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Hey, what is up?
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Welcome to this episode of the Entrepreneur to Entrepreneur podcast.
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As always, I'm your host, Brian Lofermento, and I'm so excited about today's episode because we all, as entrepreneurs, we love technology, we love the future, we love what's coming next and, most importantly, we love making a difference across all industries, in all sectors, and that's why we have found an amazing entrepreneur who is making big waves in the public sector.
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Yes, so frequently, big waves in the public sector.
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Yes, so frequently we focus on the private sector, but today's entrepreneur is making a difference in cities across the United States and across the world, and that's because he is focusing on smart cities.
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We already know the concept of smart homes, of having your lights turn on and off automated, having your refrigerator tell you when the milk is running low all this cool new technology.
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Well, today's entrepreneur focuses on smart cities.
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Let me tell you all about him.
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His name is Leland Zhang.
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He's born and raised in Chicago.
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He's the CEO and technical founder of Spessland.
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His education includes one year at UCLA and one year at Northwestern University.
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Previously, he's been the assistant general manager at Rongwen Technology, which is where he managed over 10 international smart city projects across Brazil, Portugal and Hong Kong, integrating multilingual support and security features.
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Collaborating with local partners, he helped develop smart crossing, smart irrigation and smart restroom technologies all technologies that make all of our lives better in the cities that we live in.
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Leland is doing such cool work.
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I'm excited to hear more about it, so I'm not going to say anything else.
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Let's dive straight into my interview with Leland Zhang.
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All right, Leland, I am so very excited to have you here with us today.
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First things first, welcome to the show.
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Thank you very much.
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It's a pleasure.
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Heck, yes, I think that you do such cool work.
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I am definitely a tech junkie.
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I love any and all technology and automations that make our lives better, but we've got a lot to dive into today.
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So, first things first, kick us off.
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Take us beyond the bio.
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Who's Leland?
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How did you start doing all these cool things that you're up to?
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Yeah, sure, so I think you did a great introduction.
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Um, I started getting into the public sector through rolling this company.
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Um, I had a great experience there.
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I was introduced into many different international markets across the world.
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Uh, what smart city is?
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And then how you sort of talk, what are the pain points of governments and what they look for to help their citizens?
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Uh, and then afterwards, um, me and one of my co-founders, we thought of this idea of using the latest and best large language model technology to try and solve a lot of the issues that we were seeing that Smart70 still had on the software side.
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Yeah, I love that, especially because we're going to be talking about software side.
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Ai is incorporated into all of this research development.
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There's so much behind the scenes, at your company especially, but before we get there and before it makes sense for listeners, we've got to talk about what is a smart city.
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I tease just a few of those smart elements, such as smart crossing, smart irrigation, smart restroom technologies.
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What is a smart city?
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What is it in their current state and what is that future vision of what a smart city is?
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yeah, yeah, sure.
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So I think the easiest example that I like to give actually is smart lighting.
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It's the most common across the world and, um, it's much more impactful than I think most people realize.
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Uh, you have these things like these little hats on top of street lights.
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They'd call them control nodes, and what they do is that they collect all this data, this IoT data, such as the electricity, the voltage, perhaps the energy used, as well as maybe if they're failing or not, and then you send that to a central management platform and over there you can actually control, perhaps like the dimming of the streetlights, and so pretty much what you can do is you can save lots and lots of energies millions of dollars per year in a city as well as making sure that the streetlights always work, and that's.
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I think people don't really realize that if streetlights don't work and maybe the US that could mean higher rates of car crashes, things like that, but in countries like third world countries or maybe developing countries like Brazil, that would mean higher crime rates, etc.
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So it's quite a powerful thing.
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Yeah, I'm really glad that you're starting there with us, Leland, because as someone who I'm a sucker for cities I've lived in cities my entire life and when I drive down the road at night, you're right I mean, how the heck is a city going to stay on top of?
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Is every single street lamp working?
