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

This episode is part of my series on Water Networks. Go check it out!

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?

Your best Email

Please enter a valid email address.

Get the Infographic!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.

Resources:

Send your warm regards to Elango on LinkedIn

Visit NEER’s website

Have a look at Gener8tor

Check the Elemental Excellerator website

is on Linkedin

Antoine Walter 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.

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

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.

Download my Latest Book - for Free!

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.

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

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

Download my Latest Book - for Free!

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

Download my Latest Book - for Free!

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.

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to make the best use of each dollar to cut water losses as a small community? appeared first on (don't) Waste Water.

Transcend is an incredibly innovative company that provides software to automate any vertical asset’s preliminary engineering.

This episode is part of my series on Water Digitization – check it out!

What we covered:

How scratching their own itches, Transcend’s engineers came up with a revolutionary take on early engineering stages

How AI, Process Modeling, Cloud, and many others combine to deliver overnight what previously took weeks

The virtual 5-step process that turns simple inputs into ready-to-ship documentation

How this builds for a win-win between all project stakeholders with more quality time for value-added tasks

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

Get the 3 Page Synthesis for free!

How do you turn an industry challenge that you're facing yourself into a stunning business opportunity? The secrets behind "scratching your own itches"

How to automate engineering in 5 steps (Data Validation, Process Engineering, Mechanical Engineering, Civil Engineering & Document Generation)

Behind the scenes of a rocket growth: how to get selected among 800 contestants to join a very special growth accelerator (with a stellar track record)

Your best Email

Please enter a valid email address.

Get the Infographic!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.

Resources:

Send your warm regards to Ari Raivetz on his LinkedIn page

Check Transcend’s Website

is on Linkedin

Antoine Walter on LinkedIn

Full Transcript:

These are computer generated, so expect some typos

Antoine Walter:

Hi, Ari, welcome to the show. I’m really glad to have you on the show today. I have to say I’m pretty excited on my end, by the field where your company is active, but before jumping into that, I’d like to get a bit more about you, your story. So let’s start with maybe the postcard you’re in Princeton right now. So I was going over there.

Ari Raivetz:

It’s good. It’s a bit rainy today. Typical weather for the East coast in October, but it’s usually very green here. So it’s a beautiful place

Antoine Walter:

Having a look at your path so far, I have to say you have quite a non-common path compared to the previous guest of the show because you’re coming from the finance world. So you have a finance background at some point you turn to water. So can you explain as how that’s happened?

Ari Raivetz:

Sure, sure. I started my career in the enterprise software industry, actually in the late nineties, I ran my own business for a year and then I worked in kind of ERP and supply chain software and kind of got my early start in business, trying to understand the way business processes work inside a company and how you can break those down and automate them to make a business more efficient, which is kind of what ERP and supply chain software or about. And then after four or five years in that industry, I went to get my MBA and after business school, I had to pay back my loans. So so I went to work on wall street for bank of America as an equity analyst. I started to get interested in water because I was covering the oil and gas companies when they were first fracking for natural gas and the Barnett shell in Texas and in the federal shell and in Arkansas.

And we took a lot of those companies public and did a lot of equity and debt offerings for them. So one is very big part of that process. And so I started to get interested in it later. I covered the oil refiners and the industrial manufacturers. You can look at my coverage universe at bank of America as pretty much anybody who is destroying the natural world. Who’s destroying the environment. That’s what I covered. So once I paid back those loans, I had to pay back my debt to the planet. So I left bank of America and I went to work for a clean tech private equity fund called RNK capital, which is really a pioneer in the clean tech investing space. They were one of the first investors in carbon credits and one of the first investors in solar projects and wind energy projects. And they had a fund that was focused exclusively on water. And so I was responsible for all the private investments and some of the public investments in that fund. And that was my kind of winding path to water where I’ve spent in the past 15 years.

Antoine Walter:

Would you say that these software automation aspect you had in the very, very early part of your career, then the business aspect and know the water, does that make for your secret sauce? Or is there anything different or supplement that I don’t know yet?

Ari Raivetz:

Yeah, it’s definitely the way I think about businesses is, you know, was framed by those early experiences in terms of, you know, how can you break it down into the different processes and automate as much as you can so that the people can shine because ultimately the people are what drives the success of the business. But if they get stuck in monotonous and repetitive and non-value added work, then they get bogged down and they can’t achieve their maximum potential. So that is definitely kind of been a defining factor for me. And it’s definitely led me on the path to transcend

Antoine Walter:

That path to transcend is a smooth transition to, to the deep dive. But I’d like to understand at which point you thought that there was such a pain that you had to solve with transcendent. I’ll let you explain in a minute what transit is really about, but I’m interested just before in understanding when you clicked, when you realized there is something there and you wants to solve that problem.

Ari Raivetz:

Sure. Yeah. I mean, actually the pain is that pain point is what transcend is about. So one of the investments that I did at RNK was a company in Budapest, Hungary called organic or water and organic sells unique wastewater technology, both services and products that are used to build these beautiful botanical garden wastewater plants. And after three years as an investor in the company, they had just commissioned an 80,000 cubic meter per day reference plant in Budapest. And the founders asked me if I would quit my job and pack up my family and moved to Budapest to run the company. They both had a technical background as engineer and architect, and they felt that, you know, they wanted a business person to help them scale the company. So when I moved to Hungary, the first thing I realized was we had this major bottleneck in our sales process where we had to do all of this preliminary engineering work before we could bid on a job.

And then clients would come back to us and want to change things. They would want to change various aspects of the design or the appearance of the plan. And we were a small startup, I think maybe like 15 people at that time. And, you know, we couldn’t afford to hire an army of engineers and it was taking us hundreds of hours every time we wanted to bid on a project or even evaluate whether it made sense for us to, to bid on a project. So we built a software tool inside of Organica and that software is the transcend design generator today. And that’s kind of how we got there.

Antoine Walter:

So you were scratching your own itches, which is somehow business one Oh one, the best way to get to the actual point and get an actual good business ID.

Ari Raivetz:

I mean, you know, you I’m sure, you know, engineers, and they were extremely skeptical that we could automate that first 20, 30% of the design of a wastewater facility. And it was not easy. It took us two years to develop the software about a year of software development and then a year of QA QC. But by the end of it, you know, all the skeptics began to love it because not only did it have great business impacts, which I can talk about in a minute, but it just made their lives better. You know, they didn’t have to work on all of this monotonous repetitive work in the early stages of a project generating the early drawings and the early design options and redoing work when clients wanted to make changes, but they could solve the really difficult problems. And that was what inspired them as engineers. And so people began to,

Antoine Walter:

I love it. We’ve come to that because I have to say, I’m going to set it already. So let me repeat it. I I’m excited, but I had to look at your website and on your website, there are a couple of quotes from your existing customers. One says we’ve been trying to do something like this for 20 years. And the next one says, this is the Holy grail of software for the water engineering words. So that sounds like incredibly bold statements. It sounds too good to be true. And then the next thing on your website is, wow, it sounds too good to be true. So I thought, yeah, that’s good marketing, and you have to test it by yourself. So I didn’t have the chance to test it by myself, but now I’d like to start really from the roots and to go step by step through your process. So let’s start with your pitch. What is it actually about what is this software as a service that you are

Ari Raivetz:

The transcend design generator automates the first 20, 30% of the engineering of a water or wastewater treatment facility, or really any kind of vertical asset at its core? That’s what the software does. And the idea is that when you start the design of a water wastewater treatment facility in the kind of manual world, you have a set of inputs that you usually get from a client. It’s how dirty the wastewater is, how clean you’d like it to be at the end, the temperature, the flow rate, and then a number of other kind of client specific preferences that are different for each project. And what usually happens is you hand that over to a process engineer and that process engineer evaluates different technologies and different biological or physical chemical processes to determine the best way to treat the water and does some process sizing and simulation. And from there, it goes to a mechanical engineer to an electrical engineer, civil and architectural engineer and engineer. And at the end you have this preliminary engineering package and our software does all of that in the cloud without any human intervention.

Download my Latest Book - for Free!

Antoine Walter:

So let’s go step by step through that process if I get it right, it starts with that. You input that in the system. And if I got it right, again, you give you a waste. What’s the data you’re intending outputs and some simple parameters. What do you do with that data? Do you have a way to check if what I input is slightly accurate or how do you do that?

Ari Raivetz:

It’s much more than slightly accurate. I mean, it is a garbage in garbage out process. So the data validation algorithms are one of the most important parts of the software. And we have two layers of data validation. The first layer is just simple kind of checks. Like for example, in biological wastewater, the level of ammonia cannot exceed the total nitrogen. So if somebody were to enter a higher influence pneumonia than a higher influent TKN, it would immediately give them an error message. Similarly, we have ratios of the DOD to Cod, to TKN, to TSS that we use in our validation. The second layer is an algorithmic validation that happens on the server side. And basically we just do a very quick steady state simulation to see, hf there are issues with the inputs that the user put there. And if there are we’ll spit back in our message and tell you what needs to be corrected.

Antoine Walter:

So assuming that I correct my data or that it was correct from the first stake, once you are sure that the data is right, it goes to process engineering, right? Yes.

Ari Raivetz:

So now we’re going to talk about different modules, but in many cases, these processes are happening simultaneously on the servers. But I think when we talk about it, it’s easier to talk about in a kind of more sequential way. So the first step is process engineering and there are algorithms which take the inputs, both the actual water characteristics, as well as the preferences that you have for this project. And they set up a model, they select all the different process units that are needed to treat that wastewater. And then they go through the mathematical calculations to size those units. And after that’s done, it goes to a third party simulator. There are various simulators that are used in the industry, and we’ll go through both steady state and dynamic simulation dynamic is 365 days with diurnal flow patterns. And at the end of that process, you will have the size of each tank in your treatment facility. You will have all the process units and all their characteristics, and you will have the air requirement, how much area you need, how much chemical you need, the kind of core process parameters that drive the operating costs.

Antoine Walter:

So just about this third-party simulator you were mentioning, are we talking of process modeling here? Like Cimbar GPS X or something different? Yeah,

Ari Raivetz:

Exactly. Our bio LAN or Sumo. Those are kind of four of the simulators that are used by the industry. So we can go use any third-party simulator as part of the design process.

Antoine Walter:

Okay. So once this process engineering is done, you can move to the mechanical engineering, I guess.

Ari Raivetz:

Yes. And just to reiterate, this is all happening on web servers without any human intervention, right? So the algorithms have selected the processes. They’ve sized them. They’ve started up the simulator, they’ve iterated in the simulator and they’ve taken the outputs now. And so now we have the outputs from that process engineering phase, just like when a process engineer, hands, their process design over to a mechanical engineer, that process happens on the server. So we have to translate that data from kind of process language to mechanical language. And then here we go through equipment sizing and selection. So we are based on the process that was designed, we are using algorithms to select and size every piece of mechanical equipment in the treatment facility.

Antoine Walter:

So that means that you have to rely on the database on the existing equipment, or how does that work? It’s like a library, which says there’s activated carbon filter or something else. How does that actually work?

Ari Raivetz:

That’s correct. So at our core transcend our software developers who are also engineers, water, engineers, energy engineers, and so on. And so over time, we’ve built up a comprehensive database of all of the different types and sizes of equipment that would go into a water treatment or a wastewater treatment facility. And it is a generic equipment that’s done in the professional enlight versions that are available on our website for a subscription or in an enterprise version. You know, we can use branded equipment based on what that particular client wants and if they have their own database of equipment that they like to use, and we can easily integrate that into the selection, the sizing algorithms there.

Antoine Walter:

So if I was Veolia, for instance, and I come to you and I say, by the way, here, it’s not a filter it’s called an active flu or whatever, what’d you just have to input the parameters and then I can use my proprietary equipment.

