Artificial Antics
Artificial Antics is a podcast about Artificial Intelligence that caters to the skeptic and uninitiated. Join this unlikely trio Mike (the techy), Rico (the skeptic) and A.I. as they dive headfirst into the world of artificial intelligence. From debating the social implications and ethical concerns around AI to figuring out how to break into the lucrative AI market, no topic is off-limits.
And with A.I. on board, you never know what kind of shenanigans are in store. Will A.I. turn out to be the brains of the operation, or will it be the source of all their problems? Tune in to Artificial Antics to find out!
Artificial Antics
Swimming in Data, How Ravi Kurani Transformed Pool Maintenance with AI
Join us back in the lab for another innovative and inspiring conversation with Ravi Kurani, founder of Sutro and host of the “Liquid Assets” podcast. In this episode, Ravi shares how he turned his experience as a pool boy into a revolutionary AI-powered solution for pool maintenance. Learn how AI is transforming water technology, scaling hardware solutions, and driving efficiency in both product development and customer engagement. Packed with real-world insights and actionable advice, this episode is a must-listen for AI enthusiasts, business leaders, and innovators!
Chapter Markers:
00:00:00 Introduction and Episode Overview
00:01:21 Ravi Kurani’s Journey: From Pool Boy to Entrepreneur
00:05:18 Revolutionizing Pool Care with AI
00:12:20 Building and Scaling AI Hardware Solutions
00:20:02 Marketing and Customer Engagement with AI
00:22:08 Tips for AI Adoption in Businesses
00:26:22 Ravi’s Podcast and Final Thoughts
Products & Resources Mentioned:
➡️ Sutro AI Pool Monitor: https://mysutro.com/
➡️ Liquid Assets Podcast: https://liquidassets.cc/
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Episode mastered by: Nomad Studios (https://nomadstudios.pro)
Description: The team behind mastering the Artificial Antics podcast audio. Big shout out to Nick and the team! 🎉
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- Check out Ravi's podcast: https://www.liquidassets.cc/
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Natasha: [00:00:00] Welcome back to Artificial Antics, where Rico and Mike will talk about the implications and opportunities around
Natasha: artificial intelligence, machine learning, and deep learning.
For this episode,
Natasha: Swimming in Data, How Ravi Karani Transformed Pool Maintenance with AI, Rico and Mike are joined by Ravi Karani, founder of Sutro, to share how he revolutionized pool care with cutting edge AI. From his roots as a pool boy to developing an AI powered pool sensor, Ravi takes us through his fascinating journey and offers insights into the intersection of water technology and artificial intelligence.
Rico: What's up everybody. Welcome to another exciting episode of artificial antics. And we are here with Ravi Karani and he's from a company called my Sutro. And he's also a podcast host of liquid assets. you want to say a quick hi, Mike, and then we'll pass it over to Ravi.
Mike: Hey Rico. Thanks for that. And Ravi, uh, thanks for being on the show with us. We've, uh, since we talked to you last time, I don't know, it was probably about a month and a half ago. Uh, we've been [00:01:00] excited to have you on. I think that your AI journey is, is super interesting, right? How you bridged engineering and, you know, really what water and swimming pool maintenance right into, um, you know, what you're doing now in your, in your business here.
Mike: So, uh, looking forward to, to hearing your story and, uh, if you want to just introduce yourself and, uh, and. You know, take it away.
Ravi: Yeah, yeah, yeah, for sure. Um, so thank you guys for having me on, by the way. Um, my name is Robbie Karani and I'm the founder of Sutro. a little bit about the backstory. Yeah. Let me, let me go ahead and kind of kick, kick into that. So I grew up a pool boy in, in Southern California. Um, my dad used to have a chain of pool and spa supply stores.
Ravi: And so I worked in the family business, right? I've done everything from acid washes to driving around a pickup truck to I ran one of my dad's stores in Huntington beach. Um, and yeah, that's a, I grew up a pool boy and, um, through, through that experience, I ended up, um, getting a degree in mechanical [00:02:00] engineering, um, worked a really short stint at, at NASA Ames, um, doing a project on jet engines to make them more efficient, got an MBA, got sent to India, which is kind of where the other half of the story ends up.
