How to make the best use of each dollar to cut water losses as a small community?

with 🎙️ Elango Thevar – Founder & CEO @ NEER.ai

💧 NEER.ai is a Start-Up aiming to redefine Water Management through Artificial Intelligence

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What we covered:

🍏 Why to leave a cushy job and its associated good money in the middle of a pandemic

🍏 How reading one book became a decisive trigger to leverage a 15-year corporate experience and make an impact that lasts

🍏 Why water utilities might be betting on the wrong horse today when they’re planning their Infrastructure works – especially the smaller ones

🍏 How NEER’s technology enables to make the most out of every dollar invested in network upgrades

🍏 How this allows to cut non-revenue water, improve overall sustainability and build for tomorrow

🔥 … and of course, we concluded with the 𝙧𝙖𝙥𝙞𝙙 𝙛𝙞𝙧𝙚 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣𝙨 🔥 

➡️ Get the 4 Page Synthesis for free!

Is it reasonable to attempt to change the World in the middle of a Pandemic? Is it at this cost, that you can make an impact?

How do smaller communities invest their money on infrastructure projects today? Is their room for improvement?

How do you ensure that today's effort to cut non-revenue water and improve the water network actually builds for tomorrow?

Elango Thevar Infographic Page 1

Resources:

➡️ Send your warm regards to Elango on LinkedIn

➡️ Visit NEER’s website

➡️ Have a look at Gener8tor

➡️ Check the Elemental Excellerator website

(don't) Waste Water Logo

is on Linkedin ➡️


Full Transcript:

These are computer generated, so expect some typos 🙂

Antoine Walter:
Hi, Elango. Welcome to the show. I’m really glad you joined me today. I really like to start with a postcard and I think you are in a beautiful place, Kansas city. How’s the weather today? How’s the city today.

Elango Thevar:
Hi Antoine. Thank you so much for bringing me on the show. Yes. I’ve been Kansas city, Missouri being here for the last 15 years. So the weather is beautiful here. It’s nice and sunny, you know, so now it,

Antoine Walter:
What brought you to Kansas city? Actually, you said you were there for 15 years, but how did you end up being in Kansas city? And does that have something to do with your,

Elango Thevar:
Yeah, absolutely. So I grew up in a very small town in India less than a thousand people and very, very tip of the South India. And then you can actually see if you go to the ocean, you can probably see the Sri Lanka from there. So I grew up there small town until I was 15. And then I moved to another city called Chennai, probably heard about a few you’re in the water nerd. You know, the city is experiencing some water stress there. So I spent my bachelor’s degree in Shanai four to six years there before I moved to the United States for higher studies. So I came here for higher studies to do my master’s degree in environmental engineering at Oklahoma state university, which is in Stillwater, Oklahoma. That’s where I came to the United States in 2002. And I was there for three years. Got me masters done.

And then I worked for a very small laboratory, you know environmental lab slash kind of engineering company. You know, they have a small each negative division, like a two person team. So I went in, you know, I’ve talked to the owner and like, Hey, I’m just, you know, I want to work for you. You know, you just want to grab the job that whatever you can get right after graduation. So I worked there for like eight months, maybe nine, almost. It’s got like a close to a year and I was, you know, go to different water wastewater treatment plants as my job is to grab samples and then bring back to the lab and analyze it. You know, they’d be already Cod. And, you know, the chlorine residue was on the distribution systems. You know, the disinfection byproducts. That was the time that EPA was forcing a lot of regulations, you know, 2005.

So bring those samples to the lab and analyze it and then, you know, help the communities that the water wastewater treatment plants to send the report to EPA, you know, the state EPA pretty much. That’s what I started my career in water. And in Kansas city, you know, my ex girlfriend was actually, you know, pretty close to here. So I wanted to be closer to her. That’s how I ended up in Kansas city. And I got a really great job here, a big consulting firm they’re based in Boston, where they have a great office here in Kansas city. So that’s how I transitioned from kind of a small, kind of a EGA technician to a, you know, full blown engineer in 2005 September. You know, that’s how I moved to Kansas city. I fell in love with the place and I’ve been here for the last 15 years. My wife, she grew up here. So I ended up settling down in Kansas city. So,

Antoine Walter:
So you were like 15 years in the corporate world. And then all of a sudden you move out from that corporate world and you create your own company near, how does that happen?

Elango Thevar:
Yeah. You know, when I transitioned from that, you know, small Indian technician to a water resource engineer, that was my title. I really loved that the digital modeling, you know, and then helping a lot of communities and I was not kind of like, you know, what’s next, it’s always kind of like, okay, you know, are you happy or you want to do something different? So I went back to did part-time MBA around 2014 to 2016 while I was working full time. And here in New York, Casey, this is university of Missouri, Kansas city blocks, skate school MBA, you know, did MBA in entrepreneurship and and financing. That was my emphasis on, I had no idea how this is going to help with, you know, my job, you know role. But I really enjoyed the process of going through the MBA program, giving me the business perspective about, you know, why we do things, you know, what we do from our organization level.