Ari Raivetz:

Yeah, that’s correct. Active flow is, I mean, you throw it out there as an example. I mean, that’s a proprietary technology of Veolia. I’m not sure if there’s a generic technology for high right clarification or not. If there was, we would have a generic technology in our database and then if the only was a client and they wanted to customize for their specific equipment, we would replace it with their active flow and we would use their sizing and selection rules to effectively size that act of flow. So,

Antoine Walter:

Okay. And for the generic one on, what do you build on, is it like, you know, there’s the, I’m assuming as boy, I grew up with Suez, so we had this blue Bible called the Duke lemon water handbook and everybody, every engineer would have that Bible, but that Bible actually tells you that the sand filter can be designed from these two days. So did you read through all these Bibles? I know there’s the Nalco Bible. There, there are several ones. Did you read through all of them to create your database? Or how did you proceed?

Ari Raivetz:

I don’t know if that is that as Bible or Devin Bible, a public document. Like, could you get that on the website? You can buy it. Yeah, exactly. So if it’s in the public domain, you know, in, in the U S many engineers use Metcalf and Eddy, which was kind of the original wastewater Bible and the still taught in many universities or whatever is out there in the public domain, that’s what we’re using. And if there’s multiple options, we either have a selection choice in the design process. So, you know, would you like to size based on X or Y or we make a choice based on our feeling of what the proper best practice is, and that’s kind of hardwired into the rules there.

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

Antoine Walter:

Okay. So my data was good. You could do some process engineering with it, some mechanical engineering with it, and now, you know exactly which treatment train I have to use to achieve my intended treatment objectives. So what’s the next step.

Ari Raivetz:

So we know a lot more than the treatment train at that point, right? We know every process unit, we know what size it needs to be, the volume it needs to be. We know the air requirement, we know the chemical requirement and we’ve now sized and selected every piece of equipment. So we know the dimensions of that equipment, the material that that equipment’s made out of, and with our algorithms, we also know where the equipment needs to be from a layout point of view in a facility. So we can build the bin model. We can actually build a three-dimensional building information model, think of it as a parametric data structure in the cloud, which represents all of the structures inside of that treatment facility. And this is the civil and architectural design phase of designing a wastewater plant. So we have various kind of modules within this.

You can actually think of it as like a four-part process in the first part, we’re virtually generating spaces or physical spaces to lay out the equipment and the process units that were identified in the prior steps. So now we have a data structure that represents those spaces. Next we’re arranging spaces. They have to be arranged in a certain sequence. They have to be arranged in a way where it’s most space efficient. And you can think about it like this in the very beginning, when Organica at the time of organic one, when the software was first developed, maybe the first designs, if you were designing a person, it would have been like they had their on their head, you know, so you have to build in a rules that say, okay, well, your needs to be in this general area. And that’s what, what these rules do.

And then, you know, over time we’re using evolutionary algorithms, we’re arranging them in a much more efficient way. And once you have all the spaces and they’re all arranged, you have to add elements to those spaces. So you have to add access rails, doors, windows, you know, if there are buildings, you have to have like the thickness of the walls and so on. And once you have that, you can essentially generate a virtual BIM model. And then that BIM model can be opened in any third party software like Revvit or Bentley or any of the other ones out there on the market.

Antoine Walter:

And what is the level of detail that you have in that beam model? Do you go up to, you mentioned that the windows and the doors, so I’m starting to think it’s quite detained, but what is the limit

Ari Raivetz:

And construction firms in the U S will say that we’re doing the first 30% of the design?

Antoine Walter:

Okay. So now to my feeling, it gets really incredible. If I get it right now, you push a button and you get every kind of document you would wish, right?

Ari Raivetz:

Actually you push the button in the beginning. So everything we just described, the process engineering, the mechanical engineering, the civil and architectural design. And by the way, within the mechanical phase, we’re also selecting the instruments. So we have an IO list. We’re selecting on the equipment. We’re looking at the electrical consumption and doing some very kind of early electrical design. So there’s some electrical and controls engineering that’s happening in that mechanical phase as well. But that’s all happened on the cloud when you hit the button the first time, typically, you know, it happens in a few hours, the simulation takes longer. It can take up to eight hours, but typically it’s happening on the servers. Again, just no human intervention, but now you just have a data structure me, say, you know, everything that’s going to be in the plan. You have a virtual BIM model, you have all the equipment data, you have all the process data.

So all you have to go, you know, you can put it together into documents. And so we have a document generator module and make, you know, up to, you know, 15 or more engineering documents. And most importantly, the documents are available in PDF form, but they’re also available in their native form. So you can open up your process or diagram or your P and ID in AutoCAD, and you can edit it in the future. When you add more detail in, you can open up your BIM model and rev it, and then you can start to add the level of detail, the final 70%, you know, that we don’t have in there. And you can open up all of your, your word and Excel documents and use them for cost estimation and add more details over time. And that’s it. When it’s done, it generates those documents that send you an email and you’ll come back a few hours after you hit submit, and you have all those documents there.

Antoine Walter:

I guess that what you just described that you have this, these native documents makes for what you call this head starts that you might have then in the detailed design, because you can already drew on what was existing. Right.

Ari Raivetz:

That’s exactly right. So that was one of the biggest pain points that we’ve heard from clients over time is that, you know, after they do this preliminary engineering, they always end up having to start from scratch. The process is not connected to the detailed engineering side. And so by using our software, you know, you naturally have this database of all the designs you’ve run for every project. You can imagine company like Veolia or Suez would have run, you know, 20 different designs for one project. And so you’d have all 20 listed. And then when you choose the final one that the client wants, you just open it up and you start from there on the details.

Antoine Walter:

That would, I mean, I can give you my testimony here is back in the days when it was working at, in Suez, it’s basically whatever was done in proposal phase and was absolutely good until the client signs. And the second that the client signed, you would throw everything away and you start from scratch again. Now with the real boys, you know, they’re the one that really know what they do

Ari Raivetz:

At the end of the day, the client doesn’t get what they wanted. A lot of times the client doesn’t get what they wanted. I mean, I think one of the reasons we exist as a company is because we want the engineering firms and the technology providers to deliver better outcomes for the utilities. And oftentimes they don’t. And this way, if you’re focused on what the client wants, instead of getting those drawings done so I can get my proposal out, it changes the whole dynamic of the industry, and hopefully helps innovation happen faster projects happen faster, and it gives everyone happier clients. So,

Download my Latest Book - for Free!

Antoine Walter:

So far, I think I’ve been a bit too transparent about my enthusiasm. I have to be now a bit the devil’s advocate, one of the limits of your system, are there some processes that you cannot cover some water conditions, which makes it impossible to compute? Did you already find the limit to your system?

Ari Raivetz:

Absolutely. So we always start every call with a potential enterprise client by explaining what we are and what we are not. And you look at this, it’s a software as a service tool. So we’re constantly adding new processing units to it. You know, as of today on October 23rd, it does Greenfield wastewater plants at some point in November, December. And you’re going to see us release a version for brownfield wastewater plants over time. We’re adding process units that allow you to handle industrial flows today. We’re just doing light industrial and municipal flows, but over time we add more and more processing units. So you can handle more and more types of wastewater. It is never going to be able to do the truly custom industrial wastewater jobs. So things like a textile plant where they’re changing the dyes every six hours or a fry site, where, you know, I originally got interested in the water space.

Those are really custom design jobs that have to be done, you know, one-off, and at least today the technology doesn’t exist to use automation for those. But we do believe that over time, as we add more and more process units, we’re going to cover in larger and larger percentage of the total designs that are able to be done for wastewater. We’re also adding a drinking water module next year. So we’re able to handle that as well and mean that just gives you a sense of what it does. I mean, the other aspect from a limitation point of view, isn’t about the kind of types of waste water can handle or the types of water treatment we can do, but it’s about the level of detail that’s in there. So we’re not going down to the nuts and bolts. We’re not doing the pipes in the isometrics. Those are things that are done in detailed engineering. This is about the preliminary engineering.

Antoine Walter:

So who’s your target customer. I would guess it’s the EPC, but could it also be the engineering consultant?

Ari Raivetz:

So when Organica started to use the software, it totally changed the business overnight, went from clients having to wait weeks and weeks to get one designed, to being able to show clients eight different options in a day. And it became a huge competitive advantage for organic go to be able to respond to clients faster, with more detail and more options. It was also a great advantage from the detailed engineering point of view because we had a headstart. So it took less hours to do detailed engineering, and that was better from a cost reduction or a margin point of view for the business. And then from an R and D point of view, every time organic developed a new technology, we were able to quickly integrate that into the design procedure. And we run old designs because you have sales cycles in this industry that are years long with the new improvement and give clients that benefit.

And so that is one kind of customer for us, or are the technology providers, the OEMs, anyone from a Veolia Suez down to a smaller company, like an organic, this is a tool that can make them more efficient, allow them to bid on more projects, do more detail, responding to clients faster, integrate on the innovations faster and reduced Intel engineering costs, a second kind of client and equally important are the engineering firms themselves. And that could be large engineering firms or smaller engineering that would use our professional and DG light tools that are available on our website. Anyone with a credit card can subscribe for those, and this is about making them more efficient so that they deliver better outcomes for their clients. It’s a tool to reduce their non-billable hours tool to allow them to evaluate more options within the same time period. And again, just give clients what they want. And we do also have some asset owners that are interested in using this. These are people that do have in-house engineering firms in house engineering staff, I should say. And they typically outsource a lot of engineering work, but they would like to do some of it. In-house. And so they’re looking at using the software for that as well.

Antoine Walter:

Can you give us a glimpse into your existing customer base or big is it today?

Ari Raivetz:

I don’t share publicly the names of our customers and transcend was starting in December of 2019. So two months after we started, I was spun out from Organica in December of 2019. So two months after we started, COVID hit. And, you know, I’ve been grounded here in Princeton ever since it’s probably the longest I’ve gone without traveling. So I’m essentially a COVID baby born during COVID. And, he’ve had to adapt everything from our sales processes to our hiring processes and everything in between, but we have had a very good first year in 2020, I’d say we’ve exceeded our expectations on the enterprise side. And then we’ve just launched our lighten end processional products, actually just the people weeks ago. So they’ve only become available. So, you know, a few weeks ago for people to register with a credit card and use them. So

Antoine Walter:

Above this aspect of being able to register with a credit card, I just had loop to a, to your various packages. And I really don’t want to sound too salesy, but you know, here in Europe, if you have to pay a good consultant who knows a bit about process design, you would pay him roughly 1,800 per day. That’s roughly the daily costs. And here I see that for, for the same amount. I get the full design of my plant. When of course that guy alone is not going to be able to do a food plan design in one day. So that sounds like a steal. That’s

Ari Raivetz:

Great. Tell your friends,

Antoine Walter:

You know, when, back in my days in, in Suez, sometimes you would approach a project and you know, that’s, you’re not necessarily the preferred vendor there because they used to be, you know, there’s Suez, there’s Veolia. Sometimes the customer has been working with Veolia several times in the past, and you approached him. He has a problem. You might be able to solve that problem, but you’re thinking, is it worth, is it worth it working with that project? Because I will need to take my proposal team to take my engineering team and they will have to work to find something. And a long before we get a chance to get the contract, we will be already spending thousands, tens of thousands. So there was a process in place at two at the time, which was called the go no-go. So at this stage, you meet the customer, you come back home and you explain your CEO, why you as a, as a sales guy, you would go. And,uhe tells you with all the best reasons in the world, why you should not go because the chances to win compared to the risk to give out all this money is it’s not worth it. It’s just not worth it. And then there’s you, and I’m starting to think, you know,uthat’s a revolution for the end user because they are going to get much more ideas from much more people around the table, assuming that everybody hits the bandwagon, but

Ari Raivetz:

That you had done now squarely on the head. So, I mean, that is, you know, our mission as a company is, you know, myself, our COO Steve Davis, Adam tank, our commercial director. We’ve, we’ve all been around this industry for 15 plus years. And the pace of innovation is just, it’s awful. It’s just really slow and we’ll want to change that. So we believe that exactly what you said, that if the end users get more options in rest time, that if you know software like this is used for the preliminary engineering, so that whether the engineering firms, the EPC is the technology providers are forced to provide these options to clients and do more for them in these stages without having to spend all that money and all those resources, it’s totally going to change the game in the industry. We’re going to see more sustainable water infrastructure, you know, everywhere that the software is used.