Ravi: Merging with the, with the pool boy side. So while we were in India, I was an associate at a venture capital fund. Um, the venture capital funds thesis was funding entrepreneurs who built products for people who earned less than 2 a day, right? So, um, Back then it was called the bottom of the pyramid. And so we were funding technologies like solar electrification, right?
Ravi: We would go into the villages of India and figure out, um, how to best light the villages so that, uh, the women in the, in the village are able to study at night, right? That was kind of a big problem. We were trying to solve that. We were trying to solve things around poverty alleviation. We invested in a company that got old Motorola flip phones and built a little Text messaging bot that we would give to rural farmers [00:03:00] so they could basically better see what weather patterns were, right?
Ravi: Otherwise they wouldn't, we wouldn't kind of have access to that information. Where the story kind of intersects is we saw a lot of deal flow around water, um, and predominantly around water filtration or fixing the water, um, because it has a large water problem. And what kind of hit me while. I was in India and seeing these deals come through is that nobody was really looking at the white space of water sensing, right?
Ravi: Everybody was like, Hey, we have this Jack or Jane of all trades of how we're going to fix the water, these amazing Brita filters or whatever, whatever filter they had. Um, but it's like water is different for each application. You use it, right? You're swimming pool that has a high chlorine content. It's not good for you to drink.
Ravi: You're drinking water probably isn't the best to grow your plants because you need some minerals and fertilizers inside there. Um, so. That example just shows that water is different and you need a sensing device to basically tell you what the quality, what the, what the parameters of the water are. Um, so we set out to [00:04:00] basically build the world's first affordable water sensor, because if we know what is wrong with the water, what the parameters of the water are, we can then better fix them.
Ravi: Um, tried selling it to the Indian government. Stupidest idea a startup can have. Um, especially for a young founder that doesn't know what the heck he's doing. And so ended up, uh, flipping the model around and came back full circle to my pool boy days, um, moved back to San Francisco and I was like, Hey, let's go ahead and sell the water sensor because I used to do water tests in the pool store.
Ravi: Right. You have that little test kit and you do the drops.
Rico: Right.
Ravi: Um, that's what I was doing. And so what we did was I just shrunk me as a pool boy into a robot and automated me, you know, a thousand times over. And that was kind of the like vision. Let's sell the pool monitor to a clientele. That's more wealthier.
Ravi: We can take the scheme or the margin off of the more wealthier product and then reinvested into the drinking water and agricultural stuff later. Um, and so that was kind of the model we set out to, uh, build the product. [00:05:00] Um, filed a button, a bunch of patents. I ended up selling the company in 2019 to a large water sanitation company out of Canada, um, and have been running day to day operations since.
Ravi: So that's, uh, Kind of what brings us to today. There's a lot of nuance in there, but I'll, I'll kind of pause there.
Rico: And at what point, uh, did you decide that like AI was the way to kind of like put it into your product? Like how did, how did that become part of the, the management of the device itself?
Ravi: You know, I'm kind of pissed off because I bought the domain sutro. ai back in 2015 before AI was even cool. Um,
Rico: Right. Right.
Ravi: AI pool boy, right? That's that, that was kind of always my like vision when I, when I first came back to California and so since, you know, since back then what we did in the pool stores, we would, we would write these things or we'd obviously have like an Excel sheet or this calculator.
Ravi: Um, and the whole vision of Sutro AI was how do we basically take all those calculations and in [00:06:00] real time, not only calculate what you have, right? Not, not just build a pure software program, but begin to learn what's about to happen and what has happened. Um, and so that was kind of always the initial vision of, of how we would build a quantitative AI tool using pure software.
Ravi: Chemistry and numerical analytics of like what the pH is, what the alkalinity is. Um, and yeah, that's kind of the first time that we actually, to answer your question was when we started thinking about AI,
Mike: Now, when, now, when was that? Like, so like you said, you, you bought that AI domain back in, I think you said 2015. You're right. Before AI was cool and it was just predictive analytics and everybody called it ML and not AI. Right. Yeah. And, uh, and all that other good stuff. So was that, was that around that time where you started to say, okay, well, you know, we built some software, we could do this.