And then I was still, you know, continued to work full time as a, you know, a civil engineer, you know, slash I said management expert on the, in the water sewers somewhat industry. It was great. And then starting of this year, I think I would say further March, I decided to leave my full-time job and, you know, start my own company. So I started near April, but I would say the triggering point in my life is I read this book called troubled water by Seth seagull, which is what’s wrong with what we drink. It focuses on American water system. I don’t know if you know, Siegel. He wrote a book called that there’ll be water before. He’s a great author. And in that book, he talks about the challenges faced by the American water system, in terms of the water main break and the water quality issues, the PFS and all those stuff.

I, you know, one of those pages I was listening on the podcast. I didn’t get the book hard copy at that time. And then he was talking about like, you know, how many water utilities in the United States, and then that I’m, you know, I’ve been in the water industry for 15 years and I had no idea, no idea there was 52,000 drinking water to it. It’s just the drinking water utilities and then add the sewer water system and stormwater systems. So we’re getting very close to like a 90,000 water sewer and stormwater system. So I bought the hard-copy and then I looked the page number, you know, where that breakdown, you know, how many sizes, different utilities as like, okay, you know what I’ve done with being in a full-time engineer. And I want to go and do something, build something and help smarter medium-size communities.

That was the triggering point. So I left, I started here in April, started building the platform, especially triggered, I mean, to those communities that have been struggling to meet the modern tools and all those meet the regulations and fix their water main break, asset management, all that stuff. So I needed to create something for those people, especially too, because it’s the, it’s almost embarrassing to be an, a engineer and not to create something to help these communities. So that’s what I felt that, you know, deeply emotionally. And that’s why I left and started this journey.


Antoine Walter:
So you read the book, you get the epiphany. And when people would just spend years thinking about what they could do, you actually leave your job, you start a company and not only do you start a company, but you’re already in your second accelerator program. So it sounds like you must have, whether it touched something and touched a nerve and really found something which is a big pain, or maybe you have a secret source for an extra talent, or maybe a combination of those. So what’s your secret sauce?

Elango Thevar:
No, there’s nothing secret sauce, I think. Or the, it was probably the right timing. I would say, you know, I mean, I left like doing pandemic. I was supposed to leave in March, but I left in like a first week of April, but in generator, I mean, that happened right away. Like in may, you know, be because of my MBA definitely helped me. You know, what is the best way to actually build a company is where you can go raise money, especially in the Midwest in Kansas city, you cannot raise money because you’ve been like a very high growth companies. Investors are very, very conservative risk averse, right? Because my MBA definitely helped me understand that kind of scene. You know, how the high tech companies like Uber, Airbnb and that got built through ECS money and everything. So if you really want to build kind of a massive change, you definitely need some help.

The best way to start is to apply to some of those early stage accelerator program. They can actually help you get to the next level. You know, I mean, you know, I, I, I left a really cushy job, you know, I was making good money, but this is the time that I really wanted to take risk and see, you know, what, I can do it, you know, build, you can always go back and be an engineer, but this is the time that you want to experiment and fail fast. Right? So definitely the MBA background helped me and we just, I didn’t waste any beat right after I left. And we just started building like crazy, you know, nights and weekends and apply to all this Techstars and all this programs and a generator happened. And they, they extremely, you know, great program. I would say they are one of the, you know, top right neck to neck with textile program and great people there. And they supported us first. And we went through 2012 weeks, very short program. They just throw a lot of stuff at you and you’re building the product and you were meeting the investors and, you know, you’re also like try to talk to a lot of communities, you know, because we didn’t have a full blown product at that time we have a clickable prototype, we just went to the cities and get some data. So we can actually Cain the models and stuff like that. But yeah.

Antoine Walter:
Can we just deconstruct that you say you had a prototype, but you realize that there are thousands of utilities and then you have a prototype, but in between you have been finding a pain, something that you wanted to solve. So what is it that you wanted to address at that moment? Yeah, it was

Elango Thevar:
Since I’ve been in the industry for awhile. So I know that the biggest pain point that I wanted to solve was a strategic long-term investment at the same time, understanding their operational challenges so that they can actually see what’s happening in their system day to day. And then also strategically they can invest long-term because you have all this problems that are happening in the network and the dream let’s speak some examples, like you’re drinking water, right? So you have all this water main break, like, you know, there’s crazy statistics out there in the United States and in Canada. And then you have a lot of leaks, you know, like 20 to 30% of the drinking water that gets lost. They have no idea where they are actually leaking. And then on top of it, you have this huge water main break on the water system. So if I’m a smaller utility, I have to, the leaks is okay, I’m losing money, but that’s not their top priority.

They just wanted to make sure that they don’t want to be on the news. You know, they don’t want to be colored by, you know, the local TV that there is a water main break and a hundred people out of water and somebody got into, right. So they’re dealing with a lot of these challenges day-to-day and then on top of it, they have to invest into the future, you know, and then w which pipe I should replace. They don’t have any framework. They don’t have any kind of asset management framework to how do I replace pipe in a organized way? So that can benefit most from a dollar perspective, they are investing in the right locations and they’re also getting the most benefit out of it. So that was kind of the first core product that I wanted to build is kind of a predictive asset management, get the data from the community and extract intelligence outfit and provide that framework to them.