Ari Raivetz:

That is our mission. As a company, we actually believe this can expand well beyond water, but that’s a discussion for another time, but I really believe that it’s going to change things. Then we talk about our challenge. Our challenge as a company is to transcend the, kind of, let’s say, difficult culture inside these large firms in the industry, like your former place of work. So I have a question for you, which is if you were at Suez and this was there, how do you think it would be received inside the company? You know, how do you think that the culture that’s been built inside these big companies can adapt to using a tool like this and how fast can that change happen? And, you know, what are your thoughts on that?

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

Antoine Walter:

Well, that’s a broad and difficult question. I would say. You know, I remember people trying to make like a lot of programming within Excel, Excel, macros, just to try to make even a portion of what you do. So those guys, they, they will be incredibly happy because the burden would just be much lower than don’t have to, to maintain pages and pages of Excel, macro, and then ultimately never really, really, really works and so far and so on. So I would say that those guys would be happy, but on the other end, you know, when you were assu as where you were, when you were this kind of very big boys, you build your reputation and you build your full history on the fact that you are the absolute experts in engineering. And now if you had to rely on a computer to do your job, I would imagine that they would be breaking quite hard to explaining you how much a human is going to always going to be better than any kind of machine. So there must be a bit of both. I don’t want to speak for them. I think that could be a interesting question to raise directly to them actually. But I could see both have things. I’m sure you’re going to be very excellent friends with all the people working on the commercial side, just because their life is going to be so, so much easier, but the engineering departments might be a, a castle you have to, to overtake it.

Ari Raivetz:

Yeah. I mean, there’s a lot of cultural challenges with this and it’s very similar to the other part of my career. When I watched ERP and supply chain software get implemented in various consumer products and manufacturing businesses, there were big changes there similar to when CRM got implemented and how sales and marketing organizations reacted to it. So it’s something that, you know, I’ve dealt with a lot in my career, but that’s kind of like, what’s going to define the success of transect is our ability to transcend those cultural challenges inside of companies. And I mean, when it comes to enterprise customers, but we customize the rules based on the rules that their engineers use. So, you know, if those engineers are using a handbook that can be put into code pretty easily, actually. So at least easily for us anyways. So that’s the kind of path that we’re on.

Antoine Walter:

It’s good because it’s not a show. It’s not recording, nobody’s listening. So I can say that. No, but I remember there were some tools that we were using and the guys which had developed the tools were no longer with the company because they’re retired like 20 years ago. And nobody had a clue what those tools were relying on. We just knew that empirically it was working. So that’s the output was giving you something which was realistic, but in between it was like black magic. So I think this is the kind of thing you have to build this trust in your tools so that you can blindly trust them just because you’ve been building them in 2000 plants. So whatever happens in between ease, okay. Even if you have no clue what it is. Yeah.

Ari Raivetz:

I mean, we hear that all the time, all the time, you know, we hear people say, we’ve tried this before. We’ve tried this before two or three times, it can’t be done. We hear people say we have internal tools that we’ve been using for 15 years and nobody knows how they were built. I mean, we hear it in almost every conversation and then we’ll just show them what we have. And, you know, from there it’s more about a discussion of how do we implement this in our company? How do we change behaviors and get people to adopt it? And you’re right. I mean, the commercial people love it, but the engineering teams are typically the gatekeepers. And it was very similar to the experience I had at Organica because the commercial people wanted this. They could never get responses from engineering fast enough ever. They were calling them at all hours.

You know, when is this going to be done? When can I send it to the client or the client changed their mind, they want this. And then the engineers would throw their hands in the air because all I have to redo everything. And when we launched it and we’ve seen this at various mines at transcend, the engineers love it because all of a sudden, they don’t have to deal with that stuff anymore. They can focus on the real problem solving, which is why they got involved in that job or vocation in the first place, you know, and it totally changed the kind of happiness quotient for engineers. And I mean, we saw better retention than ever before because of it. So I think it’s a real game changer in ways that we don’t even think about.

Antoine Walter:

So actually what’s your path forward because you just joined the accelerator elemental accelerator. And if I got it right, being part of that cohort means that your targets is to make a factor 10 on the company within two years. So what’s your path forward to achieve that, which is a huge ambition.

Ari Raivetz:

Yes. And we’re super excited to be part of elemental. It is, you know, more than 800 companies apply and only 15 were selected and wow, it’s super competitive. And she has a group of very passionate people who are really smart and experienced in driving impact. And, you know, I’m a big believer that we can’t fix our climate problems. One project at a time know that we need scalable ways to do things. And I really believe that software can provide that. And I think a lot of the problems that we face are in the design stage in that early stages of project development, when not enough options are considered, new technologies are not considered and this artwork and change that. So, you know, our goal is to start by building it out within the water sector, there’s in the U S alone. You know, we estimate there’s more than 70,000 water and wastewater engineers that could potentially benefit from using the software.

We target them and try to make their lives easier. And then at the same time, you know, we look at expanding this to other industries, other places where critical infrastructure is being designed, inefficiently, whether that’s power, whether that’s building design. And one of the first use cases for the software this year was actually to take parking lots, enter the dimensions of a parking lot and generate drawings to turn it into a COVID testing facility, a rapid COVID testing facility. So there’s all kinds of ways that, you know, we can grow by using this design automation concept in other sectors, even beyond water. So that’s kind of the path forward for us in the future.

Antoine Walter:

And what’s your long-term vision because you know, it’s acquisition time. If you see that the number one in the world is able to buy the number two, in the words, in the water world, you could imagine that anybody can be bought by anybody. And that that’s somehow is your far away background. So what’s your longterm vision for transcend. Are you going to be there the next big boy in five, 10 years time? Or would you say that there might be a chance at some point that one of those big players just says, it’s not possible to have so much value outside of the company and they need you, they need you to be part of, of the group.

Ari Raivetz:

No, I’ve been involved in startups for pretty much my whole career other than my years of bank of America. And you know, the only thing that’s certain about startups is that stuff is uncertain. So never know where things can go. So, you know, I won’t talk about the kind of potential end game paths here, but I do think that we’re at the beginning of a transformation in the engineering and construction industry in both water and in other sectors related to design and critical infrastructure. And we are at the beginning of this transformation because we’ve ignored climate change for way too long. And the problems gotten really bad. And now we have to act and public sentiment in Europe and also in the U S even has changed and people want to act right. I mean, I have two 13 year old daughters, I think about what their life is going to be like, what their kids’ lives is going to be like, and we have to change. And so we want to be part of that transformation. We want to help show what’s possible by using software to kind of scale climate impacting solutions. And that’s really our path forward.

Antoine Walter:

Well, that’s a beautiful mission. I have to say, I have to be cautious of your time. I have to be cautious of the time of the people listening to us. I could be discussing that matter with you for another hour, but I’d suggest to you that we switched to the rapid fire question.

Rapid Fire Questions

Antoine Walter:

So in that section, I just ask you questions that I try to keep short and ideally you can try to keep your answers short as well, but don’t limit yourself. If you have to be a bit longer and to elaborate, I’m not going to cut the microphone. So first question, what is the most exciting project you’ve been working on and why?

Ari Raivetz:

So, one of the projects we’re working on is helping kind of a new market entrance into the water space with being able to scale their offering quickly. And I just think that’s a great use case for the software because when you’re introducing a new product, typically firms will set up a whole team and, you know, it’s not always the most economic way to do that. And I think by using our software to do it, you can do it in a very kind of resource light manner. And it gives the project a much better chance of success because their whole rate is so much higher when you have to have a big team involved in kind of a, a new initiative like that. So I’m super excited about that.

Antoine Walter:

So that’s a technology supplier, or

Ari Raivetz:

I don’t want to say, but it’s in the space.

Antoine Walter:

So what’s your favorite part of your current job?

Ari Raivetz:

I just love building the team and that’s really the most exciting part of it for me, you know, seeing how people grow into their different roles as the company grows. I mean, usually, you know, in pre COVID times, I probably got to answer this by saying, it’s sitting with customers cause that’s really what I love, but on the zooms and stuff, it’s just, it’s hard. It’s hard to do that in the same way. So yeah, I mean, but that’s been so far at least, you know, for the past 11 months or so, that’s been the most favorite part for me.

Antoine Walter:

What is the trend to watch out in the water industry?

Ari Raivetz:

Interesting one, I mean, obviously the whole Veolia Suez potential merger, you know, is, you know, has the potential to shake up the industry. I think one of the likely unintended consequences of that if it were to happen is that you’re going to have a huge amount of talent. That’s out there. That’s passionate about water. It’s funny from Philadelphia. People always say that people from Philly stay in Philly, so they lived their whole life there and they never leave. And I think the water industry is the same way. You know, when people get involved in water, they get passionate about it. They never want to leave it. And so I think you’re going to see all this talent, you know, trying to innovate in the industry and hopefully usually mergers kind of stifle innovation. And that’s one of the arguments in antitrust theory. But in reality, all these people out there trying new things, going to startups, I mean, that’s exciting. It could spark a lot of innovation in the industry.

Antoine Walter:

That brings me to a, an unforeseen question, sorry. I said it has to be short and I’m the one making longer. But back in the days when I was in Suez, I had the impression that it was about big groups, that the water treatment was about big groups. And over the past two years, I see so many startups left and right, which are really reshaping this industry. Would you say that I was not giving enough attention or would you also say that there’s a trend to a bit more new bloods in this industry?

Ari Raivetz:

It’s interesting. I mean, historically it’s an industry with massive players on top 20 huge companies and then tons of tiny little companies. Most of them never make it. And if they do make it, they usually get snatched up by one of the big guys. But I mean, I do think that with the amount of consolidation that’s happened, you know, you have the potential for a lot of these startups to grow into their own right, to become major players in the industry. And so, you know, if you were to look at, at the water sector in 2030, you might see some new names in that kind of mid range of the industry who have grown organically, or maybe through some smaller acquisitions on there.

Antoine Walter:

So let me make a up visit with you in 2030. And we have to check that. 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 less

Ari Raivetz:

Making the customer happy is by far the most important thing in any new project, especially for a new company like transcend, how about the least? I mean, I, I’m a very detail oriented person, so I care about most stuff, but I guess if I would have to say the least, I always feel like it’s more important in the software world to get a product out there for clients than to get the perfect product out there for our client. So the expression that, you know, don’t sacrifice the good in favor of the perfect comes up a lot here, and we will release a product with less features to make sure it’s out there and they can start to see the value because for us, it’s not about the software, it’s about how the business is going to use it. That’s what business process automation is about. And that’s what cultural transformation is about. So we want them to start using it as quickly as possible.

Download my Latest Book - for Free!

Antoine Walter:

I think that’s one of these rare occasion where the French language is a bit catchier than the English language in French. We safe face and NewCo parfait, Dawn and perfect have the same sound, the same to say basically done is better than perfect, but

Ari Raivetz:

That’s great. That’s great to brag about being friends. So let me brag a bit.

Antoine Walter:

Do you have sources to recommend, to keep up with the water and wastewater market trends?

Ari Raivetz:

I use LinkedIn a lot for that. I think it’s great and Twitter a little bit less so, but LinkedIn is a great source and following the right people there, I think water online is great. It’s been around for a long time, so they have a lot of good content there. Those are two, two good places that I would recommend.

Antoine Walter:

So talking about these good people you should follow. That’s actually my last question, would you have someone to recommend that we should definitely invite to the same microphone?

Ari Raivetz:

I will suggest that you invite Cambrian innovation, who is another elemental portfolio cohort company in this cohort with us? I think that they, they would be a great guest. They have an interesting technology on the wastewater side and they’ve been around for a long time in the past, a lot of different experiences with kind of getting adoption for that. So that’d be a great guy.

