#87 - Maxime Interbrick: Street-Level Intelligence Is Changing How We See Cities

Is world of ambient air quality monitoring is in a deadlock. Despite having targets and technology, air pollution remains a persistent urban challenge. 

Why aren't things changing? This question drives Maxime Interbrick, co-founder of Sparrow Analytics, whose company is pioneering a revolutionary approach to environmental intelligence by deploying mobile sensors on vehicle fleets.

In this conversation, Maxime reveals how mobile monitoring provides a fundamentally different perspective than traditional static sensors. While government-operated reference stations offer precise measurements at specific points, they miss the dramatic variations in pollution levels from street to street. Sparrow's approach combines mobile sensors mounted on postal vehicles and delivery fleets with AI analysis to create comprehensive pollution maps showing street-level variations in real-time.

The results are surprising – between 60-80% of city areas actually have good air quality. The problem isn't that entire cities are polluted; it's that we lack the granular data to identify the "healthy paths" through our urban environments. This insight transforms how we might approach urban navigation, especially for vulnerable populations like children with asthma or elderly residents. Rather than avoiding cities altogether, we can make informed choices about when and where to travel.

Maxime shares fascinating examples from their deployments, including discovering dangerously high pollution levels behind a school where older children were dropped off – caused by carpet dust in buses – and identifying extreme urban heat islands where temperature variations of 10-15 degrees occur within the same street. These discoveries enable practical, immediate interventions rather than waiting years for infrastructure changes.

What makes this approach particularly powerful is how the data can be integrated into platforms people already use – navigation apps, fitness trackers, health applications, and real estate services. Instead of creating another dashboard nobody checks, Sparrow envisions environmental intelligence becoming as routine as checking the weather. For cities struggling with pollution, this offers a path forward that empowers individuals while informing better urban planning.

Have you checked your neighborhood's air quality today? Perhaps it's time to start. Follow Sparrow Analytics' journey as they expand across Europe and the United States, bringing environmental int

Chapters

• 0:00 Welcome to Air Quality Matters
• 1:36 The Deadlock in Air Pollution Monitoring
• 12:51 Mobile Monitoring vs. Static Sensors
• 32:55 Technology Behind Sparrow's Sensors
• 51:37 Partnering with Fleet Operators
• 1:05:18 Data Applications for Health and Cities
• 1:19:35 Business Model and Future Expansion
• 1:35:52 Impact on Indoor Air and Urban Heat
• 1:48:30 Vision for an Environmentally Intelligent City

Transcript

simon: 0:00

Welcome back to Air Quality Matters. We already have the tools and knowledge we need to make a difference to the quality of the air we breathe in our built environment. The conversations we have and how we share what we know is the key to our success. And co-founder of Sparrow Analytics, a Swiss-based environmental data company, he established with the CEO, Elshad Hajeev, in 2021. Sparrow Analytics is pioneering a new approach to urban environmental intelligence by deploying mobile sensors on vehicle fleets to map street-level pollution and other environmental factors in real time. Maxime is a seasoned entrepreneur with decades of experience building startups across the informational media and IT sectors. Since 2019, his focus has been on solving urban environmental challenges, culminating in the foundation of Sparrow Analytics. As Chief Operating Officer, Maxime has been instrumental in the company's rapid growth, including securing a $2 million seed funding round in March of 2024 and forging key strategic partnerships with major fleet operators like Swiss Post and FedEx. He's currently leading the company's ambitious expansion into major cities across Europe and the United States.

simon: 1:34

I love the mind of genuine entrepreneurs. They see the world differently, are innate problem solvers, and the successful ones, as I've known them, find ways to see things that no one else does. I think what's interesting about Sparrow, as Mac puts, it, is the world of ambient air quality monitoring in a kind of deadlock. It's not like we don't have targets and the technology to monitor is there. So why are things not changing? Can the problem be looked at differently?

simon: 2:07

We talked about this how they deploy mobile sensors, the challenges of doing so because there are plenty and what it could mean for understanding not just the urban landscape, but more besides, including our own homes and workplaces. I think this is a really interesting conversation that dives both into the concepts of looking at a problem differently, but also the technology deployed to do so. Don't forget to check out the sponsors in the show notes and at airqualitymattersnet. This is a conversation with Maxime Interbrick. One of the things that you mentioned when we last spoke and I thought it was really interesting, and I think it's something that would be interesting to explore as an opener is this you mentioned that you believe that air pollution monitoring is in a kind of a deadlock, and perhaps expand on that for me a little bit. What did you mean by deadlock and what are you doing to solve that issue?