Mike: Let's set it sort of move to that next level level of that. Like, let's predict the future. Right. Was that, was that around that same time?
Ravi: it was around the same time. And then I think [00:07:00] we very quickly learned that again, we didn't have the data set that was needed to actually do that. Right. We, we realized with a bunch of junior engineers that even, even at the machine learning. You know, gate, gate point or, or at the AI gate point, wherever we are.
Ravi: Um, we just don't have enough data. We didn't have enough data even for like the numerical analysis. Damn it. Like we have enough data to put in an Excel document to just run formulas on it. Um, and so we kind of set out to that first vision of let's go ahead and, you know, get tens, hundreds of millions of data points around water chemistry analysis.
Ravi: And then we'll sit down and figure out where. You know, what's, let's like, let's, let's look at the tea leaves and figure out kind of what, what all this, what all these numbers say. Um, and that's actually something that's happened as of recent. So probably about two years ago, um, we really started digging deeper and building vectors between the, Existing chemistry data that we have comparing that against the weather information we have alongside the actual device [00:08:00] tolerances, right?
Ravi: Because the device itself also has its own tendencies, like the lights, you know, one particular batch of LEDs will perform a particular way. Um, and so what we're able to do is based off of that output, we're not only able to like tune the device to see if it. You know, worked properly the same way that you're like iPhone, a LIDAR camera actually like re adjust itself based on the phone hardware, um, using software, but then also begin to actually build a product for the consumer to be able to predict what's going to happen with their pool.
Mike: yeah, that that's awesome. And now with so here, here's a quick question. So are you also build? Are you building those data sets with your own live devices where it's sort of building it back in and taking like, let's say, today's statistics, right and turning that into a data set and continuously refining the model or pretty much is it when it's deployed in the pool, it just does its thing and it doesn't really, um, you know, build on that data set.[00:09:00]
Ravi: Uh, it, it, it does the, it does the former, so it does actively keep, uh, providing and building on top of the data set. Um, the, the, the, the beautiful part of the way that the architecture is set up is that the learning happens, obviously in the cloud, right? It, it, it happens at where the large data set is, which is the culmination of all the devices basically sending its data up to the cloud.
Ravi: Um, each device only needs to be fixed on its own corrective measure, right? Of like where it stands within the world of all the devices. But the product that we give to the user is the output of what happens in the cloud, right? And so in a certain sense, you just have a bunch of these agents that are like contributing to the larger, you know, brain hive.
Ravi: Um, the brain hive gets smarter. It tells the agents like, Hey, look, you're, you're a little bit off. You need to correct yourself. Um, it corrects itself. And then it basically throws it back into the cloud to, uh, give to the user, um, just better chemistry information.
Mike: that's great. That's what I, that's what I figured you were going to say. [00:10:00] Honestly, if you didn't say that, I would have been extremely surprised. Right? So, so here, you know, I was just talking about, you know, you mentioned at scale, right? Like, you've got to scale this. I'm sure this is deployed all over the place, right?
Mike: Were there any challenges? Because that's 1 thing that I hear. Frequently. It's like anybody can do a little AI project and it's pretty straightforward. Um, what were some of the challenges and how you were able to get past them during that, you know, scaling that, that architecture out?
Ravi: Yeah, the, the question you're asking is how do you sell, distribute and manufacture tens of thousands of devices to generate millions, tens of millions of tests and then Store that, and then actually build the model to then actually do what I just said. Um, which is just a very age old marketing problem, right?
Ravi: It's like you, you, the marketing is an age old marketing problem. We have different tools today, right? You can sell on Facebook or on Google. Um, we don't advertise in newspapers anymore. Um, although [00:11:00] we did try that, we've tried like physical mail because, you know. We know where pools are. Um, the, the manufacturing is a very, you know, I wouldn't say it's a solved problem, but it's a problem that we've had again for, for the longest time.
Ravi: We've been manufacturing things from the days of the blacksmiths all the way to, you know, the last 50 years of manufacturing electronics in China. Um, and so each. You know, I say these things lightly, but each thing that I just says comes with its own infinite set of problems. Um, that one, you have to figure out how to turn infinite into finite.