Hey, you know, you should be investing in these locations first before you, when you start dealing with this stuff. And then once they keep continuing to collect the data, how their system, you know, how often they have a water main break, where they address the leaks, and we can be able to extract intelligence, you know, the pipe materials, the groundwater soil type, and all those open source data we can able to pinpoint and give them at least kind of a 90% confidence that we’ll hit. Okay. This is how you should be actually investing long-term and also addressing your short term bottlenecks in this way in optimized way. That was the core product we wanted to do.

Antoine Walter:
So that means that today it’s like, they hope that they fix the right issues, but they don’t really have a clue of what they are fixing or, or in which order they shall be revamping the networks. I can give you an example in France, there is an investment program, which says that you should replace 0.5% of your network every year, which means you are betting that your network is going to last 200 years. And that’s just a bet. And it’s based on statistics, always that’s in the U S and on top of that, is there a difference between a large utility and the smallest size utility? Would they address it the same way, or is it like you replace first, your main lines? And hopefully if you still have some money or time left, you go to other branches.

Elango Thevar:
I think the problems are the same. It doesn’t matter how big you are and how small you are in the Aww you know, American waterworks association has a 0.5%. It’s very, very small compared to what they’re doing with some other utilities. They, they want to achieve 1% here in Kansas city, Missouri. They, I think they are doing 1%. You have to have a less than 15 water, main break, every hundred miles of pipe here. They have some kind of a KPI, like a key performance indicators to go by. So if I’m a small utility, let’s say like 50,000 or less, they’re struggling to even 2.5% because their rate structure is, you know, skilled and they have to do more with the less amount of money they have. So even with the 0.5%, you have to come up with some kind of framework, okay. Which 0.5% of the pipe size should be replaced, whether it’s age-based calculation.

Okay. Despite has been underground for like 95 years. It’s a designed for a hundred years. Maybe we should replace this pipe in the next five years, or should be build a better model to say, we can predict, okay, that 95 year pipe could last maybe 120 years. You don’t need to touch that pipe for the next 25 years, maybe go and replace some other pipe, which installed in a bad soil condition, go and fix that. And maybe puts in, started between like a certain timeframe, like 1940 to 1950, the pipe manufacturers who are manufacturing to the different standards at that time. Maybe you should be placed that by first. So giving them kind of a framework kind of intelligence intelligence way that they can use that intelligence to go after the most riskiest pipe, even selecting that 0.5% to optimize it in smarter utilities, you will be surprised.

I mean, I don’t know how in France they do it. What kind of models they use, especially on the drinking water. I said, I think the predictive analytics started from France, you know, because if I remember right, you know, they are the one actually built the AOE or you, the algorithm there to collecting all this data sets, they actually, you can optimize your investment. A lot of companies like, you know, modern, you know, factor and what are they kind of use borrow that technology in? We also relay on that little bit, so the smaller communities is challenged because 1% he has all this institutional knowledge and he tells the water director, okay, we should be replacing this by. We should be replacing that by, based on what he observed on the field. So we need to capture all those institutional knowledge somehow. Otherwise it will be lost when he retires. So those kinds of stuff that, you know, when we go there and we actually educate the water directors and sort of directors, that we can actually do this in a better way, and we’ll save tremendous amount of money and resources for the communities and alarms.

Antoine Walter:
So what is this better way? What is this better model that you’ve been scratching a bit, I guess I hope that’s near, but how does that work?

Elango Thevar:
Yeah, so we go to the community and then we tell them, Hey, there is a better way to do this. Let’s use some of the modern tools like machine learning, the technologies there let’s collect as much as data as possible. You know, what are the data you have? You know, you have 50% data, 60% data, that’s fine. Let’s get all the data, your physical information, your location. You know, you’re an extent why coordinates your, you know, fire hydrants and, you know, your, your all the nodes, water meters, you know, pipe installation date. If you have ’em and take us of the five weekend, you know, so we get all this information, what are the information they have? And then we able to collect the data and then be normalized. The data, meaning that no utility has a hundred percent of the data, you know, so they may have 90% somebody to these may have like a 40% of the data, but we able to make a best engineering, judgment, you know, populating those missing information.

We get to that full data set. And then based on their experience on the field in the last five years, even if they have a paper maps, we can able to capture those information into the Neo platform. And we train the models you know, meaning we different different algorithm to see, can we use this information to validate these failures, using the model, and then we can extrapolate what’s going to happen the rest of the system instead of you writing your program to tell, okay, soil is affecting 20% groundwater is affecting like 10%. And if the pipe is like near hospital, I give you 20%. You are, instead of explicitly, you’re programming all this stuff. Why don’t we just give the, all this variables to the computer? Let’s the computer make the best decision for us. And then we can continue to validate that on the field and continue to get better over time so that, and then the communities can use that intelligence. So that’s kind of what we do.

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Antoine Walter:
I think about the data. What is the typical that’s attitude you see is always missing? Is there something that you see kind of a trend consistently that data is missing?