max: 3:18

well, first of all, I came to the industry only about five years ago, so I'm pretty much new for that industry. I'm not from air pollution, neither education-wise neither experience-wise. I just came in as an entrepreneur, as someone, the man of action, someone that's looking for making a product and sell it on the market and make profit and make everyone happy. So we reached to monitoring of air pollution with my co-founder and partner by surprise and it's also a personal story and when I arrived that you know everything is regulated, you have proper involvement of the government, you have proper setup, rules and everything is properly done, and I decided to join a lot of entities and companies and groups and forums of discussions in order to understand better. So we become part of International Telecommunication Union. Out of UN, we became part of different discussion at EU legislative part committee and we spoke and attract to our startup people from the industry that are very highly positioned, people from the industry that are very highly positioned.

max: 4:45

And after a certain period of time I realized that there is a heavy investment in focus of air pollution, but from one side and from another side we don't see any changes on the ground. You have a lot of education, plenty of discussions, a lot of money being invested, both public money and private, but there is no change and there is no real effect on people, because when you ask everyone, they don't really see any difference. They do see difference, for example, if we speak about vehicles. Yes, you have more electrical vehicles, so they feel kind of less pollutants, but again, it's very difficult to identify and quantify and overall, when you ask a certain person how they feel in the morning, how they feel during the day, both indoor and outdoor, they don't seem to have any changes. And since pollution is transparent, you know you can't really address the problem.

max: 5:48

So one of the things we investigated very deeply is the city of London and in the city of London today is, I think, the most invested and most taken care of and measured city in the world from all perspectives. You have their funds, you have their people, you have Greece, london, you have Cambridge. You have everything there in place Low emission zone, ultra low emission zone, all aspects, all initiatives taking and tried and nothing has changed, even getting worse in many cases. So from that perspective, there's a lot of goodwill, there's a lot of initiatives, but at the end of the day, me as an entrepreneur, as someone who wishes to have progress from A to B and see certain evolution of product or approach. For me it's a complete deadlock and the reason for that is because maybe everyone just sit too much and spend too much time in the office and they don't really understand what's going on. And even if they do, they set up certain static establishment and the static establishment is not effective enough in order to understand what's going on at the street level.

simon: 7:12

Yeah, it's interesting you say that I always think it's interesting from an outsider's perspective and an entrepreneur's perspective, because an entrepreneur's perspective is very unique. You're you're looking at situations in ways to unlock problems. You're trying to find the gaps, the needs, the pain points, the things that are going to generate a demand for something. So you look at something like air quality, ambient air pollution, and you see a need. You see cities and parts of Europe and parts of the world that have sustained and problematic environmental conditions yeah, there is a demand, what we call.

simon: 8:07

Or a problem, let's say right and a problem that isn't manifestly improving. It may be shifting and adjusting, improving in places, but generally speaking it's not a problem that's going anywhere fast. So it's a sustained problem. So it's a sustained problem. It's a bit like the obesity pandemic it's a problem. It's not going anywhere quickly. It's going to remain a problem for society for a long time to come. So you see a problem, but you also see a market that's quite saturated, don't you? You look in to this sector and there's no shortage of companies developing sensors to monitor air quality, and there's no, as you say, there's no shortage of company. There's no, as you say, there's no shortage of company. There's no shortage of regulations and standards. Often, you know we've got very well established WHO standards and local standards for local cities and so on, and rules and regulations and money pumping.

max: 9:17

It's not standards, they're recommendations and everything's done on behalf of UN. It's a recommendation. Even EU legislation is a recommendation. It's not real. You know, we're based originally in Switzerland and in Switzerland we have our own regulations. For example, in Switzerland it's not by the law, but it's agreed Entities from the cities authorities.

max: 9:44

They cannot push shades or use low-cost sensors, that's it. They just don't. And even if you have an amazing solution, if you have set up 1,000 sensors across the city, the city and environmental agencies in Switzerland will never collaborate with you in a way that they will use your data. They will say they will open the doors for comparison and setup and deployment of sensor on top of referencing stations, which is good, but they will never collaborate in a way that was going to offer that data. And from one side, when we started to work with low-cost sensor. I understand them. Why? Because low-cost sensors, they're really problematic and sometimes it's a philosophical question bad data or no data? So here I would take the side of no data, because bad data is misleading and can really put you in a position where you just you know, take the bicycle road, which today is one of our biggest challenges, to change that subject of the bicycle lanes in the cities Because they placed and I see in the United States it's just completely out of logic why they placed along the roads. So, coming back to what you just said, we were absolutely lucky to start our activity and my activity within the last four years, five years, because all the companies that existed on the market with StaticSense for like 10 years and more, they always were in the frame of working with the government, always industrial partners. They never had an opportunity to address their solution to the private market or to commercial market, which is like B2B or B2B2C. And we were absolutely lucky to be in the right time when the platforms and AI and everything related to digitalization of our lives came in a point where checking the weather on your app, having Aura Ring, apple House Whoop, all the bracelets, all the wearables, all the connected lives are very well established.