Ravi: Um, and once you have the finite set of problems, you need to actually solve them. And then you need to make sure the product is robust. You have a customer support team that can actually support it. You can actually get a product from China to Los Angeles and then distribute it via USPS to show up at your door in five days.
Ravi: Um, you need the app to work. You can't have AWS crash. You can't have queuing issues, right? All of that stuff also needs to just work at its basal rate. Um, and then we can figure out the A. I. And so that's kind of, um, I guess all the things that unlock the A. I. Problem [00:12:00] for us to now to be able to solve.
Mike: Yeah. It's yeah, that makes sense. It's all the architecture and infrastructure put in place that sort of builds and I don't know if you said it or just popped it in my mind. It's almost like a pyramid and AI is at the very top of that. There's a lot of So, um, yeah, no, that, that absolutely, that absolutely makes sense.
Mike: Um, do you, you know, do you want to tell us a little bit more from, let's take that marketing side, that product side, and again, you know, I'm at, I'm asking sort of like a little bit for like a mini sales pitch for people that might be interested in this, right? Because I don't have a pool, but if I did, I think to myself, gosh, um, I don't want to build a workout routine or my grocery list.
Mike: And I use AI to do some of these things. So, um, you know, tell us a little bit more about that, about that product side of the product and how people can get their hands on it. If they're interested. Mm-Hmm.
Ravi: Yeah, for sure. Um, so I'll start there. Um, if you want to get the product, it's basically a floating laboratory in your pool that has an app that tells you your [00:13:00] pH, your free chlorine, your bromine, and your alkalinity three times a day. Um, and tells you exactly what to do and when to do it. And if you double click into that, we're, we actually have over 2000 barcodes in our app.
Ravi: So you can scan the chemicals that you buy from Leslie's, Home Depot, Lowe's, wherever you buy your chemicals, Amazon. Um, and as you scan those chemicals in, it'll tell you exactly with a little cute little image of exactly what to put in and when, because that's kind of the most complicated part of running pool, pool water chemistry. Um, double clicking into the product and you know, that's kind of the, that's like the headline of how it works. Um, A little bit on the technology, which is interesting to folks out there is we take exactly what you do in a water test. If you go to like a pool store and you get a test strip or you get a little liquid test strip and you put the little drops inside there, we do the exact same thing.
Ravi: We've just shrunk it down to 1 50th the size. Um, and so, like, imagine, you know, somebody at a pool store or a little, a little scientist, a lot like a laboratory [00:14:00] guy doing these tests. We've literally just taken him and shrunk it down. Um, and so that means that those little chemicals that you drop inside there that changes the water's color.
Ravi: We send you a cartridge that has those exact same chemicals to your doorstep once a month. Um, that's the, that's the replenishable and we do the exact same process that you as a user do, right? We dose 1 50th of a drop of water into a little small little pool of water that you can't even see that happens in the device.
Ravi: Um, and we use basically an led in a photodiode array to measure what the absorption and the color is of that. The same way that your human eye does actually. Um, Take that information. We calculate it in a big array and then we give you a pH value at the other side. Um, so that's kind of how the, how the technology works.
Ravi: It is a precision piece of robotics. We have five patents on it. Um, and we're the only autonomous device out there that uses what's called liquid colorimetry or liquid, liquid reagents, liquid assays to be able to actually measure your water chemistry. Um, the product [00:15:00] itself comes with a, with a little floating thing.
Ravi: That's about the size of size of my arm. Um, the. thing actually connects to what's to like a hub unit. And for all the nerds out there and the geeks that are interested in this, we use a 915 megahertz, which is a frequency that's able to move through concrete, rebar, and water because wifi and Bluetooth is not really the best set to actually transfer information.
Ravi: Um, that hub connects to your wifi and then that's how we get all the information into the app. So that's kind of how the, uh, architecture is built.
Mike: Awesome. Yeah, that, that's, that's pretty exciting. I mean, you know, I, it's one of those things where I knew that, um, I would be interested in it and I'm sure the listeners are interested in how that works. So thanks for kind of walking through the app and like what it does. I said there's gotta be an app, right.