Elango Thevar:
It’s so you know, that’s a good question, but we don’t want to have a tremendous amount of clients yet, but based on few customers. So we have, we have a whole huge spectrum. You know, we have seen to these, they don’t have any GIS data. We have to convert them into GIS. And we have this other system, they have pretty much everything that we wanted, but it’s not standardized meaning that if it’s a PVC pipe and then somebody will enter, you know, Paul even chloride, you know, and somebody will just set like, you know, PVC and in the small caps and large caps. So there is no consistency among the pipe material. So there is no standardization in the industry, which is kind of sad. Typically the missing information, even some, you know, in word elevation will be missing. A lot of invert, elevation will be missing.

Pipe material would be missing. And the thickness of the pipe, which is critical, you know, that will be missing. And even the installation date, especially the, our customer targets, they are missing a lot of installation date. And that’s kind of the, the core, you know, they have X and Y where they are, but there’s lot of way of us. That’s been missing kind of a even basic elevation, you know, the top of the ground elevation. So there is no consistency, but I think some of the larger systems, they do have some kind of a, you know, I wouldn’t say they standardized everything, but at least they are, they are very close to be there to, they can actually use the data with the very minimal data manipulation. You can actually, we can start training the dataset, but other data, we need a lot of cleaning just to make sure to get to that standardization of the data.

Antoine Walter:
And what is the shape of this data? Is it fully digital data, or do you still have physical data? I’m asking you because in Europe you would typically, if you go to a utility and you ask where’s your data, I mean, most of them, they would just bring you to a room full of binders and say, Hey, here, there’s everything we did for the last 200 years, but there’s no chance to find. And even if that data is accurate, there’s no chance to find the data you’re looking for.

Elango Thevar:
I would say most of the data that we get is all GIS, geographic information system. They all digitized, you know, at least 90 to 95% of the data. I mean, some States in the United States, the state EPA with the rural water association, they funded this digitization of, you know, GIS converting the paper maps to the GIS in the last, you know, 10, 15 years. So, which is certain States are more advanced in the United States compared to other States and some utilities, especially smaller utilities. They do have some paper maps, but I think converting from the paper maps to the digital GIS information, it’s, it’s a huge value for them, right? You don’t need to do any mission learning. You know, they just let’s come work that the pipe to the, you know, the, the GIS and show them, you know, digitally, you know, where they are, how they can interact with this feature. So it’s a huge source of would be to have the very minimum number of those clients, but not a whole lot. I think, you know, 90 and 85% of the systems already digitized, you know, especially with the, with the RTA has growth in the last 15 years, you know, Q GIS and all of the free tools available. So they able to convert those, you know, I mean, there’s a lot of data missing on the beach days to data set. So which is okay, we can, we can populate that using some machine learning tools.

Antoine Walter:
So you consolidate all of, all of that data into your, your tool and with machine learning, you can fill the gaps if there are some, but that is still static data. What about dynamic data? Do you also integrate this aspect of things like measurements on the network leveraging, I don’t know, IOT to use some, some big buzz words, but how do you bring this, this into motion?

Elango Thevar:
We, we start with the, you know, the static data first in the utility. So we targeted, they don’t have a tremendous IOT sensors data yet. So the real time kind of a modeling and, you know, understanding they have maybe water meters, but that they are not collecting every 15 minutes. You know, they collect drive with, they still use drive by to collect once month data set on the drinking water may have maybe one or two pressure sensors, you know, on the DMA, the, the district in the monitoring area or the zones, the pressure zones. So they don’t have a lot of IOT sensors yet. So from that perspective, the dynamic data, once I think we have to start somewhere, so we give them, we predict all the risk in the system. First, that’s kind of the long-term strategic. And then on the path, the next is just to moving towards that dynamic data, you know, okay.

I have only one presser sensors in one location. Can we use this process sensor to identify, pinpoint exactly whether I have a leak in certain locations and the answer’s yes. You know, we can build a model to, if you have a pressor senses, you know, we can actually see if there is a sudden drop in pressure. One persistence is not going to help you, but if you have a two pressure sensors in the network, we can actually pinpoint the X and Y where the leak is actually happening. You know, so some of the utilities, they are not there yet. Again, you know, it’s a process, but I think we want to bring them, all those communities, you know, affordable technology tools to that finish line, that they have high level of disability that they can you know, use on not only the long-term strategic asset management perspective, but also understanding, okay, I know there is a leak is going to happen or a leak is happening today.

Maybe that’s like a, you know, maybe one gallons per minute or something like that. We pick and pinpoint that they can go ahead and fix it without installing lot of acoustic sensors and the network, and able to pinpoint that, and then able to continue to optimize this model. As we build intelligence out of it, it’s not going to be like a day one. They get, they know exactly where the leaks are, but I think it’s a process, right? Some utilities, they do have a lot of senses, so we can actually convert them right away, you know, using the existing data sets. They have to build that model right away based on the data. And then being able to pinpoint, you know, where the problem is going to occur before they can address it. And it will be, it will be a huge value for them. But I think from the smaller utilities, which is my passion, is, you know, we need to bring these communities to where they need to be in the next case, the next year or two,

Antoine Walter:
If the data they have is fully static, what you can do is still make a full modeling of the network and tremendously help them with the predictive maintenance. And as soon as you add this kind of dynamic data to the pot, then you can go into leak detection because that helps you to have a look on the network, but that’s still not the majority of the utilities which are in, into that dynamic level, but can you maybe give them some advice to say, okay, if you had to put a couple of sensors, according to the model, according to what you knew about the network, you would put them here and here and there with you would have the best lever to improve in the future. Is it your

Elango Thevar:
As well? Yeah, we can do that right now. I mean we can, you know, we have a simple algorithm to predict, you know, based on their DMA and see, because if you have, you know, pressure sensors are very affordable, you know, acoustic, then, you know, your flow sensors and, you know, other sensors, the goal is to use the pressure sensors to pinpoint the leaks. If the pressure is down in a two different locations and we can able to correlate exactly where they are so we can optimize there, you know, give them such something like you’re saying where they should be putting sense of sane. I mean, since this of the key thing, you know, I mean most of the software runs on hardware. So when we go to the client, we don’t tell them, Hey, you need these many sensors to follow a platform to work.