max: 11:59

So, from our perspective, the demand is not within the cities, not within the governments, but within the private sector. Once the demand becomes within the private sector like obesity, like sport or like fashion or like everything that we see coming into the commercial interest that becomes trendy, interesting and useful. In some cases it's overused, like I don't know. There are too many people doing fitness. Fitness clubs are completely full and sometimes the air quality there it's not great, but you know it's better than having obese people.

max: 12:37

Same with air pollution. We're trying to make it cool and trendy and important to check air pollution in the morning, like you do with weather, and maybe not only once, twice a day, three times a day, not only for yourself but for your elderly parents, which expose for high temperature and can be really deadly for them, but not only for them, also for your kids, you know if, if they have asthma or they have some sort of other problems, just don't walk to the school on that particular road. Choose an alternative one. So we see a lot of demand from that side and it's a gap from us. From our perspective, it's a gap For sure. We are very mission-driven. My older son has allergies, so I have a personal story about that. We do want to save world, but we also would like to make commercial opportunities in the private sector which are main driven and main motivation for everyone to use and integrate and cross-integrate our information about air pollution.

simon: 13:47

And do you think that the private sector is a way of unlocking some of this deadlock then? Is that gap, that opportunity that you've seen beyond just the able-to-pay part of the market, like who's likely to want to pay for certain services, but the? Is this a? Is this a facet of society that the b2b and b2b to b2b to c market that you think hasn't been unlocked from a? It from a ambient air quality perspective? Is that effectively where you that there's this systemic and technological deadlock, that we've got sensors, we've got guidance, we've got cities with problems and places with problems and we've not seemed to be able to move the needle. Is your hope that this, this sector, is a way or another way of trying to move that needle effectively?

max: 14:47

the ideal situation would be cross-collaboration across everyone and everyone, because the regulation must take place and we're very much aligned with regulation and we like to collaborate with entities like AQSpec, for example, or environmental agencies. When we work in each city, we do calibration process and show our data openly and make sure that everyone is really, really aligned with what we do. But we shouldn't trust the government and authorities just to help our lives, because if the government again decided to place bicyclists along railroads, it doesn't mean that we have to use them just because it's a fashion or trendy. But it's better to look at our application where it says don't go there, you should take alternative goods. So the collaboration and regulation part is very important from authorities, but the cross-integration of data within existing sectors, such as real estate, insurance, ventilation, industry, filtering both vehicles and homes that parts variables, of course, longevity.

max: 16:09

There is plenty of areas today where the air pollution data not raw data but processed and analyzed can be cross-integrated. Not with the new application, not with the new map, because when you show to the people AQR map or Purple Air map or any other map, this is just a beautiful map. There is no insights, no action, nothing. But if you use the Aura Ring application or Apple Health application and instead of suggesting to do 10,000 steps, it would say you should do 10,000 steps, but in that direction, because then you will be less exposed. Those type of products, I think, should be integrated. It is much easier to use the air pollution data Afterwards. People will be just educated in ways that they will reduce their personal exposure. It will work on the personal level and we're going to the real change.

simon: 17:12

From my perspective, I will have introduced you at some level in the introduction, uh, before we speak today, uh, max. But perhaps to set the scene, to expand a little bit on how you see this deadlock breaking, perhaps explain to listeners a little bit about what your approach is, what you're doing and of interest to me particularly, is this kind of partnership model that you're talking about and things like Swiss Post I think it was was a good example of that kind of public-private partnership. So maybe let's just start there and explain a little bit about what this solution of yours looks like, what it's called and how you're kind of working with this partnership model to deliver it yeah.

max: 18:08

So four years ago, me and my partner, we set up a startup called sparrow analytics. We we were searching for the name quite extensively but at the end of the day we decided that it will be that real bird and we did have some already. Other companies took that name, but for us it was important because we knew we will be the real sparrow, because it will be not just single bird, it will be flocks of sparrows and packs of many, many different birds. It will just. You cannot imagine today's city without sparrows. Every city has their own birds, it doesn't matter where you go. So for us that name was very important and also the mission was very important.

max: 18:46

But me and my partner, we came again for that industry not from the industry. We came absolutely from a completely different industry and we initially worked with taxi fleets. So for us public transport was the main carrier and for us it was absolutely obvious that having a device that's moving in the city is better than its static, especially when we analyze and proceed a few of the tests and realize that when they're static the effective range is very short and we just cannot, we're blind. But when we put the device or certain sensors on top of moving vehicles in the city. Then it becomes very, very effective and the data becomes very interesting because we're just reaching out places where the static sensors simply cannot reach because they don't have that effective range. So we decided to move into the mobile version of the measurements. We didn't have this backlog when we first started static and then we needed to move to the mobile because it's a completely different version of the monitoring. So we started immediately from the mobile, which is the most complicated and difficult. This is how we learn along the road.