Mike: That you have with this thing. And, you know, thi this sort of reminds me, Rico, when we had Tim Chiko on, he mentioned. You know, doing his own pool maintenance with chat GPT. This would be even easier. We need to let him know about this. [00:16:00] Hey,
Rico: We should
Mike: just drop this in, just drop this into your pool. He's super into AI.
Mike: So he'd be, he'd be super into this. Um, so, so let's jump a little forward to let's call it a modern AI. The ability to. The modern let's, or how about this? Let's call it conversational LLMs, you know, stuff that's happened in the past, you know, year. So, um, I, I'm, you know, obviously you've got a product, uh, that doesn't work in that way.
Mike: You're right. It works in kind of a different way with like higher level AI ML. Um, you know, uh, you know, over the past, let's say year and a half. What are some of the, some of the trends that you've seen that you might use in your daily life, or even let's say you're still working at the company, right? So what kind of, um, you know, challenges and opportunities are you seeing with AI adoption?
Mike: Let's say at the internal level, right. Where you all are using it to gain efficiencies as well as the external level where you're using it, you know, at an external, like for customer experience, let's say.
Ravi: Yeah. Yeah. We, we do use LLMs, [00:17:00] uh, Pretty, pretty regularly and prevalently throughout the organization. Um, let me just kind of walk you through a few of those. So like outside of the. The like quantitative AI and the quantitative ML that we do on the device side. Um, let's start from the viewpoint of the consumer, right?
Ravi: When the consumer interacts with our device, um, even before that, before they're actually looking to buy it, we use, um, AI and LLMs in our, in our marketing. Um, so the kind of two places that we, we actively use them is one in, in Klaviyo, Um, so we, we, we kind of backfill Klaviyo, which is an email marketing software to be able to sell, um, email marketing or sell our product through email marketing copy.
Ravi: That's a lot more better tuned for the actual demographic that we want. And so we use a, we use a combination of, of segment. io plus, um, an LLM tool that we've kind of, you know, packed together to make this email drip campaign. Um, another place that we use it as obviously in ads, ad copy [00:18:00] and, um, just running, running Kind of a multitude of images and copy kind of back to back.
Ravi: So we can make sure to see what's working, what's not that's like on the marketing side, if we move over to the customer support side, we have, um, an AI bot or an LLM bot on the, on the website. So. If you're on a particular page, if you're looking at our FAQ, if you've been sitting for a while on the technology page, we'll kind of drive comments or conversations.
Ravi: It's like, Hey, Mike, looks like you're looking at this microfluidic chip. Like, do you have any questions? This is how the technology works, right? Or looks like you're the subscriptions. We have two subscription plans. This is what we have. And this is what we have. What do you think is good for you? Right?
Ravi: Like, we'll, we'll kind of nudge you on a particular time because you might have a question there. Um, if that doesn't work, we'll obviously escalate to a customer support agent where it then sends a ticket in. Okay. Once it's sent a ticket to our customer support agent, we also use LLMs within Zendesk. Um, so we, uh, we were piloting this program called CODIF and CODIF basically takes the world [00:19:00] of Sutro tickets that we've had, right?
Ravi: The thousands of tickets that we've gone through and it will not only pull from the internal FAQ to help a customer support agent better answer your question, but it'll pull from the plethora of tickets that we've actually answered to say, Hey, customer support agent, you know, Mike and Rico. Two months ago had these similar questions that Jack is asking today.
Ravi: Take a look at these tickets if you want, and if not, here's a response that we put together. Please edit it before you like send it out. Don't just hit the send button. But here's a little nugget for you to like actually make your job easier. Um, we use it. So that's on the customer support side. Uh, when we move into engineering, we definitely use it on the project management side.
Ravi: So if we need to do summaries of Jira, we obviously at the end of the week, we'll kind of have it go through our Jira board to figure out exactly what tickets have been completed. What are red flags? What are green flags? What are wins? What are losses? Um, we use it in Slack. We use the Slack AI to help summarize channels.
Ravi: Um, [00:20:00] we. Also, our, our, our software development team is starting to get into using like replet and cursor. Um, and we use, use chat GPT regularly to like help us fill out code. Um, we're using chat GPT to build a QA QC bot that's able to like help us run through a sequence of QA QC checklists within a, within an app launch to make sure that, you know, we're, we're running QC a little bit more efficiently and a little bit more thoroughly.