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And here in Tampa, Florida, I'll call our local government out a little bit We've got quite a few bulbs that are going bad at this moment in time and it's all the rage there's newspaper headlines about this, asking what's the city going to do?
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And so having that technology assist them, I can think of.
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That's increased efficiencies, it's just increased operations, because, to your point, how would they even be aware of these things?
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And that's where technology is the solution.
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So I want to go deeper into that.
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Why Is it from an operational perspective?
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Is it from a personal life perspective, the life of the citizens?
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Is it from a cost perspective?
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Lay us on all those whys, all those benefits of why we care about smart cities.
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Yeah, so actually sorry to answer your original question.
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Smart city at its core is digitalizing the entire city, managing, going from hardware to the data itself and then using that to help improve the city infrastructure operations.
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So usually there's several aspects Cost saving is definitely one, and then environmental friendliness for some cities is quite important, and I think the biggest thing is like the safety, the disaster prevention.
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So actually I mean, smart restrooms is really just more like quality of life, for example, but when you get into a critical infrastructure like bridges, gas pipelines, that becomes even more apparent there.
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Yeah, makes sense.
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I love these examples.
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We're going to get into the depth of what it is that you do with your company.
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But one more question with regards to the structure of smart cities is who's driving this?
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Why do we care about this?
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Obviously, there's so many benefits that you listed.
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I even love that consideration.
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Obviously, you live in this world so you can rattle all of these considerations off.
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But even decreased crime in area there's so many different benefits.
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So I would argue that probably we all want these in various capacities.
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But who's that driving force behind it?
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Is it governments that are saying, hey, this technology exists, let's engage with entrepreneurs like Leland, let's engage with companies like Spetsland?
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Or is it being driven by the private sector, showing cities and governments, hey, here's this opportunity, here's what we can do for you?
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hey, here's this opportunity, here's what we can do for you.
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Yeah, the latter is mostly true, I think.
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Most governments in the world they tend to, you know, be more traditional.
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They tend to be more stable.
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They're quite slow in their process and their technology adaptation.
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So a lot of times it actually involves a lot of what we call customer education on our side.
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So we have to show them the value and how this actually helps the citizens.
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That's what you're paying for.
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We actually give you higher in return.
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Uh, and that's sometimes even hard to quantify because, again, we're dealing with things like like safety and public well-being, which is hard to quantize a lot of times.
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Um, yeah, I hope that answers yeah, it definitely answers it, because that's the fun stuff.
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Now we're getting straight into your entrepreneurial journey and what it is that you do with your company.
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Because you talk about customer education, and you're right.
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Governments are doing governing that's exactly what they exist to do, whereas it's the private sector, the entrepreneurs.
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That's why this show exists.
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We're the ones that get to be those change agents in so many ways.
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So take me to that transition time in your career where you said hold on, there's a huge opportunity here.
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I want to be part of that solution and obviously you launched your own company there.
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Talk to us about that transition in your own life, personally and professionally.
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Yeah, so I think, personally, I really have always been looking into entrepreneurship.
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It's something I wanted to do.
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I wanted to make an impact.
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I want to build something big.
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Something I wanted to do.
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I wanted to make an impact.
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I want to build something big.
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Um, and then how the idea started really is that, uh, we always knew that there was a big um lack in technology software it's.
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It's the one that you've been using in smart city right now.
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It's just normal platforms, um, like, I think it's very common, uh, you see some data visualization on the platform, you can do some controls and they're still improving over time, but there's no big innovation that we see.
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And so when this large-limit model like ChatGBT came out, we saw this as quite, a very large tool and we were like, okay, this is definitely going to provide value in smart cities, but actually in the beginning, we couldn't think of like the most specific or the most valuable use case and that actually we found out over time over building and iterating and sometimes failing and throwing away functionalities.
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Yeah, that's the entrepreneurial journey right there by heart, is that we all face those ups downs, those twists, those turns, those pivots.
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What were some of those thoughts?
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Because you're right, I mean ChatGPT literally changed the world.