Antoine Walter:

So I make sure to extend an invitation, talking of a great guests. I have to say we had lots of fun recording these interview with you. I’m excited about your offering. So I’m really looking forward to see how that’s going to develop. Thanks a lot for, for your time. And thanks a lot for all the value that you brought us. And where can I recommend our listeners to, to have a look at you? Is it the website of transcends that you LinkedIn?

Ari Raivetz:

Yeah. TranscendH2O.com is the best place. You follow me on LinkedIn follow transcend on LinkedIn while Adam tank on LinkedIn. Who’s our director of digital transformation and,udoes a lot of great stuff on, on LinkedIn. So check us out. Thank you very much for having me today.

Antoine Walter:

I put all the other, the links in there in the episode notes for the one listening to this. So thanks a lot. And let’s meet in 2030.

Ari Raivetz

Sounds good.

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post Save Time, Boost Value and Wow Customers in a Snap with these 5 Simple (AI) Steps appeared first on (don't) Waste Water.

GF Piping Systems is the global expert for the safe and reliable transportation of water, chemicals, and gas.

This episode is part of my series on Water Digitization – check it out!

What we covered:

We just turned 2’000 downloads with the #podcast, and I still can’t believe it!

I remember that anecdote when I was three weeks into the experiment. 

I was discussing with one of these “influencer gurus”. 

She asked, “so what’s your show about?”

“Water & Wastewater Treatment”

“Well, that sounds niche…”

I smiled and went on.

7.8 billion people rely on water to survive. If that’s a niche, I don’t know what’s not!

Fast forward to last week: we not only passed the 2’000 downloads but also reached 75’000 infographic views on LinkedIn!

I get this is still baby steps compared to beauty influencers on Instagram.

But it’s something, and I’m so glad we achieved this something!

Every step towards better information sharing in the #waterindustry counts! 

Every step towards the preservation of water counts.

So to celebrate together, and summarize one of the main trends my guests have been addressing over the last weeks, I prepared you a special episode:

How to cleverly embrace the digital craze

The rapid-fire questions  are this time for you:

What topics would you like us to address in the coming weeks?

What is the trend we shall explore?

Who should I invite?

I’m thrilled to read your answers! 

Get the 5 Page Synthesis for free!

Wastewater treatment processes can be very inefficient, especially when it comes to energy consumptions. This is where digital will more and more have our industry's back, by supporting process optimization!

But digitization is a long-ball game. We need to invest today in data collection and information gathering, in order to reap the benefits tomorrow.

This is also a financial challenge: rather than investing in expensive extensions and revampings, there is a broad untapped potential to leverage in many plants!

Your best Email

Please enter a valid email address.

Get the Infographic!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.

Resources:

Send your warm regards to our beloved Sponsor on their website!

is on Linkedin

Antoine Walter on LinkedIn

Full transcript:

20% of the songs available on Spotify were never listened to. Not so long ago, Instagram (then called “Burbn”) enabled you to check in at some locations, earn points to hang out with friends  and – as an afterthought – to take pictures. Oh, and YouTube was built to be a video dating site.

Well there you are! Fully equipped to cool down a digital enthusiast, the next time he proclaims that IOT is the future of water and wastewater treatment. But wait, should you?

In fact, the stories these anecdotes tell are a bit different.

Why would you upload thousands of songs and keep them on your data centers if no one ever calls them? Simply because, you had no way at first to determine which ones would be endlessly listened to.

Sure, an early-adopter of Burbn, checking in at Mount Rushmore while waiting for his YouTube date might hardly recognize what both platforms have become. Yet, his behavior at the time framed those digital behemoths, gaining them billions of subscribers (and dollars) on the way.

Future is always uncertain. However, you can still shape it.

Digital applied to Process Optimization

As water professionals, we know that our processes always have room for improvement – and that’s an understatement.

Take a typical wastewater treatment plant. Aeration tanks burn about 60% of a plant’s energy, essentially to maintain bacterial activity. It is yet very common to use a combination of throttle and break to keep those microscopic workers in their comfort zone.

You know the story: whenever the mixed liquor suspended solids (MLSS) exceed your intended threshold, you turn up your aeration (the infamous and energy gluttonous “turbo mode”). Then, while increasing your sludge extraction to bring your MLSS back down, you might brake a little too hard… and end-up inhibiting your nitrification.

Factor in that whenever you change your sludge extraction ratio that affects other areas of the plant (settling tanks, digesters…) and even if still on helicopter view, you get a sense of operation complexity.

However, focusing on MLSS or nitrite levels tends to forget the root cause of those fluctuations: the incoming organic load.

Every WWTP is equipped nowadays with carbon sensors at the inlet. Yet a pollution spike, “early” detected at the plant inlet, is the wastewater treatment equivalent to your car detecting you might crash, when you’re two foot away from the tree and still driving at 55 mph.

Your plant is not lying in the middle of a desert, treating wastewater it creates. It is instead connected to a sewer, where residence times range from hours to days.

Download my Latest Book - for Free!

Sow today to reap tomorrow

The spike in organic load which causes your headache when reaching the plant probably appeared some hours ago somewhere in the sewage network.

Imagine if neither you, nor the original polluter were to accept the passive status quo! Pollution could be reduced at source, while you would prepare your microbial chaps to hold the siege.

Nevertheless, collecting organic load data on sewage networks is not easy as pie. First, the corrosive atmosphere in the sewer doesn’t really make it a go-to place for a happy gathering. Second, complex sensors did not become instantly cheaper just to support your optimization crusade.

This is where data has your back.

It is absolutely doable to build models that use simple indicators (pH, conductivity, temperature…) to extrapolate quality factors (like organic load). It “just” requires you to build a critical mass on data.

This is neither science fiction, nor rocket science as Ari Goldfarb explained in Episode 6 of the “(don’t) Waste Water” podcast. Companies of the likes of Kando or Emagine simply leverage AI and Machine Learning on one end, of-the-shelf sensors on the other to provide helpful insights on sewer and pollution loads.

Operators that used to detect one quality shift per month while wondering why they had to heavily play with the turbo mode, now notice that quality actually shifts 3-4 times per week.

Meanwhile, industrials that had no clue so far they were a pollution source received a welcome revelation that might prevent them from repeating some mistakes in the future.

Here’s a first take-home message: start gathering data today, even if you don’t yet exactly know what for. Don’t go for the fancy: keep it simple and consistent. The models you might feed with it one day thank you in advance – so does your stunt double with grey hair that will have to pay the energy bills by then.

As much as you might agree at this stage with the Idea that there is room for optimization in each plant, I could swear you still wonder how big of a potential this represents. So did I not so long ago.

Revealing the untapped potential of existing plants

While recording Episode 3 of my podcast series, Thomas Debruyne shared some numbers on plants that underwent the digital diagnosis and remedy route Veolia Water Technologies markets under their Aquavista brand.

Two of those might simply blow your mind. A WWTP in Denmark reduced its operating costs by 40% while increasing its hydraulic capacity by 80%!

Just think of it that way: there’s no need to expand a plant if you can reach thus large untapped capacity. Said differently, this last number implies a tremendous saving on investment.

Too good to be true? Jeroen de Lathouver from Waterleau shared similar experience when interviewing on Hakim El Fadil’s “Smart Water Solutions” podcast. So did Jonathan Grant from Emagin on Dave McGimpsey’s.

When you hear the same lines from various non-connected individuals and companies, chances are there is truth lying beneath (and if not yet done, you probably should start listening to podcasts).

A 3-step process

To reap the process optimization benefits, just apply this simple recipe.

First, make sure to collect the right data at the right place. Second, use this entry to run a diagnosis of the plant, and build up a model (aka Digital Twin) that computes the process behavior. Third, run process and control hypotheses on the digital twin to find out which ones deliver the best output, and share those insights with the plant operators.

It is then up to you to launch such an initiative on your own or to get the support of companies of the likes of the ones mentioned before.

WWTPs are actually not the only playground for this methodology. Applying it upstream, within the factories that contribute to the pollution events, might also rank as a value move.

Download my Latest Book - for Free!

Digital can also support factories

Industrial operators don’t pollute for the sake of harming the environment while smoking cigars and drinking Brandy. It has much more to do with a lack of understanding of the water cycle within their plant.

As embedded and necessary to their processes as water might be, the core of a factory’s mission still lies in the production of an industrial good. Which means that plant operators have little focus on the water cycle, when they notice at all there is one.

However, acquiring data on process and wastewater streams and using it to run a diagnosis often acts as an eye-opener. As Stephane Gilbert, CEO of Aquassay reported in Episode 5, this exercise allowed to divide downtime on a factory’s reverse osmosis skids by 15… at the only expense of an additional valve.

If water is not everywhere on the radar, operation costs surely are. Factoring in the opportunity of a data driven preventive maintenance should achieve to convince industrial players: it almost builds for a no-downtime guarantee.

For sure, digital is not going to disrupt and replace your plant operators. But it will lighten the load on their shoulders upon making a decision while giving them headspace to take action on all the other tasks a human still accomplishes better than any machine.

Statistics say, you’re watching your smartphone 150 times per day and reacting on an SMS within 4 minutes. Why would you still rely on a single daily analysis and your operator’s gut feeling to run complex water processes? Act on the incongruity today: you’re also allowed to reap the benefits of the digital craze!

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to Cleverly Embrace the Digital Craze in the Water Industry? appeared first on (don't) Waste Water.

AM-TEAM is a company offering leading-edge simulation services for process optimization and design.

What we covered:

Why developing new water treatment processes the conventional way is unnecessarily slow and expensive – and what to do instead

Why having a functional process that you don’t understand is useless

The simple trick that allows you to take the risk out of experimenting with your plant’s operation

What’s in for water professionals, beyond today’s capacities of process modeling

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

Get the 3 Page Synthesis for free!

Discover why piloting can be a painful step, often torn between two directions (and the implications this has)

What if you could skip this step, and run several virtual pilots at once, in order to only translate into the physical world what's proven to work?

Let's focus on an application example that cuts long debates short, in the field of ozone treatments.

Your best Email

Please enter a valid email address.

Get the Infographic!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.

Resources:

Send your warm regards to Wim Audenaert on his LinkedIn page.

Check AM Team’s website

is on Linkedin

Antoine Walter on LinkedIn

Full Transcript:

These are computer generated, so expect some typos

Wim Audenaert:

Hi, everybody. We have a very special episode of a LinkedIn life broadcast. I’m actually the guest. I’m not the host guys, but I always start talking. It’s my feet. This is my feet, but we have another special person here. Antoine Walter, actually, I have met Antoine the first time on LinkedIn. And this is how I started following the podcast of Antwan. I think it’s a great podcast. I love it. And now apparently I can also play a role in this podcast. How beautiful is that? So the podcast is don’t waste water. It’s actually not usually alive. We do it live because we thought it was going to be interesting for you and you can find the podcast, hopefully. Hopefully. Yeah, well, that’s depends a bit on me I guess. And the link can be found@dww.show. So that is the link where Antoine, he has a lot of interesting episodes. This will probably be the least interesting one, but he has a lot of interesting episodes. So yeah. And when I think I’m actually crashing your podcast, I will give the word,

Antoine Walter:

Thanks for the intro and the thanks from welcoming me and my podcast. So it’s kind of inception. I’m hosted by my host, but welcome Wim. Welcome to the show. Actually, it’s quite strange that our paths didn’t cross before, because you have been active in micropollutant treatments. So was I, you have been active in ozone treatments. So was I, you know, people that I know as well, but it looks like for whatever reason, we had to cross a different way. And that’s today. The first time I see you in person, I have that feeling because I’ve been watching videos, I’ve been reading your blogs and I have the feeling I know you. And I think I have this little advantage on you that you don’t know me. So welcome to the show Wim, and maybe to start for the people that might not know you yet, can you start maybe by pitching yourself and pitching your company?

