 --> 00:16:50,116 Speaker 1: process going from months to day's basically is it basically 311 00:16:50,316 --> 00:16:56,276 Speaker 1: exactly like you're almost there. I think the targets are 312 00:16:56,276 --> 00:16:58,476 Speaker 1: always moving, right, Like you get one thing done and 313 00:16:58,516 --> 00:17:00,556 Speaker 1: then a customer is like, well, let's make it do 314 00:17:00,636 --> 00:17:04,196 Speaker 1: this other thing. So it's moving goalposts, which I think 315 00:17:04,236 --> 00:17:06,156 Speaker 1: is always the hardest thing because sometimes you feel like 316 00:17:06,156 --> 00:17:08,036 Speaker 1: you've never gotten anywhere. I mean, let me ask you 317 00:17:08,156 --> 00:17:10,276 Speaker 1: a different way. Is most most of your business now 318 00:17:10,316 --> 00:17:13,156 Speaker 1: apples and pairs, like most of your revenue. Yep, most 319 00:17:13,196 --> 00:17:16,236 Speaker 1: of our revenues and apples and pairs, But our customer 320 00:17:16,276 --> 00:17:18,756 Speaker 1: segments are different. So we've got the packers who we 321 00:17:18,796 --> 00:17:23,196 Speaker 1: work with in storage rooms, We've got importers who's shipping 322 00:17:23,236 --> 00:17:26,356 Speaker 1: containers we monitor, and then we've got retailers so we 323 00:17:26,436 --> 00:17:29,476 Speaker 1: help them organize their inventory and always send the best 324 00:17:29,556 --> 00:17:31,996 Speaker 1: quality stuff to the shelf. Is there ever going to 325 00:17:32,076 --> 00:17:36,436 Speaker 1: be a day when you'll sell me your service. Basically, 326 00:17:36,476 --> 00:17:39,316 Speaker 1: I'll buy some little mini brick and put it in 327 00:17:39,396 --> 00:17:42,396 Speaker 1: my bowl of avocados and get a text on my 328 00:17:42,436 --> 00:17:45,716 Speaker 1: cell phone when it's time to eat an avocado. That's 329 00:17:45,716 --> 00:17:48,396 Speaker 1: a great question. I think the future could look like 330 00:17:48,476 --> 00:17:51,596 Speaker 1: your fridge, for example, telling you that something's going bad 331 00:17:51,636 --> 00:17:55,036 Speaker 1: in there. But you know, the further you go down 332 00:17:55,316 --> 00:17:58,476 Speaker 1: the chain, the cheaper. It's got to be more developed. 333 00:17:58,476 --> 00:18:00,916 Speaker 1: The technology has to be which is why we started 334 00:18:00,956 --> 00:18:04,436 Speaker 1: upstream right, because if you're monitoring millions of pieces of fruit, 335 00:18:04,956 --> 00:18:06,516 Speaker 1: you have the ability to have an R and D 336 00:18:06,636 --> 00:18:09,796 Speaker 1: budget included in your margin if you will right, you 337 00:18:09,876 --> 00:18:11,556 Speaker 1: have to be able to make something for like a 338 00:18:11,676 --> 00:18:14,876 Speaker 1: dollar that can perceive four avocados, which is way harder 339 00:18:14,876 --> 00:18:17,916 Speaker 1: than making something for what hundreds of dollars built to 340 00:18:17,956 --> 00:18:22,436 Speaker 1: perceive a million avocados exactly. So you think you'll get there? 341 00:18:22,756 --> 00:18:27,076 Speaker 1: I think so, yeah. I got time. A started earlier 342 00:18:31,036 --> 00:18:33,956 Speaker 1: in a minute the Lightning Round, including Catherine's tips for 343 00:18:33,996 --> 00:18:46,676 Speaker 1: solving hard problems and a discussion of underrated fruit. Okay, 344 00:18:46,756 --> 00:18:48,916 Speaker 1: let's get back to the show. We're gonna close with 345 00:18:48,956 --> 00:18:51,036 Speaker 1: the Lightning Round. How do you feel about doing just 346 00:18:51,156 --> 00:18:56,516 Speaker 1: a bunch of quick fun questions? Sounds good. What's one 347 00:18:56,556 --> 00:18:58,596 Speaker 1: piece of advice you'd give to someone trying to solve 348 00:18:58,596 --> 00:19:02,556 Speaker 1: a hard problem? Take the risk, just do it small steps. 