You know, we just get to work right away based on whatever the data they have. And then once we provide that long-term strategic risk profile of the entire system. And then we will say, okay, if you really want better, this is what you should be installing your census at affordable cost, and then be able to do this, be able to do this, you know, so, and then they get excited, you know, okay, if you can do with, what are the data we have with this. And people are like, if you census in here and then we gave him to do that, because since it’s a very, very affordable now, you know we’re talking about $20 a month, even if they put like 10 different census, it’s only like 200, $300 a month. So they get excited. You know, I think they, they’re looking for tools that actually empower these communities. And I think we believe that the, I can do that.


Antoine Walter:
And the machine learning aspect, you also mentioned it doesn’t happen from the first second that you can get the full set of data. So what are we talking here? It’s like, they start using near and, and the next day they get value. What is the aha moments? And that the war effect is it’s rather after one day after one week to one month longer, when it’s this moment where they really realize, Oh gosh, NIR helps me so much.

Elango Thevar:
Yeah. That’s a great question. It depends on where they are in the dataset. If say like if they have a digital data and they have like a 40 or 50% of the data is most of the data is there. And then we just need to fill out the next 50% of the data that the missing information. Then we can get the results within like eight weeks or within eight weeks provide that risk spectrum of the entire assets and say like, if they have an 80 to 90% of the data, everything is complete. And then there’s 20 10%. We can get like a, you know, five to six weeks and can turn around and give that expectation. And if they don’t have any GIS information, need to convert it into paper maps, and then it’s going to take at least six months for them to provide the first aha moment.

Like you’re talking about the risks spectrum of the entire network, and they are able to go to the mayor or the city council to see, Hey, this is the risk we are dealing with. And this is where we should be addressing our investing of Dallas here and here. So that will happen depending on the, out with the data tapping as quick as possible, like five weeks, or it could happen probably like three to six months based on, you know, where they are at. But I think we are working on a product. I don’t want to delve too deep into it, but I think our goal is to turn it around within 24 hours. It doesn’t matter how small or how big your utilities, as long as you have, you know, certain data like a 40 or 50% of the data. We can maybe make it, you know, within 24 hours. So that’s what we believe the industry can tremendously benefit.

Antoine Walter:
How can I imagine your service? If I’m the director of the public works, it’s small to midsize community. I start to working with near. First thing you do is you check my data. We sit together. If it’s this 10% or 50%, which are missing, I’m no skipping the part that I might not have a juice. I say, I have a juice. And then you start working with that. But do I have to understand that? Is it my, just that integrates into near? Or is it near that integrates into mergers? What is my touch point?

Elango Thevar:
We can integrate with any system they have, you know, if you have, if you use work order management software, you know, you’re documenting all your digital information, that’s kind of a little advanced communities like, and if you go to 50,000 to a hundred thousand or even higher, they definitely document everything digitally. They have a good art GIS, good. You know, city works, Lucy or cardiograph. They kind of platform there. We can integrate directly to API to bring that data directly into our platform. So they don’t need to manually here is my GIS data go and figure it out, but we can collect that dynamic like you’re talking about. So every time somebody did something in the database, they are documenting where there is a leak, and then we can pull that information right away. It can be sent with it. So that’s one approach. If they don’t have any digital work order management, they just have a GIS GIS they’re documenting it.

We can also connect in RPA as online to our platform or wherever they’re storing a database, simple saved files or anything like that. We can refresh that data. And we also, you know, talking with, if they don’t have any world or management, you know, which is which so I’m finding it really, really, you know, some of the utilities, why it is, they don’t even have a good work order management software, you know, so we’re talking with a few partners that we can actually, you know, help these communities to because we cannot do, you know, we cannot digitize the entire city’s, you know, operations, you know, our focus is water, right. But we definitely want to help them to partnerships so they can go from digital, everything. And then Neo kind of fits on top of it.

Antoine Walter:
So that’s the feeding loop. So that data feeds into near, near miss the modeling and give some advices. How do I get these advices back? Is it like on the same GIS I’m using? I see this part is in red, so I should act. And this part is in green. So I should put it out of my plan because that pipe is fine for the next 50 years. How is it?