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Ai has existed in so many different capacities for decades, but for the first time ever, a large language model was at our fingertips, the end consumers.
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Every single day, people can use the power of this.
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I'm sure you, as a highly technical person, you see limitless possibilities when you look at the AI landscape.
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What were some of those considerations before you landed on the one that you chose?
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Yeah, so actually our way of thinking before was okay, we look at what ChatGPT or these large-scale models do best, and what they do best is textual generation.
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So we actually started with trying to look at the most difficult cases that governments need to do for technical sorry, for textual.
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So a lot of it was like policy or we did tender generation, but we actually found out over time that that's actually not the most useful for the government because they don't use it on a daily basis.
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So we really wanted to target what they use on a daily basis, and what we found out and what our product currently is is a little bit more counterintuitive is that we actually had to figure out how to use these large-scale models and integrate them and make them useful for large amounts of IoT, like big data, that are numerical data.
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So that's what we found out as we talked to these cities.
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As we build products over time, the product over time we realized more and more that what they're going to do every single day for these maintenance teams is that they need to make sure that the status of all their equipment is good.
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They want to see the trends in energy consumption Always.
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They want to see if there's any concerning trends.
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Overall, these are the things that matter to the city the most, and that what they're going to check on every single day, and so we tried to cater towards that instead.
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Yeah, I love that overview, especially because for me personally, Leland this is fun to have this conversation with you on the air is that, with the adoption of AI and behind the scenes, that all of my companies were integrating AI in so many different ways, I'd say the one change that it's done for me from a mindset perspective is shown me how much data is available at my fingertips, things that I've ignored for literally years.
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I mean this podcast, for example.
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What we quickly realized is wait, we can transcribe all of these podcasts.
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We now have a repository, a deep, extensive library of all of this entrepreneurial data.
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How do we make sense of it?
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And that's just one real life, practical example of how we're leveraging AI to take advantage of the things that we already have and we've already been amassing without realizing it.
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And so when you talk about the large amounts of data and you talk about IoT, Internet of Things for listeners who aren't as well-versed in that regard, Leland, a lot of people are probably thinking how much data can a city have with regards to lighting and crosswalks and traffic lights and restrooms?
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Walk us down that path of just how much data cities have and probably how much they don't even realize that they have at their disposal and, most importantly, what can we do with that data?
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So you obviously have a company that makes sense of a lot of that data.
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How do you make sense of it?
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What does that data give you insights into?
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Yeah, but I first want to say you hit it right on the head is that it's really that AI is so data hungry and actually makes the data all the more valuable.
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And it's true that Smart City it does great in collecting large amounts of data, and so what that data size looks like is I'll use this real example again.
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Let's say you have 100,000 streetlights, and each streetlight there probably has for us in our projects has maybe 65 data points.
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So, for example, that may be just the communication data, or that may be the electrical data, and then, for example, for failures, we have 20 different classifications of failures that a streetlight may have.
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So and then the data would be sent over, like these sort of mesh networks or whatever networks you use, probably once every five minutes is what we see most often.
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So if you do the math, that's 100,000 times 65 and then times however much data you get in a day, with a difference of every five minutes, and so you end up with hundreds of millions of points of data per year or perhaps even more.
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And that's just for street lighting.
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And then there's lots of other verticals.
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Like smart building is a great example is where a single building that has like maybe 12, 15 different systems in it, such as the plumbing, the electricity, the air quality, hvac, etc.
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Or security.
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So that's pretty much.
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It gives you a very large data size.
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What it can help you do is there's, for example, we do failure prediction.
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So that's really data hungry.
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You actually want as much data as possible is to give an accurate prediction on, maybe, when a certain type of failure may happen on a street light, so then you can prevent those things from happening beforehand.
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If you think that some street lights are very highly likely to fail, you can send out a maintenance team beforehand.
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On the other side, there's lots of trends.
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You can look at things like okay, why is there increased energy consumption in certain districts?