Ravi: Um, that's it on the customer or that's it on the, Software side, which leads us to the device side, which is where we get into the quantitative AI. So that's kind of like, that's like the laundry list of places. I'm sure I'm forgetting a whole bunch of other
Mike: Yeah. There's gotta be, there's, there's probably a lot more, but, you know, I think that really covers, you know, full circle, right? Like the different pieces and how you're using it. And I'm really glad to hear it. Uh, you know, I, I, I, myself, I mean, Uh, especially on the engineering side with, with cur, cursor, repel it, like some of these new tools, like it's extremely good.
Mike: And, um, [00:21:00] we, we have something I call, um, app Wednesdays where we'll just build something small and usable. Like we'll take this like backlog thing. That's been sitting around forever. And it's like, Oh, we need a UI around this. And it's like, instead of. Saying, yeah, we'll just, you know, we'll schedule that sometime next year.
Mike: It's literal. I'll take the stakeholders and we'll like, talk about it, build it together. And we've had it be as fast as seven minutes and as long as two hours. Right. And they have something usable, right. Where they can pop up. Automate that quick thing, right? And, um, and so, uh, so yeah, that, that's really exciting stuff.
Mike: Um, you know, glad to hear, uh, that, you know, you're still finding uses, right? It's not just built into your core product and all right, that's cool. Uh, you're actually, you know, moving forward. And all the stuff that you're talking about makes perfect sense, right? Those workflows and, um, you know, we're using, uh, Zoho, but we, you know, Zia and whatnot, like there's some different, it's, it's, it's similar technology, right?
Mike: Um, so it makes a lot of sense. [00:22:00] So, um, you know, I know that we have a little bit of a time limit on this one, which is great. We're why we wanted to try out a shorter episode anyway. Um, Um, Ravi, is there anything that you wanted to share? Like, let's say, let's say tips for, um, business owners, right. That are, are looking to like, uh, maybe adopt AI.
Mike: They know there's an opportunity there, but they have worries and whatnot. Um, where would you, where would you suggest, uh, you know, those business owners start in their companies with, um, you know, AI adoption and opportunity.
Ravi: would recommend that it starts with a need and figure out what the need is. And what the process is, right? AI is really good at like doing the same thing over and over again for, for like the noobs out there, right? I mean, I think AI can do a lot of complicated things, but if you're just getting into it and you need to get your, you know, feet wet, um, Figure out a need, figure out a process, figure out what the steps in that process are, and [00:23:00] then go ahead and figure out how AI can automate it.
Ravi: Um, and many times it just, it comes, like you just said, with processes that are sitting in the backlog that have just really been draining on the organization. And when you unlock things like that, it just, it gives a breath of fresh air to everybody because everybody can kind of look up and like focus on something else.
Ravi: And so, uh, that's, that would kind of be my recommendation.
Mike: Awesome. That's good. Oh, go
Rico: earlier, I was just going to say, so early adoption of AI within the staff, let's say within the past couple of years, as it's starting to progress, was it easy to get people to adopt that, that, uh, that hadn't had experience with it, say in the software development
Ravi: I would say it's a mixed bag. Um, The media always paints things very negatively, right? It kind of is like, oh, it's going to take our jobs. And like, I feel like. Yeah, people may have a handful of reasons to like, not use it, right? They're just like, Oh, my privacy. What if I put an email inside there? Is it going to, you know, publish it on Google and then people can search it?
Ravi: Like there's obviously these fears is one. [00:24:00] Um, the second it is, is it is a little bit of an unknown, right? And there you're like any sort of tech adoption curve, you're going to have your early evangelists. You're going to have your, your main users and then your laggards. And so I think that's, True for, for anything.
Ravi: Um, it's up to the business and management to figure out how to use it. Right. Just like any founder or CEO, if you're not jumping in and learning that process from the beginning, and you don't know how it works yourself, then there's no way that you're going to convince your organization. If you yourself don't, you can't just be like, Hey, use AI, like use what?