Wim Audenaert:

Sure. Antoine. So, first of all, I want to thank you actually for having this opportunity. I feel honored because I have seen who was in that podcast before. And I mean, these are very experienced people, very valuable for the water industry. So yeah, I’m honored to be here. It’s a pleasure, but I will start introducing myself. Of course, I’m Wim Audenaart, my main background is water and waste water treatment technologies, mainly physical chemical, and a lot of modeling. So modeling simulations has always been a very important part of my professional activities. I started my career at Gent university. I had the ambition actually to become a professor and it was really not far from that. In the meantime in parallel, I also had entrepreneurial aspirations and then I decided to go found this company with some very nice colleagues four years ago in 2017. So our company, AM team, the name comes from the A-Team. The A-Team is a special force Team. It’s a television series from the eighties, the American people will definitely know it, but we want to combine a very complimentary people to deliver something that is extremely unique. So our company is highly specialized in simulations for the water industry. So we do computational fluid dynamics. We do multi-phase one, two, three phase five phases. We have a lot of projects with where we add a lot of different solids classes. For example, we’ve combined kinetics with CFD computational fluid dynamics, and we provide digital twins to our customers or digital. I heard a very good name of that. Recently. It’s like virtual twin. There are different names for that. And now we also offer a couple of other computational services. So we have three main customers which are end-user of processes end users, for example, utilities and utilities, but also large industrial customers with operating plants. Then we have consulting and engineering firms, but we also do a lot of work for technology companies. And this is very important because we help them in is quite confidential typically, but we help them designing novel technologies, scaling them up or improving their existing technologies. So it’s quite revolutionary. We are Belgian based company. We have customers all over the world. So really a big fraction of our revenues come from outside Europe, US until New Zealand, Japan. So customers everywhere because of the niche focus. So that was my short introduction.

Antoine Walter:

The niche focus is actually a nice transition because you know, I like to start with a simple question. What’s your secret sauce? And there are many aspects in what you just said. Let me just let you answer this one. And then I give you my impression of what your special sauce is

Wim Audenaert:

That would be interesting. I’m a very curious when. Yeah, yeah, indeed. Well, I think our special sauce is, but indeed I think I should talk to the markets. Maybe for that. I think our special sauce is that we are highly capable of performing the best simulations in the world, in our domain, in the thing, what we do combined with a deep understanding of the process side. If you do that magic can happen. It means, for example, if you look at computational fluid dynamics, people generally look, Oh yeah, you have a dead zone. You have some flow it’s hydraulics, but I don’t call CFD as such a process simulation tool. You can also look at it from the process angle. You add the bubbles, you add the reactions, you do your simulations different because you understand how the process is working. This allows us to develop unique models that are globally unique, which are allowed really to do quite innovative stuff or to solve problems that were hard to solve before. I think that’s our secret sauce. And then a second one. So this is the extremely advanced backend. I think we have really advanced stuff. We work in an advanced way. We have very good collaborations with also researchers, academic institutes or front end. I think it’s very accessible for the customers. So we don’t overcomplicate things to our customers. They often don’t see how complex, how sophisticated our backend is because that’s equally important. Otherwise the market will never believe in it if you sell it way too complex. So I think that’s our secret sauce being very advanced, but not acting like that in front of the customers.

Antoine Walter:

That’s an interesting one, and probably a right one. Now I give you my impression. My impression is that your secret sources, another secret sauce is that you have a different approach. We’re living in a world. The water industry is a pretty conservative world and you have a specific tone. You have a specific, I mean, I can feel the culture about AM Team. Maybe it’s an impression, but that’s my impression. And I was wondering if you also feel that that’s an important take in your success, whatever you do, the way you approach it, the way that’s you show what you’re doing in your communication, but also in the way that you present the people working with you.

Wim Audenaert:

It’s true Antoine. So, I mean, the first part I was talking about, let’s say that was our technical USP, unique selling points. We also have indeed a culture, cultural USP. And did we try to communicate a very distinctively, quite an unique way? The team is in our company name, the word team is in our company name. This is one of the core values we have. One of our values is also fun or having fun. So it means if you work with us as a customer, it means that you probably are getting energy out of this project, personal energy. If you’re working for AM team, probably you also get a lot of energy out of your job and in the end, as you also know. Yeah. It’s all about technology. Typically we talk about, we are engineers. We are, we look at the treatment, but it’s people business and this is the thing everywhere. How do you attract good people and especially keep the good people. Yeah. By having a very good culture and not only talking about it, but really doing it. That’s a big difference, right?

Antoine Walter:

Yeah. It’s about walk the talk and last question in this first section, before we dive into the serious stuff, but I’ve read your advice that you should not read 20 books and then apply them whenever you wish. But instead of that, you should read one book and strive to apply it day by day and to do get it. And then you go to the next one. So I was just wondering, what’s the book you’re up to right now

Wim Audenaert:

Up to, well again, so I’m a parallel. I hate call it parallel readings. So typically I read like 20 books in parallel. So that’s a very difficult question. I cannot name all of these 20 ones, but I can say that I’m currently reading, for example, crossing the Chasm. I don’t know if you’re familiar, but you’re like also have a very good commercial background. You will definitely know that book with the innovation curve. Why is that important for our company? Yeah, because we offer some of our services are quite, I can call them quite disruptive. We have some lower barrier services, but some of them are quite new. And then you need to have a very good strategy to bring it to a rather conservative market indeed. And then another book I don’t only well read business books. And for example, reading the biography right now of the guy, Phil Knight, Phil Knight, the founder of Nike, how he founded his company, getting some shoes from Japan, selling them in the U S it’s so inspirational. It’s like every, every of these big, huge companies are born like that. I am looking up to them. So I believe reading biographies, history, economics, psychology, psychology book. I’m currently reading is wild Charles Munger. That’s the business partner of Warren Buffett’s. Yeah, he has a very good book. That’s poor. Charlie’s Almanack, that’s a Bible for me. Wow. It’s huge. But they’ve contained so much wisdom.

Antoine Walter:

The best I can wish you is to follow the path of the founder of Nike. Because when I read that book, I was pretty amazed because when you think of Nike, it’s like, you think that company has been around for two centuries. It’s so big it’s so everybody knows. It’s like one of those worldwide companies. And when you read the books, just recall that, well, they started small as well and they might’ve died every second for years. It’s pretty inspiring. I would say

Wim Audenaert:

It is another one is Sam Walton’s biography of the founder of Walmart, same story, very inspiring.

Antoine Walter:

So that there’s kind of a pattern. So I hope for you that you follow that same pattern because you are the one founding a company I’m not. So thanks for those advices. I have to say, I have new books on my reading list. Thanks to you. If it’s fine with our proposal to dive to our deep dive of today and what I was pretty intrigued in, what you are publishing is how you can remove all those steps, which are pretty common in the water industry. What I mean by that is that, and I had people on the microphone on that podcast explaining me what’s their go to market? How you start from a very process Idea that’s moves to a pilots. Then the pilot goes to a small scale eventually to a middle scale, to a first full-scale. And then everybody’s watching for five to 10 years. If that things might be burning or working at all. And then if you can prove yourself to have one reference, then somewhere down the line, you get five. And from the five, again, that third chance so far and so on, then you have people in the industry which start talking about the digital twin and the way that the digital twin can be a clever way to run many, many modelizations of your plants, to be sure that you are running the best process at the right time. And actually, that’s what I discussed with my last guest on this podcast. But you take it even to the next step. You are building the digital twin to a plant that doesn’t exist. So the plant is fully digital and only once it’s fully designed digitally, you say, that’s the process, which is the right one. And that’s the one that we can build. So I’d like to deconstruct that and start from the beginning of your process and go step by step. So what would be the first step for that? Well,

Wim Audenaert:

Because it’s, it has become such an important topic in our company. I mean, if you look at their services, this is the service, what we call virtual piloting. So we have a service that we name virtual piloting. The name itself says that, okay, let’s pilot on a computer, instead of in reality, it’s quite, Indeed’s newer people call it digital twins. Or I heard two days ago, we were having discussing with some people from IWA, my co-founder Ingmar and then a Dragan Savage from KWR. He was actually saying, yeah, everybody has his own definition of digital twins. You can call it the virtual clone, whatever. But the thing is, I will walk you through. So if you develop a new process, you have actually two big parts, two big objectives. People don’t see these dual objectives. They only see one, let’s make a technology that works. You have two objectives. The first one is more of scientific nature. Let’s figure out the process. And let’s also determine the optimal conditions for the process to work. When that is done, you actually build a reactor system to host that process. In my opinion, a reactor or a system is nothing else than just a vehicle to get to your goal. So a process as such cannot work. That’s why you put it in a design, in a reactor, the skills you need to develop the process, to do the testing and to do the reactor design, and then to scale it up to successful full scale plant or installation, they are so different. Usually the same people work on that throughout the whole period of time. My impression is that it would be good to work with different skills there and to blend in computational fluid dynamics from the early start. So what do I mean with this? Not making this distinction. People will then build indeed. Okay. Our process works. Let’s build a small pilot. Usually they build a pilot for two reasons. They want to test the process in real conditions. And also they want to tweak the design of the reactor. They want to start tweaking it. They want to start optimizing the process. If you ask me the second one should be isolated and should be completely virtual, completely virtual. Why? Because I have literally seen people building a unit of like meters of height, stainless steel. It’s a reactor of that costs also operating it, measuring a sampling, hundreds of thousands of euros easily, easily, and then it doesn’t work. They manually open it. They start changing the design. Suddenly it works. They don’t know why. So if you don’t know why you are not learning, if you then make a scale up again to a larger scale, you have no principals where you can base yourself on. So you can start from scratch. Why do I say that? You can start from scratch because scaling up is the most difficult exercise. I always say to my customers, water molecules or gas molecules, unfortunately they don’t scale. Along with the reactor. They remain the same size. If your molecules don’t scale with your system, which you’re scaling, you have a nonlinear behavior and it costs huge amounts of money. But I mean, the time spent is years. What if you can cut this process with like a few years, your technology goes to the market way earlier. It is quite disruptive. If you ask me, it’s hard to stop because it works so brilliantly that the benefits are just huge. I mean, people that are now like doubting it, I think they will one day just start doing it. So that’s what I call the virtual piloting. You will accelerate the development of technology by isolating the design. So let’s see if the be used for the design. Of course, we use very realistic CFD models. We include all the process phenomena that we think are important. I mean, you have a virtual clone of your process that is extremely realistic. And if it’s very realistic, you can use it

Antoine Walter:

To start really from the roots for the beginners, for the stupid like me, what do you need at the beginning to feed your model? I guess you still need to have some kind of physical stuff. So I mean, water analysis, water samples what’s the root.

Wim Audenaert:

Yeah. So if people are developing a technology and they call us or they email us, typically what they have, if you’re in development stage, you have a small scale system that works, that typically is happening either in a lab or a small pilot on site, we have seen both and then they ask a questionnaire. We have to scale this up one or two orders of magnitude. Well, first we will start modeling this small scale installation because it’s a test installation. They have a lot of data on that. Measurement data flow. Everything is very well monitored because it’s an experimental setup. So we need some flow rate data and some water characteristics. For example, of course, some operational data like flow rates of air as well. And then a, the design, not in a digital file by some clients just draw the dimensions on a paper in order that we can make a 3D design. Then we bring in the physical equations and we start. So actually it’s two things input from the process side, which is actually readily available. And then the design inputs.

Download my Latest Book - for Free!

Antoine Walter:

The lab or the pilot version of it is already working. It’s a process which works to treat a small amount of water when you’re scaling up. But can it happen? That’s on that scale already. There are still open questions on the process.