349 00:19:02,956 --> 00:19:05,356 Speaker 1: Just figure out how to take that huge problem and 350 00:19:05,436 --> 00:19:09,116 Speaker 1: break it down into something that you can do today. 351 00:19:09,116 --> 00:19:12,076 Speaker 1: It's really about just every single day. What is a 352 00:19:12,076 --> 00:19:16,116 Speaker 1: difficult problem you're working on today? Then? How do I 353 00:19:16,156 --> 00:19:22,436 Speaker 1: get a retailer to pay me? Okay, good one? How 354 00:19:22,476 --> 00:19:24,316 Speaker 1: do you get a retailer to pay you? You could 355 00:19:24,396 --> 00:19:25,996 Speaker 1: change them on my podcast? Do you want to say 356 00:19:26,036 --> 00:19:29,876 Speaker 1: the name of the retailer that's not paying you? Oh? No, no, no, no, 357 00:19:29,876 --> 00:19:34,196 Speaker 1: no no no no, I can't. I'm not there yet. 358 00:19:34,236 --> 00:19:36,356 Speaker 1: Maybe like when I'm Elon Musk, I can like talk 359 00:19:36,396 --> 00:19:40,756 Speaker 1: shit about like big companies. So you started this company 360 00:19:40,796 --> 00:19:43,436 Speaker 1: when you were still in college, and you've grown it 361 00:19:43,476 --> 00:19:46,356 Speaker 1: and it's a real company now. One is one tip 362 00:19:46,436 --> 00:19:49,596 Speaker 1: you would give to other young women trying to start 363 00:19:49,676 --> 00:19:51,556 Speaker 1: companies or just trying to get people to listen to 364 00:19:51,556 --> 00:19:56,556 Speaker 1: their ideas. Oh that's a good question. So take whatever 365 00:19:56,556 --> 00:19:58,676 Speaker 1: attention you get and use it to pitch your product. 366 00:19:58,756 --> 00:20:01,196 Speaker 1: You know sometimes Eddie, you know, whatever, attention you get 367 00:20:01,236 --> 00:20:03,076 Speaker 1: if you're sticking out in a room. Use it to 368 00:20:03,076 --> 00:20:07,076 Speaker 1: your advantage and get people to buy into your technology 369 00:20:07,236 --> 00:20:11,076 Speaker 1: and you're offering and understand how you're impacting their business 370 00:20:11,076 --> 00:20:15,396 Speaker 1: in a good way. What is the most underrated fruit? Mangoes? 371 00:20:15,676 --> 00:20:18,876 Speaker 1: Mangoes are the best? Agree? Do they talk to each 372 00:20:18,916 --> 00:20:20,916 Speaker 1: other with ethylene? They do talk to each other with 373 00:20:20,956 --> 00:20:23,956 Speaker 1: ethylene and mangoes kind of like the hot girl on 374 00:20:23,996 --> 00:20:26,996 Speaker 1: the block right now, So expect more mangoes in the future. 375 00:20:27,196 --> 00:20:30,756 Speaker 1: Tell me more. It's the new it fruit. Yes, So 376 00:20:30,796 --> 00:20:34,956 Speaker 1: they're trying to actually ripen them similarly to avocados, and 377 00:20:34,996 --> 00:20:38,556 Speaker 1: so they're looking at it as a really big, you know, 378 00:20:38,676 --> 00:20:40,876 Speaker 1: commodity that's going to grow in the future. We love 379 00:20:40,956 --> 00:20:46,316 Speaker 1: mangoes in my house. What's the most overrated fruit? Banana? 