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And what you may find out is that, okay, maybe, uh, the root cause is that the cables are actually, um like, uh, old and sort of frayed, and then you're actually losing energy there.
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Uh, so there's there's a lot of um, smaller inherent issues that you won't be able to find unless you have, like, these large amounts of data.
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You constantly get updates from yeah, I love that, leland.
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It almost feels like you and I are definitely biased when it comes to the subject matter that we're talking about today, because clearly you and I really enjoy technology.
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But I'm thinking about my parents' generation.
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For example, when I first got it, my first ever smart home device was an Alexa device and, having that in my living room, my parents would say gosh, aren't you afraid she's listening all the time, and so that's a real life concern.
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And with smart home features, I'm sure that when people who aren't aware of smart city features think about it, they immediately jump straight to oh my gosh, they're collecting data.
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Are they seeing where my car is going?
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And, of course, the truth is, I mean, they're probably collecting data that none of us even know about, and whether we're taking advantage of that data to do better things with it or not, it's happening.
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So, leland, with those concerns in mind, I'm sure that it's something that you also face, even when you talk to prospective clients and cities and governments that you want to work with is that they obviously have these safety and privacy concerns.
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What's your response there?
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Because we do live in a world where people are more on edge and nervous about technology than ever before, so I'd love to hear how you navigate that with them.
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Yeah.
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So I think, first, I think it's good news for the normal sense that the governments that we've pretty much all the governments we work with they're very secure about their data.
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They really are unwilling to share this data, and so that's actually a key importance and a key differentiator that our, our company and uh has to take into consideration.
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And actually one of the hardest technical points of our product is that we actually have our products, so it's deployed entirely locally.
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What that means is that none of the data needs to pass through our hands, so the entire product is put on the government premise.
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The data physically cannot leave like that, like maybe one or two rooms, and so it actually can't travel elsewhere.
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And then the government themselves have tons and tons of security protocols built inside, and so the only ones that are actually having access to them are approved government officials that actually deal with this, this data, every single day.
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Because you're right, like if you have data on like the energy and the water or, for example, you can actually do great harm to the normal citizens by like hacking certain areas.
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And so, yes, yes, I think security is definitely one of our primary concerns.
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And just very quickly on the technical aspect why that's so hard for us is that models get stronger the bigger that they are, and so we actually had to make a very small model that can be deployed anywhere, and so we had to still guarantee the quality of our products, and that actually is like one of our biggest struggles.
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Yeah, I love those insights.
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Leland, it's so cool to hear about the fact that you have that foresight.
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You've already thought about all of these different things in a highly technical aspect.
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I'm sure that there's so many different moving parts that are always changing.
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I mean, you're talking about real time city data.
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You're talking about how people live their lives and also how the government operates.
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They've got so many different departments one that handles the roads, one that handles the water of a city.
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There's so many different moving parts and you communicate across all those.
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Talk to me about that building of that, because I'm sure that, when it comes to government stuff, cost efficiency is one of those things, and I know that behind the scenes, your team, you think about all of those.
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Talk to us about the actual deployment and the building of this in order to keep those costs low while also growing a really impactful business.
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Yeah, so building is like it's actually harder for us to raise funding actually because our business itself, by the model it's not as scalable as fast or perhaps it's more scary for people to look at.
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Okay, if the entire sales cycle to government, they may take like half a year to a year to actually end up with the final project, and governments are just by inherently hard to work with, hard to win over, and so we've actually had quite some difficulties building by keeping costs low.
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We always we actually work with lots of part-time people and everyone on our team has to be passionate about the problem that we're solving Because just by nature, I think you can, in terms of compensation, you can earn more by going into other startups.
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So really we try to have a very highly technical team because our product by nature is very technical, but we have to have everyone very passionate about the problem itself.
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Yeah, I love that Again.
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So many parts of what you're sharing with us today is the quintessential ingredients of an entrepreneurial journey, so seeing it come to life in a way that really positively impacts the lives of citizens.