Elango Thevar:
Yeah, it’s a, you know, we have a different data farmers, you know, they can download, which is GIS against shapefiles or the fire database, or even Excel or CSV, you know, this stuff, all these pipes and score. I mean, the, the best option is, you know, the shape files. So Gog song, file formats. They can actually see it in the GIS. You know, what are they using Q GIS or RTAs. They can map it out. And then, because what they really want to do is they can go ahead, talk to their operational team, you know, how they operate. You don’t need to do all this cleaning here, just clean here, clean the air, clean here, we can optimize your operations. And then they can go to the city council or the mayor, you know, we need big dollars to invest in this location because this is the riskiest asset that could fail. Then you have time. So the maps definitely the shape files, you know, our design format definitely helps convey that message to their operational team and also their financial team and their city manager and our, the higher up people that you know, who wanted to see them.

Antoine Walter:
And you market near as a software, as a service, right? Yes. Yes. Okay. So you mentioned that surely have some customers, which is amazing when you think that your company is not even one year old. But I think that’s part of the fast iteration experimentation process. How far are you right now with the spread outs of near how many customers are we talking?

Elango Thevar:
I don’t disclose how many customers we have today, but I think I was expecting that answer. I apologize for that. But I think you know, we are we’re still a young company, you know, we are still startup. I mean, I can tell you, we don’t have a lot of customers, you know, I mean, I don’t know what black means, but but I think each individual customer that we have, everybody’s excited about this tools and you know, we want it to grow organically. You know, you don’t want to push the growth too fast. We just wanted to, the most important thing for me as a CEO of the company is to help as many communities as possible, as fast as possible, as cheap as possible. And I think we are on the right track. You know, our platform is maturing. You know, you’re adding a lot of features or customers that are asking. So we still end in Vang cycle. We start still trying to figure out a product market fit. You know? So you said like we have only less than a year, but since, you know, I spent quite some time in this industry kind of helps accelerate that a little bit, but I think we are still in a small team. We wanted to grow organically and make sure we can provide value to our existing customers.

Antoine Walter:
I’m going to make an understatement. 2020 has been especially year. How does this special year play on near? I mean, if you are in the business of having a restaurant 2020 is the worst year ever, but I could imagine that being, working into helping this smaller community to go into digitization, that’s exactly what they experienced. They were probably missing in the month of lockdown or whatever kind of reaction there was to the pandemic. Did you see this kind of things influencing your, your early development?

Elango Thevar:
Yeah, I mean a pandemic, I think I would say it’s flat. It’s probably people realizing it’s the value of each day, say Shan and, you know, understanding their bottleneck in the system. You know, investment, definitely not like a restaurant industry. You mentioned it’s kind of flat because people definitely need water. They have prefers to toilet, you know, they have to address flooding. It’s kind of flat. Maybe there is a lot more talk that, okay, maybe this is the time that we should actually modernizing our water infrastructure, not only from the local level, but the state level and all the way up to the federal level, even predicting this pandemic using, you know, some sampling in the, in the wastewater. So I think we are getting lot of, you know, acceleration towards the modernization of the, you know, water. And when I say water, including water, which were in stormwater industry, definitely this is going to push the industry forward much faster rate in the next five years.

That’s kind of my opinion, but this year, Oh, this is a great idea for, I can’t tell, like, you know, this is a great idea for us. It’s just kind of flat because we still trying to figure it out or, you know, our product market fit, you know, what our client really, what is their pain point? You know? Like, are they really, you know, care about machine learning or are they just, you know, wanted to see or just give me, I don’t, I don’t care. You know? I mean, this is awesome, but I just need there’s one, one every once, every five years, I don’t want it like every month, you know? So it’s just still trying to figure out what is sort of product market fit. So I wouldn’t say it’s a great year, but so far, it’s, it’s been a very positive experience in the elemental. We haven’t even talked about elements of, but it’s a great help for us to understand we are in the first go to market track, which is what that means is understanding what your customer segment is understanding, you know, what their main pain point is. Are you building the right tools they need, you know, so it’s a very early on for us to even say, well, we have a decent year. I would say we are very excited about 2021

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17+ hours of tips, technical advice, business hints, entrepreneurial inspiration, and market insights condensed in a MASSIVE 94 PAGES INFOGRAPHIC

20 chapters featuring 19 experts, each one addressing a specific chunk of the water industry cake

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Book Cover: Don't Waste Water Podcast, Season 1 in a nutshell

Antoine Walter:
Strongly involved in your local community. I mean, at least in Missouri, I’ve seen that you’re, you’re making some moves there to provide near to small community around your, you your home base. What is your, your plan for rollout is it’s a first misery than maybe cancers or directly the full States. And the word is the limits. What is your first target?

Elango Thevar:
So we’re targeting a Midwest, you know, not only Missouri, but Missouri, Kansas, you know, Iowa, Nebraska, you know, that sort of core States that we are targeting. We want it to expand here for the rest of the United States. We organically getting clients they’re actually asking, you know, can you help us? And, you know, there’s other companies reaching out to us, there may maybe they can use our product to, you know, help their communities or their clientele. So again, we just wanted to make sure that we are ready for the growth, you know? So we just, you know, like I said, we are still early on trying to make sure that we build the right product and make sure we can handle all the traffic on the back end to support all those services. So we are very choosy and very picky, you know, just to make sure we are growing.