380 00:20:46,396 --> 00:20:50,236 Speaker 1: There are way better varieties of bananas that we could 381 00:20:50,236 --> 00:20:53,676 Speaker 1: be eating. The one that we are used to is 382 00:20:53,956 --> 00:20:56,356 Speaker 1: definitely not the best one out there. Why aren't we 383 00:20:56,396 --> 00:20:59,316 Speaker 1: eating better bananas? Then that's a really good question. Why 384 00:20:59,356 --> 00:21:02,796 Speaker 1: are we still eating red delicious apples? We're just going 385 00:21:02,836 --> 00:21:05,396 Speaker 1: to ask you about red delicious apples? Why are we 386 00:21:05,396 --> 00:21:07,716 Speaker 1: still eating red delicious apples? They are so gross. When 387 00:21:07,716 --> 00:21:09,556 Speaker 1: I was a kid, that was like all you could get, 388 00:21:09,556 --> 00:21:11,796 Speaker 1: and now we have this whole wonderful world of apples. Well, 389 00:21:11,836 --> 00:21:14,956 Speaker 1: like what happened, Red Delicious apples are a supply chain product. 390 00:21:15,036 --> 00:21:17,916 Speaker 1: Those things can sit on your shelf for decades and 391 00:21:17,996 --> 00:21:19,796 Speaker 1: they can do the same thing in the supply chain. 392 00:21:19,876 --> 00:21:23,236 Speaker 1: So Red Delicious is a good example of we have 393 00:21:23,436 --> 00:21:25,956 Speaker 1: a lot of problems in our supply chain, and so 394 00:21:25,996 --> 00:21:28,076 Speaker 1: we're going to solve that with a really hardy variety 395 00:21:28,156 --> 00:21:30,596 Speaker 1: that might not necessarily be the best to eat, but 396 00:21:30,676 --> 00:21:36,756 Speaker 1: something we can transport pie or crumble pie. What about 397 00:21:36,756 --> 00:21:40,916 Speaker 1: you if I'm cooking crumble because like, I don't want 398 00:21:40,956 --> 00:21:45,076 Speaker 1: to deal with making a crust if I'm eating piem 399 00:21:45,636 --> 00:21:47,996 Speaker 1: that makes sense. It's hard to make a crust. I'm 400 00:21:47,996 --> 00:21:50,676 Speaker 1: intimidated by making a crust. It's so hard to make 401 00:21:50,716 --> 00:21:53,836 Speaker 1: a crust. If everything goes well, what's a problem you'll 402 00:21:53,876 --> 00:21:57,036 Speaker 1: be trying to solve in five years saying e commerce? 403 00:21:57,276 --> 00:22:01,036 Speaker 1: How do we get a perfect avocado simply by ordering 404 00:22:01,036 --> 00:22:05,316 Speaker 1: it online? Uh? I mean, produce is a very personal thing, 405 00:22:05,436 --> 00:22:09,316 Speaker 1: and you're kind of entrusting that to someone you've never met, 406 00:22:09,516 --> 00:22:11,476 Speaker 1: which is part of the reason why brick and mortar 407 00:22:11,516 --> 00:22:14,756 Speaker 1: store still exists, is for produce. Do you think you'll 408 00:22:14,756 --> 00:22:19,076 Speaker 1: ever leave Strella go work on some other problem? Yeah. 409 00:22:19,116 --> 00:22:22,036 Speaker 1: I started Estrella as something that I thought I could 410 00:22:22,076 --> 00:22:25,836 Speaker 1: accomplish while still in college. There were other ideas that 411 00:22:25,876 --> 00:22:30,276 Speaker 1: I've had that just require more capital and experience and knowledge, 412 00:22:30,876 --> 00:22:32,956 Speaker 1: and so I think, I think I want to be 413 00:22:32,996 --> 00:22:35,996 Speaker 1: a serial entrepreneur in the future. What do you want 414 00:22:35,996 --> 00:22:43,196 Speaker 1: to do next? Jerry's still out on that one. Katherine 415 00:22:43,196 --> 00:22:48,796 Speaker 1: Sizzov is the founder and CEO of Strella Biotech. Today's 416 00:22:48,796 --> 00:22:52,156 Speaker 1: show was produced by Edith Russolo, edited by Robert Smith, 417 00:22:52,316 --> 00:22:55,516 Speaker 1: and engineered by Amanda k. Wong. You can reach us 418 00:22:55,596 --> 00:23:00,036 Speaker 1: at problem at Pushkin dot FM, or you can find 419 00:23:00,076 --> 00:23:03,876 Speaker 1: me on Twitter at Jacob Goldstein. I'm Jacob Goldstein and 420 00:23:03,916 --> 00:23:06,436 Speaker 1: I'll be back next week with another episode of What's 421 00:23:06,436 --> 00:23:14,236 Speaker 1: Your Problem.