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I love even the headline on your website as you talk about actually, I'm just going to read this for listeners, because they obviously can't see your website in real time like I can Leveraging AI expertise across government sectors to reduce costs and enhance public welfare.
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Leland, that's a big mission that you're on enhancing public welfare.
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I have the pleasure of being able to see your company in action on your website, which listeners.
00:19:24.445 --> 00:19:36.896
You already know we're going to drop those links at the end of today's episode, but, Leland, I can see a diagram where it shows hey, here's all the lamps that are flickering in your lights across the city, here's when the flickering started, here's when the flickering ended.
00:19:36.896 --> 00:19:43.820
And I can see that all of this happens through one of the most core parts of your service offering, which is GovChat.
00:19:43.820 --> 00:19:56.674
Talk to us about how Spessalent actually works, how you're bringing this technology to life so it's actually usable for these governments yeah, so um, we always start with the data first.
00:19:56.816 --> 00:19:59.088
So there's the function, the features and then there's the data.
00:19:59.088 --> 00:20:25.821
So we always start with data, we analyze um data so we we are able to have some sort of um sample data from governments themselves when we work and cooperate with them, and then we look at how this data can be seen from a data science perspective, how it can be used, and then, if you're asking for how the building itself works, there's a lot of data engineering that's involved in the integration with our product.
00:20:25.821 --> 00:21:03.630
And then, on the pure AI side, on the large range model side, there's lots of work because we are like really the industry, first in trying to get this large range model technology integrated within big data, and so we have to do lots of, lots of different creative techniques in order to try and make large range model understand how to translate from natural language to commands and how to interact with this large system, and then, after interact, then how can it extrapolate the value and the key insights to give to the final end city users.
00:21:04.414 --> 00:21:05.196
Yeah, I love that.
00:21:05.196 --> 00:21:10.587
Talk to us about how GovChat works for listeners who can't necessarily see the implementation of your technology.
00:21:10.587 --> 00:21:16.086
Walk us through the actual mechanics on the end user side, on the city government, employee side.
00:21:17.616 --> 00:21:32.069
Yeah, so on a feature perspective, there's what we call like natural language data query, so you can ask in natural language some very niche, specific data that you want, maybe streetlights 455.
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And then you can ask about some of those operational parameter at a certain point in time, and then pretty much the AI writes code in real time and then queries for the data that you need.
00:21:45.150 --> 00:22:01.508
And then there's also we call report generation, so cities often by compliance, needs to write a lot of reports to make sure everything is good, and so we help automate some of that process with report generation.
00:22:01.508 --> 00:22:18.201
And then we also have we call like predictive or optimization AI, and then that helps with I think I mentioned this before but predicting problems before they happen or optimization is saving that energy, saving that resource as much as possible.
00:22:19.096 --> 00:22:24.815
And then one of our core features as an extension of data query is troubleshooting.
00:22:24.815 --> 00:22:46.178
So after an issue happens, most of the time actually the government can know that an issue happened, but they don't know why or what caused it and it takes a lot and a lot of efforts on their side and they have to contact these engineers and it's a very long process usually on trying to find out why certain failure or issue occur, and so we try to automate that entire process.
00:22:46.178 --> 00:22:48.486
Is that the large areas model is trained to know.
00:22:48.486 --> 00:22:52.565
Okay, for the specific issue, sort of data do I need to look at?
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And then, after getting the data, then what conclusion can I make out of that data?
00:22:57.205 --> 00:23:22.923
and that sort of cuts out the entire in-between process for the government so that they now can go directly and solve the issue as fast as possible yeah gosh, leland, I really appreciate the functionality of your tool and of what you've built, because we've all become accustomed just in a few short years of having these human type conversations with the AI large language models that we use, and that is incredible to think about.
00:23:22.923 --> 00:23:30.484
Probably five years ago, if someone wanted a report of a city streetlights and how they're functioning, they would have to pull manual reports.