Wim Audenaert:

Well, there can be open questions. Indeed. Some of our customers could not explain a certain behavior of technology. I mean, for example, the oxygen transfer rate was way lower than expected or the conversion rates were lower than expected and they couldn’t really grasp it. If you then perform an analysis at that scale, we have had quite some situations where you really see the problem. And of course the eyes opener. I mean, Oh man, this is the cause of course. Yeah. But once you have done that, so we typically develop a very good model there. It takes some weeks. I’m not talking about a year because that’s a misconception, CFD cannot be used. It takes months. We just finished projects within weeks. Of course, a large and big scale projects. They go over months because you need discussion with the customer. But I mean, once you have done that, we scale up without any intermediate piloting, we start going and we scale up this reactor with one or two orders of magnitudes. Typically the first prototype looks not so good. I mean the virtual prototype and then we make four or five new ones and there’s always one that is iteratively working. So you see the problems coming with, we change and you end up with a system that you don’t only understand fairly well. You know why it works? The system is the best one. It’s not like a compromise on design.

Antoine Walter:

So when you say it’s the best one, how can you assess it’s the best one? Are you running multiple hypothesis in parallel? And then choosing like Darwinism taking the best one. Exactly.

Wim Audenaert:

For example, in one system, the inlet outlet configuration can be slightly different. Maybe they’ll a water level is alterate it’s maybe the height of some baffles or membranes or whatever is in there. We change. And some of these perform best in need. Of course. Can you keep optimizing it incrementally? I’m pretty sure, but why do I say the best? If you would have not taken the virtual route, you would have never known what design would have been the best. Maybe there was still an improvement, significant improvement possible.

Antoine Walter:

I think that’s the story of what the treatment, if you look at it I think there’s no two plans which are really identical because you always identify something which would have been better and what’s develops over 30 years or 50 years. If I get your rights on your scale, it’s more a matter of weeks because you can evolve the model and have much more iterations than on a concrete tank. Exactly. Well, yeah. So when you say you model something, how do you, how can I understand that again? Don’t forget. I’m stupid. When you’re modeling, for instance, let’s say you’re an MBBR, there’s the MBBR from one supplier. And then there’s the MBBR from another supplier. If you listen to them, they are gonna explain you how different those two design are and how different everything they do. And the process is because it’s not the same chip inside because it’s not the same overall design and some of them might be proprietary and some others, it might be just common design that everybody is doing the same. How would you address that? Does that mean that you have, let’s give three names so that I don’t have only one, but do you have one design for Veolia, one designed for Suez and one design for Wabag? Or do you have one thing which is called an MDBR?

Wim Audenaert:

Yes. I would say we have one thing that is called an MBR. We have the model in place that can model a MBBR. So it means water air and these very difficult to model solid granules. So we have constructed a model that is based on physics that is actually applicable for many designs. So typically what we do is we are approached by people, either designing a new MBBR or we are working together with people that are developing these in house technologies, these technology vendors of MBBRs in needs. I’m just giving examples. So we don’t really, I mean, there are many people, very good at selecting technologies. I think on the market, we don’t want to replace engineering and consulting firms that put together treatment trains. What we want to do is we add something that nobody has to really optimize the design. So I would say it’s a generic framework. Of course, if we work for technology companies on a core technology on a brand name, but that’s very under the radar, it’s all confidential. We cannot really say a lot about that.

Antoine Walter:

So you’ve mentioned the technical aspect of things, which is finding the right process and seeing how that might scale up. You’ve mentioned in the introduction, the human aspect of things, but what about the economical part? Because sometimes the best process might be technically the best, but might be also the most expensive. And then if you compromise maybe on five, 10 % treatment capacity, you might have something which is much better in terms of money. And then it’s, there’s a subjective part. How do you weight economics against techniques? Is it something which is within your reach or is it something that you leave up to the end user or to the engineering consultants?

Wim Audenaert:

I would say the major decision there is taken by the end user and the consultants indeed because they are doing this at shops right now. What we of course offer is if it really comes down to operational costs or design costs, of course we can assess. If you can maybe make a reactor 20% smaller for that specific process here, I’m just talking about, ozonation, we have done a huge amount of work on that. I also did my PhD on that topic. It’s close to my heart, but that process has so many opportunities to improve. So are you can both reduce CapEx and OPEX significantly by doing this kind of modeling. And then of course, if you then make the comparison with the technologies, it can impact your decision. But typically we are really a company that works together with a lot of parties. We are not like a very big consulting engineering firm that does all these things together. We try to really focus what we are very good at and then work together with the parties that are good at other things,

Antoine Walter:

Typically on a project you’re there for the process. There’s a manufacturer which is there for the equipment itself, probably a consultant an end user. And that’s the winning team. If I get you.

Wim Audenaert:

Yeah, exactly. That’s a good winning team.

Antoine Walter:

Actually. I’m too tempted. Sorry. After that, in this one, you are an expert in ozonation. I recall from my years where I was working in, in this domain, we were running a pilot on the removal of micro pollutants and we had a pre ozonation. So we have raw waste water. You put ozone inside and on the paper and all your wisdom and all the scientific papers. Everything tells you that this has no chance to work because your ozone is going to target everything. But the micro pollutants. And indeed, we had to significant reduction of the micro pollutants through this pre ozonation. And as you said, the thing, we had something working, but we have no clue why. And then you cannot industrialize it because if you have no clue, why then you cannot upscale you can’t, I mean, you cannot consistently take warranty or say to someone it’s going to work because if yourself, you cannot explain it, then how could you sell that? Is it the kind of thing, the kind of riddle that you can take on and say, Hey, by the way, there’s a way to model that. And the model might tell us what’s exactly happening. Yeah.

Wim Audenaert:

My answer is yes. We have been working on a mechanistic kinetic model for the last four years. So now it’s really a, like a very advanced stage it’s in the application stage and this mobile contains mechanisms. So it means if you start modeling that process, you know, which micropollutants are being removed, how many hydroxyl radicals you have, how much ozone you have and which part of the removal is caused by the radicals. And by the ozone for each of the individual components. I can say, of course, in raw wastewater, you will not have any measurable ozone, but it has been there. Right? So you have had an exposure. You added, for example, 10 milligrams per liter, you added 10 milligrams per liter. It’s not there anymore, but it reacted. So you had an exposure. The exposure indeed can be more than sufficient to remove some of the pollutants very easily. And it might relate to a huge amount of radicals. Yeah. Given the organics, the organic matrix, ozone, will start stealing electrons from the carbon molecules. It steals them. It will produce extra headaches. All right.

Antoine Walter:

So, well then you mentioned it is Amazon, right?

Wim Audenaert:

Exactly. That’s Amazon. So am Amazon.

Download my Latest Book - for Free!

Antoine Walter:

Oh no. That you say it. I get the pun. It’s funny because I saw it written there, but I never got it. Okay. Let me Pat myself. So let’s take this one as an example. How do you come with such a model? What are the steps to develop such a model? Yeah,

Wim Audenaert:

Yeah. Yeah. That’s another good question Antoine. A very good interviewer. I think you have to start simple. That is the main rule. I think for any modeling, don’t start overly complex. And that is a big risk for scientists that know too much about the process. They will only see what is not in the model and engineer. It looks at it from the practical side and just puts up some first equations. I strongly believe in the Pareto principle, try to create the first 20% that gives you 80% of the value. I really believe in this for modeling because yeah, you can keep extending, keep extending, but I really believe that with a few equations. I mean, what are these equations? These are the key principles of this process. You have to, of course know some of the process to derive the key principles. One of the key principles in ozonation is for example, don’t build a model that only has ozone in it because you will not be able to predict any of the pollutants that react with hydroxyl radicals, right? Bromate is formed by both ozone and hydroxyl radicals. So you simply need both in your model in order to accurately predict bromates. So these are, for example, some principles in ozonation. So you isolate these principles and then you have to, I think, build a few equations around these core principles and take it from there. And it gets so much motivation because you can already start calibrating your model, you see the data being described and of course, then you can make it gradually more sophisticated. I think that’s a very good exercise. If you start overly complex, you might really get disappointed.

Antoine Walter:

So maybe what you’re saying is the Google approach. It says this iterative thing that you have to first put something which has two legs and that’s pretty it. And then from that, you build the details until it really describes what you see. And once it describes what you see and you get a fair understanding of what’s happening. Exactly. That’s true. Okay. So that’s what you did with your ozone modeling, but you expanded far beyond the ozone with your company. So do you have to build this kind of model for each kind of treatment? And if yes, of course there’s thousands of treatments. So you are busy for the next century, if you have to describe everything.

Wim Audenaert:

Well, the good news is that a lot of very beautiful models have been developed by a very, very knowledgeable people in our industry. I’m just talking about nitrification, denitrification, phosphorus secondary settling. There are really many, many things described. So if we can use an existing kinetic model, we can readily plug it in, in our CFD model and we can really use it. Of course you have to implement it, but that’s what we regularly do. If we a very big question from a customer. And we also think that a lot of other customers will face this question. Then we start developing an own model. We are not really scared of that. So our company is also spending quite some effort on R&D. Only of course, if we have looked at the markets, we will not like tailor for every customer at one model that we can use once. But we will try to look at the market, how big is this need and this problem. And then we start going. For the ozonation, it was clearly the case

Antoine Walter:

Clearly. Well, actually for the ozonation, I can only subscribe to what you say because it has been for years, this big debate bromine/bromate. When is it at risk? When not? Should the wastewater be treated to the drinking water levels. And then the roommate, of course, it’s even more of a problem. And if you’re solving the problem of micro pollutants at the cost of creating bromates, of course you are just replacing a sickness by a sickness, which is even worse. So I think there you’re bringing clarity, or at least in my eyes, you’re bringing clarity. But to me, the interesting aspect is that’s what’s replying. And the beginning is allows you to scale up from lab process to the operation. So to the full scale, but is that the end of the road? Or do you have then next steps to say now? Okay. We have designed something it’s working, but there’s always room for improvement. Is it something that you leave to the operator or do you still support them? And is it also inside your reach?

Wim Audenaert:

Exactly. Unwind. It is within our reach because we have touched upon so far in this podcast on the development of technology. Okay. Then you might select technology on your plant. Will I select technology? A, B or C? Okay. Let’s go for C. Then you design it. For example, you use CFD to design this plant. A very good reactor, no short-circuiting. I mean, it’s all is working fine. Okay. It has been built then the operation and monitoring starts, of course. Well, we have a lot of customers that come to us, Dan, in that stage, we have the CFD model. Anyway, we can actually, we have an in hose methodology to export that CFD model to a very simple process model that runs in any simulator. I know for example, you have Imre Takacs on the podcast, so you can run that. For example, in Sumo, the big drawback of only process models in quite complex hydraulic systems is that they have to assume mixing conditions. They have to assume a complete mixing in a large chunk of the basin or reactor in reality is not happening. So what happens is if they neglect the hydraulics, still, they get good fits on their data if they tweak the parameters, but they will have to tweak them from time to time. And actually they are tweaking kinetic parameters to compensate for missing hydraulic description in the model. So what we can do is we can derive from CFD simulations an extremely simple, but very, very accurate hydraulic model that can a kinetics as well. We call that a very realistic digital twin and that can be running in parallel with the plants. So that is the link I think very easily to the operation. We had one other customer where we had the virtual scale-up. So the virtual piloting, we delivered a full scale installation. That was the final one. They build it. We again, transform this model to a very simple process model. And our customer has a process model. He can, he or she can play with themselves. For example, for customer A, this water matrix comes in, how will your technology perform customer B in our technology, this affluent, how would that perform? They don’t have to pilot this. They will run it through this flow sheet model. It can run an offline or online, offline for testing different conditions online to create really the digital twin. So that’s the link

Antoine Walter:

That triggers two questions for me. The first is when you have those kind of processes, if you were running it to get it, you would build the process. So you understand what’s behind. I’m pretty sure you would see if something isn’t realistic or doesn’t make sense. I mean, you’ve probably must be 1000 times better model than I have ever been been. When I was in engineering school, we had models, we were running models and then we had water or two meters high, and then there was no wall, but still the water was not flowing out and we didn’t notice it. So that doesn’t happen in reality. So if you don’t check, of course, you just take results for granted, which are not granted. So you have that knowledge, but I’m not sure every operator has that knowledge. So maybe you might have to have a watch on that. And my second question is while we’re at it? Why wouldn’t you take the opportunity to automate aspects of the plant? Because sometimes the model might be a better operator than the operator himself.