Ravi: Like, what do you want me to do? Um, and so I think like. Being fluent in it yourself as, as any founder should, right? Like I've, I feel like I've done every single job in my organization outside of the ones I'm technically not able to do. Um, but like you, you have to know what you're working on. And if you don't, then you're not, you're not walking the walk and talking to talk
Rico: Right.
Mike: Yeah. That that's been, um, that's been one of the things I'm kind of grateful for at Clarity. [00:25:00] Our CEO is one of the biggest evangelists for it and not just use AI, but to your point, right? I always say, start with a problem, right? You never start with AI, you start with the problem, right? And then like you said, that figure out the process.
Mike: So every time anybody's ever walked into my office and says, Hey, I need to do this thing. I think AI can do it. I say, okay. Okay. First things first, I've got two whiteboard walls here. Let's start drawing out your process and let's figure out what that is. Because, you know, uh, one of our friends of the show, Wendy, she says, you got to get your house in order first, right?
Mike: You have to understand how to do something efficiently. Before you want to automate it with AI and make it faster, right? Because it's, um, you may get, you may be more efficient, but you're going to, you're not going to be effective, right? You need it to be effective at the end. So no, that, that, that is great stuff.
Mike: Rico, do you have any other questions for Robbie?
Rico: I was just going to say, Robbie, if you want to plug your podcast real quick, I've listened to a couple of episodes and a lot of people don't know this, but, uh, my [00:26:00] daytime job, I actually work with, uh, financing, uh, water treatment plants. That's part of the, what I do. Uh, so I have a little bit interested in water and drinking water and I understand, you know, how important it is.
Rico: So I really respect the fact that one, you educate people on there and I've listened to a couple of the guests you've had on there. You have some very intelligent folks on there. You're a very intelligent person yourself. Um, so I will give you an opportunity if you want to plug your podcast real quick.
Ravi: Yeah, for sure. Um, I have a podcast called Liquid Assets, which is a podcast on the technology management and business of water. Um, we noticed that I listened to a bunch of tech podcasts, right? Like the, like the all in podcast and, um, WNYC. And there was none of that in the water industry. And like, even for somebody that works in water, I wasn't able to find anything that would just be engaging to listen to.
Ravi: Um, and so I, I just wanted to create it myself. And so we created Liquid Assets. You can find it at liquidassets. cc. Um, you can subscribe on there. We're also on Spotify, on YouTube, anywhere you get your podcasts from, um, you can listen to us.[00:27:00]
Rico: And then we'll put it in the show notes too, folks. So you'll be able to find it. Um, we'll definitely be plugging that for a bit. So, and we'll also add it to our newsletter as well.
Ravi: Amazing. Thank you.
Mike: Absolutely. Yeah. I love, I love the name liquid assets. That's fantastic. Um, you know, I, I, I haven't listened to it yet and I need to, so I'm going to, I'm going to check that out. Um, yeah, no, it would Ravi. Thanks for, for joining us on artificial antics. Uh, appreciate your insights. I love the, the journey, the story, how things really come together.
Mike: And, um, I think that, uh, you know, your suggestion and your tips for business owners are spot on. So, uh, with that, uh, Rico, you want to take us out?
Rico: Sure. So, uh, no, I just want to thank everybody for listening again. Ravi, thank you for your time. Uh, hopefully we'll get you back maybe in a few months and get an update on where everything is at. And, uh, I'm going to continue listening to your podcast cause I find it quite interesting. So thanks again.
Ravi: Awesome. Thank you guys for having me.
Mike: Awesome. Thanks Ravi. We'll talk to you later. Have a good [00:28:00] one. See ya.
Rico: Thanks everybody.
Natasha: That's it for episode 18. Thanks for joining us. Don't forget to like, subscribe, and let us know your thoughts in the comments. Stay connected with us on Twitter, Instagram, and LinkedIn for all the latest updates, and check out antics. tv or our YouTube channel at Artificial Antics. We'll see you back in the lab soon.
Natasha: We want to give a huge shout out to Nick and the team at Nomad Studios for mastering the Artificial Antics Podcasts. Want to level up your audio visual game? www. nomadstudios. pro
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