00:23:30.484 --> 00:23:38.625
They'd have to probably interact and interface with so many different people and departments, whereas now they can say, hey, what's the status of our streetlights in South Tampa?
00:23:38.625 --> 00:23:47.630
They can ask those natural language questions and get those answers because your tool sits on top of all of the different data points that they have locally.
00:23:47.630 --> 00:23:49.238
I really appreciate that.
00:23:49.578 --> 00:24:02.960
I want to ask you about your expansion plans, because the one thing that I see just going through your website and all the things that you've built you've obviously you're amassing an impressive team that you already talked about has that passion, but what I know is that you guys don't just have a passion for technology.
00:24:02.960 --> 00:24:06.986
You have a passion for making an impact through that technology.
00:24:06.986 --> 00:24:16.663
So with that in mind, leland, what's that expansion plan look like you talked about client education as a core part of what it is that you do when you talk to local governments across the country.
00:24:16.663 --> 00:24:19.323
What do those expansion plans look like?
00:24:19.323 --> 00:24:28.547
Are you in that phase of we just want to talk to as many governments as possible and just educate them, or do you have a more sales and marketing oriented mindset at this point?
00:24:28.547 --> 00:24:35.564
I'd love to hear where you are and where you want to be with regards to expansion yes, that's a great question.
00:24:35.584 --> 00:24:41.479
Um, so, because our tools are new to governments and governments are actually um slow to adopt.
00:24:41.519 --> 00:24:49.164
We are at that like, I think, sales um stage where we're trying to push out and then tell people that, okay, this technology actually exists.
00:24:49.736 --> 00:25:19.042
I tell these governments it exists, and then at the same time, we're finding as many partners as possible, because in every single country they have different compliances, they have different things that you have to navigate, and so we actually rely on local partners that they have the system and the hardware devices in place, and then we provide, like this, software along with as a package, and so we're trying to enter into as many markets but then also as many we call it like verticals as possible.
00:25:19.576 --> 00:25:34.154
So right now we've touched things like smart building, smart bridges, smart street lighting, and so we're actually trying to go into more and more systems as possible and trying to accumulate more use cases.
00:25:34.154 --> 00:25:44.522
So then, on a data side and on an experience side, we actually know the pain points of all these different systems and also know how to build around them.
00:25:44.522 --> 00:25:59.326
But yes, I think, going back to the question, it's really just trying to get the word out there that we actually have the technology and then trying to show governments that this is actually worth your money and that we can provide huge value.
00:26:00.015 --> 00:26:03.445
Yeah, I really appreciate those insights, leland, and the way that you're going about it.
00:26:03.445 --> 00:26:16.777
It just seems to me in all the research that I did ahead of you and I getting together today here on the air is that you guys are going about things the right way and I so appreciate that, and obviously it's a sensitive industry that you're in when we talk about going about things the right way.
00:26:16.777 --> 00:26:25.721
There are so many concerns about smart cities and data and AI, so I want to ask you, even on that more macro level, from your vantage point, what is the future of AI?
00:26:25.721 --> 00:26:35.599
So here we are in a new year, at the beginning of 2025, and everyone wants to talk about ai in all the ways, and your business is also growing alongside, step by step, with ai.
00:26:35.599 --> 00:26:39.929
What do you see is the direction of ai this year and beyond?
00:26:43.380 --> 00:26:44.060
that's that.
00:26:44.060 --> 00:26:46.463
That's a hard question, um, I think first.
00:26:46.463 --> 00:26:50.128
First of all, I think last year there was a big hype about AGI.
00:26:50.128 --> 00:27:07.020
I think there's this idea of AGI Reaching Human Level Intelligence, but I think AI, just as a whole, what it's doing is it's moving everyone up markets in what they do like upstream, so that you don't have to worry about the small details, the sort of more manual tasks.
00:27:07.020 --> 00:27:13.107
You can worry about the big picture, but you still have to tell AI what you want to do, happen.