Wim Audenaert:

Oh, well, that’s indeed a very lively discussion going on in the whole water industry right now, the digitalization, where are we going? I completely believe that you can automate a large, if not complete parts of treatment processes. The question only is when will this happen and which part of the process first, probably the most simple ones that we know most of it. And so it means our models are most trustworthy. So it probably those first ones, because yeah, the model you need in order to do that needs to be very good, of course, but then indeed the benefit of such a model is that it looks in an integrated view and an operator has much more difficulties to see in an integrated view. What’s happening. Coming back to the ozonation. One example is we did a study for WEFTEC actually together with Dynamita HRSD and in U S and then Jacobs, a couple of people, they had a very nice biological treatment plant model. We had the tertiary Ozonation model. We coupled them together. What they did is they optimized the sensors to tweak the controls as such that the affluent was more suited to be treated with ozone. What does this mean? Lower nitrites are looking at ammonia. For example, if you combine the whole train with models, you know how an upstream decision will impact a downstream process. And that is probably the main reason why you might be able to make better decisions. One of them probably in needs.

Antoine Walter:

So really having a full plant approach against something, which is really saying optimize this one, that team as this one up team has this one, but then you end up with ozone scavengers in the ozonation. And then of course there’s as a nation gets inefficient. So you run in the most efficient way, something which cannot be that efficient because of what’s before. That’s an interesting one, which brings me to something else, which is, you know, you can digitize many things. And one of the things we can digitize if I get it right, is the sensors. So you take some simple parameters and you put some digital sensors, which are taking your advanced parameters. And then we have been using those advanced parameters for years as a proxy to measure things, which we could not measure. I go back to the micropollutants. We say, you don’t need to measure micro pollutants because if you measure COD, then you’re also measuring the micro pollutants. But now we can also say, if I measure pH flow and I don’t know, conductivity, my model might be able to give me the COD. So I would be measuring very, very simple things. My model calculates something, which is a proxy to something. So now it’s a box in a box. Is there at some point a limit to what we can do with the models or really that’s almost magic.

Wim Audenaert:

Yeah. Interesting. I think you can do a lot. I think you can do a lot. And of course, mechanistic models have their limits. Why? Because if you look at wastewater treatments, by the way, we were both in drinking water treatment and waste water treatment at our company. I love this because there is such a gap in between these two worlds. We really try to bridge this. But apart from that discussion, wastewater matrix is much more complex than a drinking water matrix. It’s so complex and variable that it’s very hard to know everything in your mechanistic model. Huh? I mean the inputs, I mean, extra equation and extra variables, but we have AI coming and I strongly believe in that. I think it has huge potential and these models can collaborate. Imagine you cannot explain part with your mechanistic model. Why not try to fill the black box with your AI model? AI can do stuff we don’t grasp with our brains. We don’t know how AI does it, but it produces a certain output that is valuable. Of course, the question is again, when will this happen? But I strongly believe in AI being capable of bringing us a lot of value in that respect.

Download my Latest Book - for Free!

Antoine Walter:

I am a digital enthusiast and I was told many times that I’m probably too enthusiasts. So for once I’m going to take the other end of the scope and I’m going to give you my star Wars example in star Wars, Luke Skywalker is flying over the death star and he wants to, to destroy the death star. And AI cannot find a way to do that, but suddenly he just disconnects the AI and he feels the force and the force allows him to destroy the death star. So I’m pretty sure that star Wars is a fiction pretty sure, but for a second, let’s look at that one. You know, the operator has his gut feeling and the guy is in the lab, developing the process as also his gut feeling and, and this kind of magic in this kind of, you know, this Apple, which fells on Newton’s head. And then he discovers gravity. Sometimes there’s the beauty of the unforeseen. Is it something which might be disappearing if we rely solely on AI and solely on modeling? And if it disappears, is it a bad thing?

Wim Audenaert:

Yeah. If it will be disappearing or how to say, if it will become obsolete, I don’t really know. All I can say is that you have people with 20, 30 years of experience, 40 years of experience, they sense things that nobody can sense. I also believe in that, for example, they go to a plant, they look at the bioreactor, they look how the swirls of the sludge go. And they know if it’s well settleable sludge or not based on the patterns because yeah, if it’s well settleable, you will have some clear zones where there’s some last mixing very easily. For example, they combine all these observations from the past. They have kind of models in their heads. It’s actually also a model, but it’s a human model. The only problem is that the principles are not written down. So if these people retire, that’s the problem. That’s the benefit of a model. It’s a database, it’s a storage of principles. And I think they go hand in hand, you cannot, as an underestimate experience from people onsite, these people can be of great help. I think in improving models, they look at the output and they will make, they will know if it makes sense or not. They will know if it doesn’t make sense, they will very rapidly lose the belief in the models. If the model shows something that they were suspecting, but could not measure, they become overly enthusiastic. I think both go very well together. How it will turn out in the long term. Let’s see here. I don’t know, but experience is so important. And this is what brings us to the process understanding. If you are a good modeler, a good modeler understands the process sites. Why, if you are only looking at your model and you don’t understand the process, you don’t know where the weaknesses are, or you don’t know which additions potentially could add value. Where is the uncertainty? These are again, the big principles of the processes. I think modelers have to have a good level of process knowledge and then talk a lot with the people on sites and people that have designed so many plants before and things like that.

Antoine Walter:

So that speaks for your secret sauce. Basically you cannot be just a software designer. You have to be a modeler of one on one end of course, but you need to have that process expertise because if things don’t appear to be the way they should, you have to notice it in you, your you’ll get feeling has to help you on that, by the way it’s addressed it. So I’m going to ask you this difficult question. I know it’s almost impossible to answer, but still I’d like to have your take. If you looking at my crystal ball and you’re looking into the future, let’s say two years, five years from now is modeling. Now the new thing everybody is doing, is it still something which is relying on specialists? Like you, maybe a bit more people, maybe your company is even twice bigger by then or five times bigger or the new Nike. I wish you all the best, but what’s your vision?

Wim Audenaert:

Well, I think my vision on that is in the end, if you look at the big trends from history, the new ideas always start with a small group of people. I mean, the specialists able to understand and daring to take the risk, to struggle using this new technology or whatever you call it. And then through time it reaches the big chunk of the market and more and more things get, I call it commoditized. So what I strongly believe in the fact that things we only can do today that would in 10 years, much more people will be doing it. And they will need much less specific knowledge on modeling because look at the simulators that they have become so accessible. So many of them, for example, many people, they are not really modelers, but they can use these simulators for treatment plants. They are so easy or straightforward to graphs. This has already become much more accessible. It will happen for CFD as well. I think for many of the applications, the simple ones, first of course, but here in the end, I strongly believe that more and more people will start using it. On the other hand, there is like another parallel thing. I like very much. Our society becomes very specialized. So we are living in the world of being very specialized. You can build a company on a niche of a super niche of a niche. You can build a successful company on that because you are simply the best in that little micro-niche. So on the other hand, if you outsource work to that company, you address probably high likelihood. They can deliver at a tremendous speed and a quality you can never meet. So probably to answer your question, we have to distinguish between what is your goal? Why do you want to reach? Is it something you should be doing in house? For example, everybody can try a building, a website, but how many companies do build a website in house? There are so many platforms, openly accessible. We are outsourcing our website. It’s not our core activity. So I think if it’s important enough to be your core activity, more and more people will start doing it in house, but still then there will be a lot of, part of big part of the markets where it’s, they can do it potentially, but it’s not their core business because they can do everything if they want to. And they will probably start outsourcing to specialists. So the objective is very important. Then also is the model existing or not? How mature is it? Do you need a lot of experience to handle it?

Antoine Walter:

And so specifically on your company on AM Team, where do you see yourself in two years, in five years? You mean in company growth or in company growth and company boats, you know, it’s the season, all the companies are buying themselves together. So maybe that’s also a way to see it. I’m joking. But I like to think with the crucible. Yeah.

Wim Audenaert:

The Gildan with our team here last week, Oh man, we have plans to buy Suez, damn Veolia did it. Damn, no. We will have to save for a couple of years to buy the whole thing. So that was our joke. But our company, while we have big ambitions, why? Because we feel we can help. So many people globally, too many people to stay small. So this means we want to keep growing. I think hiring will never stop anymore. Now in our company, we will hire regularly new people. So within two years I expect this company to be like 30, 35 people. We are now with a team of 11 people. And I expected within five years, our team size around 80, 85, something like that. And within five years we would really love to be the reference internationally. If you need that kind of stuff, go with AM Team, right? It’s like a no brainer. That should be the goal within five years internationally.

Antoine Walter:

I’m sorry, because I have to be cautious of your time as well. But there’s still one aspect, which is really puzzling me. I have to say maybe I was not watching, but when I was working for a big company, to me, the Water world was a word of big companies. And over the last, I would say one to two years, I see many startups really interesting takes on that markets. And I don’t know if I was not watching or if really that’s a trend out there that more and more people have ideas to nail a niche because there are many, many specialized aspects that you could do as you are the absolute specialist in your absolute fields. Is it your same impression that it’s growing or was I just not watching enough?

Wim Audenaert:

I also cannot really quantitatively judge, but I have the same impression I think. Yeah, probably there are water industry became much more sexy. Some of the utilities have done also great jobs in that to make it extremely like, I mean, there are some utilities worldwide. Some of them are, you look up to these people which vision they put in the field. So that is changing as it has become more sexy. On the other hand, of course you have all these things kind of climate change, water scarcity, even in Belgium where it’s raining a lot, we have droughts, it’s much more on the agenda of the politics and gradually it would be much more on the agenda of investors probably if not already. So probably there is a more interesting ecosystem that might drive all of this. I have written it down on my notes, start your business in the water industry, exclamation Mark. So I think it’s a good moment to start specializing. As long as you’re unique!

Antoine Walter:

Wait, you mean the word doesn’t need an additional marketing company really needs water treatment people, but I’m being sarcastic. But, but it’s true that it’s a field where you you’re less tempted. I have to say when I was in school, it never crossed my mind. I never thought, well, I might create my company and it’s quite inspiring to see people like you or other of my former guests on the podcast, which just went out and say, Hey, I have a different take on that industry and I can do something. And I think it’s inspiring. So I hope it inspires some more people

Wim Audenaert:

One and it’s closer than you think. It’s I did a post on this like a few weeks ago on LinkedIn. It’s closer than you think. Because before starting your business, you are thinking like, Oh, it’s so big and where should I start? Start small with the right customers. And you will get there if you are really in deep, think about what makes me very unique and you occupy this little niche. Yeah. Every victim has to be filled. That’s a physical principle. And as the market grows and changes so fast, you create much more vacuums than the people can fill them. I think there are great business opportunities. Great ones. I think coming back to the investments and economics of water industry, there’s definitely a paper. I read it on LinkedIn. It was Sudhir Murti he co authored that paper with three other authors it’s published in the inter American development bank. It’s called innovations in commercial finance for the water and sanitation sector. It’s out of your comfort zone as your technologists, but I would recommend reading it. It’s really summing up the barriers for investments in the water industry. One of the barriers is that you have a very high capital requirements and low tariffs. So there’s not really an economy in water. I mean, there’s after six years there, it’s aging for 20, 30 years challenging governance, complexity of projects, and a lack of data in payback times and things like that. But I think they do great effort because you can work on the technology side and create all these beautiful things. But if there is no economy supporting it and I’m especially looking at many of the countries that still have to build the main infrastructure while then you have a problem. So that should be there as well.