But I think next year we’ll be kind of a we’ll understand who really our clients are and how we want to grow. But as far as, you know, growth for overseas, we also getting a lot of inquiries from overseas, but we are not there yet. And my goal, I mean like a long-term goal is to hopefully every communities on this planet can use in a year to plan their water infrastructure planning long-term and short-term, and, and create this affordably know, that’s the key here. It’s our, that we achieve. I think we provide a tremendous value to what we provide, but I think the price has to be right to modernizing the digital water sector. That’s my opinion. So if you’re building a tool that just works for only like 200 cities, bigger cities in the, in the entire world, in my opinion, that’s not revolutionary. That’s not changed. We need to make sure the change happens at the bottom level. And my vision is to bring that change to every community, especially the, you know, the, the people that who, who need desperate help.

Antoine Walter:
It’s a very ambitious vision and very beautiful vision, I have to say, but, but very ambitious. What is your intended path to go there? I mean, is it like the startup scale-up unicorn path or rather the steady growth, which leads you step-by-step to that end goal?

Elango Thevar:
Yeah. I mean we just, like I said, you know, we just step by step, you know, it’s not like we want to fly right away. I think we just want to crawl before we walk. We wanted to make sure that we provide tremendous value to our clients, existing clients and the clients that we take in. I think once you figure that out, you can scale in any number of ways, right? The scaling is not the problem. I think you just want to make sure that all the product that you wanted to build or integrate all the features that will help these smaller communities and empower them. And once you figure that out next year at the scaling is you can scale through partnership. You can scale through other software companies. You don’t need to put a lot of boots on the ground, you know, even overseas now, nowadays, especially the water, you know, transformation in the industry.

We are getting tremendous traction you know, kudos to all those programs. Imagine it’s two O elemental water council in Milwaukee. And you know, even Europe, I see tremendous opportunities there from UK to all the way to France as well. Imagine this towards also in Asia. So the industry is going really fast transformation, I believe so. I think we want it to be the technology provider, especially, you know, we want it to be the artificial intelligence platform for water, sewer, and storm water industry. It doesn’t matter where you are. We can reach that point in five years. That’s kind of my vision. We’re going to figure this out. And, you know, we have a great team. We have a, you know, great support from elemental, from generators. So I’m very excited about this opportunity and transformation we can make, you know, the technology’s there, the vision is there and we just need to bring this utilities to the finish line.

Antoine Walter:
Well, I think that makes for a perfect scenario for that part. And I just checked my calendar and I’m free in five years from now. So where we can make that a, the followup interview is just to check a few. If you reached that goal and along with his phone with you, I propose you to switch to the rapid fire questions.

Rapid Fire Questions:

Antoine Walter:
The rule is pretty simple. I try to keep the question short and you have to try to keep the answers short. I’m not cutting the microphone if you want to be a bit longer, but that’s the spirit. And usually I’m the one which is making it much longer than it should. So my first question is what is the most exciting project you’ve been working on and why

Elango Thevar:
All the projects I’m working on? Exciting, man, it’s like asking like which child is your favorite. So I’m very excited about all the projects. Each client is different, but I love them all.

Antoine Walter:
Fair, fair. I I’ll take that one. What’s your favorite part of your current job

Elango Thevar:
Is the finish line, right? When you provide those risk score to the client and then they come back and tell you, Hey, we’ve been missing this for a long time. You know, this is actually mayor and councilmen comes and says, okay, this is, this is amazing. You know, this is what we really need. I look for that, that moment. So that’s kind of excites me every day. So what is the

Antoine Walter:
Last time that that happened to you?

Elango Thevar:
I would say that happened to me probably two months ago.

Antoine Walter:
Nice. what is the trend to watch out for in the water industry?

Elango Thevar:
Elemental, you know, imagine instill and paying attention to the conferences, you know, Aww EA and water. This is very, very small. So you can’t miss a beat. You know, if you plugged into one entity, then you kinda like, no, I’m also just pay attention to all those water, you know, startup programs out there, and also just pay attention to the conferences and pay attention to some big players like in SBS and Cylon, and, you know those guys, what they do it will be covered. So

Antoine Walter:
Let me take you to a sidetrack. I told you I’m always the one making it longer than should, you know, there’s the big players and there’s the startups. And I’m not sure if that has always been the case or if that’s pretty recent. What is your take? There are the startups in the water world, a new thing, or have they always been around?

Elango Thevar:
I think it’s always been around, we are getting so much traction now because you know, people talking about social impact investment, you know, I mean, it is still not compared to other industries like power industry or anything like that. I think we’ve seen how, you know, make our life much easier. Like, you know, the Airbnb, Uber and all those, you know, high code Google and everything that happened the last, you know, first, 10 years, 15 years now, you sit back and realize, Holy cow, you know, we’ve done all this stuff. You know, we have a Slack, you know, we have, you know, HubSpot for marketing, you know, all this, this is great tools, but at the end of the day, we have to invest in, you know, hard infrastructure that makes these, you know, at the end of the day, we all live in communities and we need water to survive.

So I think people are realizing if we are getting to the point that what is important for, you know, for our lives. And that’s the time that, you know, we live in today. So we are going to see a lot of investment in water sector and especially in the social impact arena. So there’s always been the you know, water startups and the bigger players, but now we are getting to the point the technology’s there, the money is there. And then also the communication, the platform, like, you know the Twitter and Facebook and, you know, all those tools, making it, you know making it I think it’s pretty big. I think that’s kind of my opinion, but I don’t know.