Antoine Walter:

So I put the link in the episode notes for the recorded version of that podcast. Wim at the end of all of my interview, I always concluding with the rapid fire questions. So if it’s fine with you, I propose you to switch those ones. It’s supposed to be short questions, short answers, but don’t feel yourself restricted. If you have interesting stuff to say,

Download my Latest Book - for Free!

Rapid fire questions.

Antoine Walter

My first question is what is the most exciting project you’ve been working on and why?

Wim Audenaert

Yeah, well, we have been working on a lot of them, but I would say my most exciting until now is the development of the novel a nation model, because there are so many aspects that make it very attractive to me, great partners, vision, daring, to do something that nobody thinks is possible. And then applying it and making an economic business model around that. I mean, we have great partners that are denied meta HRSD in the U S and us. We started this rolling ball so little, little by little and it grew. And I think that’s my number one, experience my personal, I mean, our team has so many cool projects.

Antoine Walter

So what’s your favorite part of your current job? And that might be difficult because if I get your bites, you tend to love everything about your job, but what’s your favorite part.

Wim Audenaert:

I have thought about it and prepare this question a bit, but I think it’s creating to call it in CFD terms, creating a bit of turbulence in the markets and also energizing people at the international scale. I think that’s really what gives me so much energy. I mean, that’s the benefit, but also working in an rather slow, if you call it that way or a conservative industry, there are so many opportunities.

Antoine Walter

What is the trend to watch out in our conservative water industry?

Wim Audenaert:

I define several trends I’ve written them down here. For example, there are many societal pressures, for example, regulation, climate change, things like that. Water use water scarcity, but then you also have technological trends. And if I can just isolate one, I think the main things to watch are not per se new disruptive treatment technologies. There will be, of course there will be, but I think it will rather be a platform. For example, VR, AI, these kinds of platforms, how they will be integrated in the water industry. I think that’s something to watch. And if you ask me big opportunity, a lack of companies that can one provide value seconds, explain the value to the markets in a clear, understandable words, use your opportunities. If you ask me to start the company,

Antoine Walter

You say AI as a platform, but is it the platform for water industry or is it spread from which is broader than the water industry? Because maybe you have some data from the energy sector data from the transportation sector, and you could leverage those and have a better take on the networks. And if you have a better take on the networks, then you have a better take on the water and so far and so on. It’s an integrated cycle, isn’t it?

Wim Audenaert:

Yeah, exactly. So indeed probably a lot of inspiration can be indeed taken from different industries where it’s already much more mature. One example is CFD. CFD is not new. We didn’t invent it as a company. It existed in the seventies, in the sixties, they use it in aerospace car engineering, not a single car is designed without CFD, not a single one, but that’s already decades, decades that people use it. So yeah, I think you can steal things from other industries. It’s extremely powerful. And that’s why reading multidisciplinary is also very valuable read different books.

Antoine Walter:

But I guess not everybody has the ability to read 20 books in parallel. So there’s a limit to some brains. You know,

Wim Audenaert:

I have to be a bit of a freak also. I think some people will think I’m crazy, but I don’t mind. I’m enjoying.

Antoine Walter:

What is the thing you care the most when you’re designing a new process or a new product or new service and what is the one you care the less?

Wim Audenaert:

I like that question very much. Actually I have written down care most. If we can identify a big pain in the markets, we can put a solution against that. So we can cure that pain, that big pain in the market. And we are the only company able to do that. That is what I care most about. If we start up new ventures or projects and care less, I think at least it’s money, I would say in a development stage. What I mean with this, of course you have to be careful that your account doesn’t dry up. But what I mean is if you are developing something disruptive or quite new, or you have a research project going on and you really believe in this idea, speed is much more important than money. And we always leave some dollars or euros on the table. I think in projects that are in the framework of development effort, we could squeeze these projects slightly more, but I don’t want to lose one more weekend and negotiate.

Download my Latest Book - for Free!

Antoine Walter:

No that you were mentioning money. I was listening to your interview in the podcast from Hakim El Fadil, fatty liver, which is very good podcast and very good interview from yourself. And you were mentioning that you didn’t have any accountants. Did that change since the podcast because you grew or are you now watching the finance as well? Yeah,

Wim Audenaert:

We have always had an accountant actually, when I think I said in that podcast is that the, I was still doing the administration. I was the one sending the invoices to customers following up the payments. This has changed. I can tell you, yeah, we have a very good colleague at Villa man that this is a revolution. I mean and that’s the biggest challenge for growing a company delegation. You start with like a very mixed thing of tasks you don’t know, even in which category they belong and suddenly you have to isolate them, delegating to one person, isolate them, have confidence in the person isolate. It’s incredible. It’s a very complex exercise, but that one was one of the best decisions delegating that.

Antoine Walter:

So that’s a good advice, I think for the day and from Peru, I want to be you, that might be listening. Yep.

Wim Audenaert:

By the way, on one, we are a modeling company. So it means we have a financial model that predicts our cashflow. Of course, I love that model because we calibrated. We have some data and then suddenly, yeah, of course the next quarters you have no predictions. And I mean, the model is really cool to predict

Antoine Walter:

That’s a good one. Do you have sources to recommend, to keep up with the water and wastewater markets? I’m going to do a bit of your advertising, cause I guess you’re going to do it as well. I’ve seen it. You’re enrolled for web connect and that you shared your recording. So I’m looking forward to presentations that we have to connect, but I stopped answering my own questions and I’ll let you answer it. Thank you.

Wim Audenaert:

I’m glad one person will look at my presentation. Really glad to hear that. And then yes, I have written down a couple of things. I think the first thing is the paper. I already mentioned coauthored by studio Murti and some other people by the inter American development bank. So on the innovations in commercial finance for the water and sanitation sector, to me it’s I love it. I will reread it. It’s quite heavy. But then I would recommend a book that every water practitioner should read. It is called the quest for pure water. The history of water purification. The first name of the person of one of the authors is Moses. But I don’t remember the last names, but you will definitely find it. It’s a blue cover. I love it because I mean, there, you see that water treatment started yeah. Millennia before Christ. It’s so cool to see the, all these development and the questions. And then, so that’s certainly a source I would recommend. And I like to read history to understand the future. I love this. Why? Because again, it’s about principles. Things happen over and over and over again. So if you read a lot of history, you become much better at predicting the future. I think because things repeat and then events or networks that are definitely interesting to follow, to keep up with everything. I think it’s WEF water environment Federation, IWE international water association. IO utilities. It’s a great platform for young companies to showcase what they are doing. Global water intelligence, blue tech. I think all these platforms are doing great efforts to make this world a better place.

Antoine Walter:

So I arrived at my very last question. If you had to recommend someone to interview in that podcast, who would it be?

Wim Audenaert:

Oh, well I have a person in mind. Yeah, but I don’t know if you will be able to catch him for this podcast. I don’t know because he might not like it. I don’t know, but he’s definitely worth interviewing it’s Charles bot. Charles bot is I’m CTO of HRSD the utility in Virginia Norfolk, I think while different sites. But I think that person, yeah, it was a big inspiration for me. That person not only has a vision, but only brings his vision and practice because you have many people with vision. You only have few with a vision making it happen. That kind of person is person like that. And I think he’s an inspiration globally for many utilities. So I would definitely recommend you to interview Charles bots and ask about all the great projects they are doing, including the Swift, which is the indirect, potable reuse project.

Wim Audenaert:

So I’m going to reach out to him and hope that he accepts that interview because you just teased a very nice content. So I’m really now I’m curious. So thanks a lot. It’s actually thank you very much Wim. I think it’s the longest record I’ve ever made and I couldn’t make it last for another hour, but really I’m sorry for that. I have to be cautious of your time at some point. So thanks a lot for having being with us. And I hope to learn a lot during your upcoming conferences at WEFTEC and in the next ones. Thank you very much. It was a pleasure talking to you.

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to Actually Surpass a Life of Uncertain Piloting with this Simple Hack appeared first on (don't) Waste Water.

Dynamita is the company that markets SUMO, a wastewater process simulator with open model source code.

What we covered:

How 40 years and 4 generations of modeling packages changed the world of process design (and what that implies for the users)

The single trick that allows to check if a few tens of million dollar process is worth the spending

How you can optimize a treatment process today, and explore future challenges

What hides beyond the “digital twin” buzzword

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

Resources:

Dynamita’s website

Send your warm regards to Imre Takacs on his LinkedIn page

is on Linkedin

Antoine Walter on LinkedIn

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to Reap Heavy Benefits out of a Sumo appeared first on (don't) Waste Water.

KANDO is a company that provides wastewater insights to cities by analyzing real-time data that is retrieved from IOT sensors located at strategic points around the network. 

What we covered:

The single greatest source of insights on a city’s population and how to leverage it

The four level of activities in the digital world – and why you should stop at #3

How fighting unawarenesses dramatically shifts the paradigm on both ends of the wastewater cycle

The three-step process KANDO uses to support the company’s ambition to change the world. 

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

Resources:

Kando’s website

Send your warm regards to Ari Goldfarb on his LinkedIn page.

is on Linkedin

Antoine Walter on LinkedIn

Infographic

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to Fight a Pandemic (With the Help of Your Feces) appeared first on (don't) Waste Water.

Aquassay is a water efficiency expert company, that optimizes both environmental and industrial performances of water uses and treatments.

What we covered:

Water is everywhere in industrial processes, yet widely invisible

Fixing the water cycle within a factory means first and foremost improving industrial performance

Solving unsuspected process challenges might be just one-valve away

The 3-step process that saves tremendous money in operating costs every year

How WOWing your customers eventually turns them into raving fans

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

Resources:

AQUASSAY’s website and the French Water Team association

Send your warm regards to Stéphane Gilbert on his LinkedIn page.

is on Linkedin

Antoine Walter on LinkedIn

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post The Astonishing Journey of a (forthcoming) French-Tech Unicorn appeared first on (don't) Waste Water.

Thomas Debruyne is Asia-Pacific Digital Manager for Veolia Water Technologies. He also holds a role of Food & Beverage Market Segment Manager, and is a former Operation Director Network Services, and General Manager Smart Water Services.

What we covered:

What if digital wasn’t a disruption at all, but simply the latest leg in a 20 year’s journey? 

While we’re at it, what exactly is 𝙙𝙞𝙜𝙞𝙩𝙖𝙡 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣? 

The 4 bricks of Veolia’s digital brand 

The road to digitization (and how it can save you big money) 

Digitization applied: what to expect – 𝙡𝙚𝙩’𝙨 𝙩𝙖𝙡𝙠 𝙣𝙪𝙢𝙗𝙚𝙧𝙨 

Cyber-Security, the elephant in the room?

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

Resources:

Send your warm regards to Thomas Debruyne on his LinkedIn page.

is on Linkedin

Antoine Walter on LinkedIn

Other Episodes

FREDsense: Revolutionizing Water Quality Monitoring with Biosensor Innovation

By Antoine Walter | 7 May 2025

Swirltex: Revolutionizing Wastewater Treatment with Bubbly Innovation

By Antoine Walter | 30 April 2025

Blue Foot Membranes: The Next Evolution in Water Filtration

By Antoine Walter | 16 April 2025

Echo River Capital: Pioneering the Future of Water Innovation

By Antoine Walter | 3 April 2025

1001 Fontaines: Engineering Sustainable Water Access Through Social Entrepreneurship

By Antoine Walter | 22 March 2025

Amazon’s Water Footprint: Tech Giant’s Quest for Water Sustainability

By Antoine Walter | 19 March 2025

Turing: The AI Revolution Quenching the Water Industry’s Thirst for Innovation

By Antoine Walter | 26 February 2025

Altillion: Pioneering Digital Intelligence for Water Asset Management

By Antoine Walter | 6 February 2025

Flocean: Pioneering Ocean-Based Water Solutions

By Antoine Walter | 15 January 2025

Botanical Water Technologies: Harvesting Liquid Gold from Plants

By Antoine Walter | 4 December 2024

The post How to Almost Double Treatment Capacity in About No Time appeared first on (don't) Waste Water.