Antoine Walter:
It’s a good time to be around, I guess.

Elango Thevar:
Yeah. Yeah.

Antoine Walter:
What is the thing you care about the most when you’re working on a new project and what is the one you care the least

Elango Thevar:
I care about most of the client motivation their vision for the city or for their utilities, you know, short-term, and long-term that, that’s the most thing I care about and the least we care about is the data. It doesn’t matter where they are with the data. We can get them where, you know, to the finish line, it’s a client motivation, you know, the client has to be excited, you know, in my opinion, to see what we see as the industry vision. And if they are excited, even if they don’t have any money, you know, I work with them, you know, so great.

Antoine Walter:
Well, you, you already mentioned a couple, so I’m going to ask you for all the ones. So my question is, do you have sources to recommend, to keep up with the latest water and wastewater trends?

Elango Thevar:
Yeah. There’s, you know, a lot of research there at Bluefield research, you know, water online news. And like I said, it’s very, very small, local research is a great thing. They have a tremendous report, you know, where the United States market is. I mean, especially I pay a lot of attention to the United States market and internationally also, you know, the water resources Institute and, you know, there is a, there’s a lot of, you know, online that, I mean, it just needs to type in something and YouTube and Google, you’re going to see, you’re going to see everything, whatever you see. I think there’s a lot of podcasts like yourself, you know, you’re doing amazing job and I’ve looked into all your previous podcasts. Now there’s a lot of water. Podcasts is also coming out. So, which is nice. People are talking about it. And you know, you bring in all this people missionaries in the industry that, you know, what they can automate and how they can improve the process that we’ve been doing for the last, you know, 50, 60 years in the water industry. So everything is pretty easy nowadays to get what’s coming up next. You know, what is the next big thing in the water? So

Get Season 1's Summary!

17+ hours of tips, technical advice, business hints, entrepreneurial inspiration, and market insights condensed in a MASSIVE 94 PAGES INFOGRAPHIC

20 chapters featuring 19 experts, each one addressing a specific chunk of the water industry cake

An evergreen source of Water Expertise at your fingertips to support you through 2021 and the years to come

Book Cover: Don't Waste Water Podcast, Season 1 in a nutshell

Antoine Walter:
That was people. Would you have someone to recommend me to invite to that same microphone just after you?

Elango Thevar:
You know, I started this company because I said seagull, so he’s I think he’s approachable. He’s not an a, he’s an author of let there be water and in a trouble water. So I think you should invite, invite him. You know, that’s my recommendation. I don’t know him personally, so I can’t, you know, make the warm intro for you. But I think he’s pretty, pretty approachable based on, you know if you ask him, you know, send him a message on Twitter, I think he will be more than happy to do it.

Antoine Walter:
It’s a tray. And let’s see. I mean, if he was able to inspire you to,

Elango Thevar:
Well, yeah. If, if he is, if he’s not available, talk to Kim Baker, elemental, the director of water innovation. She’s great. I don’t know if you already interviewed her or not, but she’s yeah,

Antoine Walter:
I didn’t, but I saw a couple of interviews of her. So

Elango Thevar:
Yeah, she’s a SISA champion of waters. He’s doing amazing in elemental CC 800 obligation a year. So, you know, if you really want to toggle for a chance, you know, you just pick up the phone and call her, Hey, what you, what you’ve been seeing. And she’s, she’s amazing. I think you should in my boat, you know, and see which one I’m willing to give a time for, for you.

Antoine Walter:
Thanks. Thanks for that. For the advice, actually, Elongo, you’ve been an awesome guest. Where can people find you online?

Elango Thevar:
Linkedin is kind of a, you know, very active in LinkedIn, you know, Elongo paper or, you know, or come to near.ai website. You know, you can find all the social media links and, you know, I’m there in Twitter and LinkedIn, or you can also email me, elango@near.ai.

Antoine Walter:
I put all of that in the episode notes. So everybody can can have though that those contexts, I mean, Elanco thanks a lot for your time. That was really, really interesting to me discussing with you. I hope I didn’t bother you too much with my French accent and French questions, but it was a pleasure.

Elango Thevar:
No, no worries. My, my son learned French. I’m not French. My wife is not French, but my son goes to a French school and he speaks French and I believe it or not. So it didn’t bother me at all. So love, love the language I’ve been there a couple of times in France. So love the country. Great European architecture. So not at all, but thanks for inviting me. Appreciate it. And one last thing is, you know, you didn’t ask the question, but near his water in my language, it says it’s a pebble, you know, the 80 million people speak in South Indian language, nearest just water. So that’s kind of my plug to my language. So yeah,

Antoine Walter:
Really great that you explained that because I wasn’t even thinking that would have a meaning and actually you you’re. Right. I should’ve asked. Nice, nice story behind the name. Cool. Right.

Elango Thevar:
Thank you. Thank you so much. I think you’re doing an amazing job and thanks for, you know, what you do. You need it for the industry to brainstorm and see what everybody is doing.

Antoine Walter:
Thank you so much for this time.

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