max: 20:07

But we were absolutely lucky to attract amazing people into our team, both from the sensor part and from air pollution processing. They're absolutely top-tier people as an advisor, as helpers. We have an amazing engineer and technical part and we could really do the main aspect. But the main thing which helped us a lot was actual driving experience on the fields. Because once you're in the laboratory and you make setups of all the sensors and you make sure that they all work properly in the laboratory they work perfectly. But the moment you step out of the laboratory you start to face this huge amount of things which are affecting your sensors, like humidity, like ozone, like temperature, like a lot of things. You know urban setup, the streets, everything is so complicated and the sensors are so unreliable and imprecise that at a certain point of time when we developed the company, me and my partner we were sitting and thinking should we continue doing it, because it's really complicated? Maybe we should just stop it and just leave it and do some other business.

simon: 21:29

As you said, bad data or no data.

max: 21:32

Absolutely, absolutely. Because we couldn't you know, I couldn't look at the mirror and deliver the data that come today from microsensors if you don't do proper steps. But because we had a great team and because we had great advisors. We had this internal discussion in 2023, and we decided to make certain changes, both in hardware side, in methodology, in algorithm. We built our own laboratory instead of using external laboratories, and it took us an entire year to drive and to bring the device in a level which is today the best microsensor performer in the world from the mobile perspective 100%, and I would really be not surprised if from the static perspective as well, because every mobile device can be static. So our first goal was to make sure that our technology delivers trusted data, not only trusted from the citizen point of view, but also trusted from the authorities point of view, the government and the business point of view, because in the very early stages, when we had a call discussion with Google, like Environmental Insight, explorer, with BrisoMatter, before they were bought, they all emphasize, in effect, that they want to work with that data. But they need two things First, they need to trust that the data is good, and this trust should come from authorities and third parties, it couldn't be just our reports. And second, that we will have coverage, meaning that we will have not one or five or ten, but 100 cities, 50, 100 cities, which makes a lot of sense because, business-wise, you know you need volume and you need a good product. And that's the challenges that we took during 23 and 24. We built the best device in life today. We measure extremely wide range of different parameters and, by the way, it's not only air pollution. We also measure noise and road quality traditionally because we're moving. So it makes sense to link between road quality, bumps and potholes and air pollution. But the second thing, what we did, is basically decided to expand into the cities with public transport in a way that it will be reliable from the data sets that we're getting. That we're getting Because, if we speak about transport and public transport in general, first of all, private vehicles are not good for that because they don't drive enough.

max: 24:11

Second, public transport also brings different types of data. If you mount the device on top of garbage trucks or taxi or postal cars, they have different patterns of movement in the city and they bring different sets of data. And we tried all of them. We went, we've been in Belgium. We did a great pilot over there. It was the city and we use, I think, all type of vehicles, from electrical garbage truck, street sweepers, just delivery vehicles to heavy machines, and they all have very interesting data sets. But the real and at least used is the postal vehicles that we decided to work with because they cover large areas, they go in same addresses twice a day, every day. Yes, they don't work during weekends and during nights, but at least during the day. What we will discover as hotspots and as problems we can not just scan but also monitor and create monitoring approach. So we have today that approach which we do scanning and monitoring and we decided that postal vehicles would be the best and we went again to tackle.

max: 25:28

One of the biggest challenges is to work with the Swiss Post and for those who is not familiar with that company, with that corporation, it's a very, very honorable, very, I would say, old school and conservative corporation that looks for innovations, but within the range of the scope of work. When we approached them saying we're going to place on top of the postal cars our devices and going to measure air pollution, they said, oh my God, it's impossible going to measure air pollution. They said, oh my God, it's impossible. So we said, all right, what can we do in order to make that possible? So they gave us a list and took us six months to prepare. We did a crash test, we did what they call lending of the data, because our data went in big data servers in US, so we made it in Germany. We even work on an anonymous approach for each driver by geohashing the data. So there is no kind of controversial or any problem with the GDPR, et cetera.

max: 26:43

After six months we were capable to provide for Swiss Post enough evidence that by placing the device we can make it very easy. It's very, it's reliable, there is no risk for having the device on top, but also also there is a market for the data that we're going to sell and share revenue with the fleets. So we did that work extensively and, after they were convinced, we signed the contract in the beginning of 2024. And we equipped 50 vehicles which were covering 647 postal codes in five top cities, addressing air pollution data to 2.1 million citizens in the city. We equipped them within less than three weeks, so it's a plug-and-forget approach. Once we placed the devices, we didn't have any maintenance or any needs to see the devices we didn't have any maintenance or any needs to see the vehicles.

max: 27:48

Of course we had some sort of small issues, but they all had because of the fleet, because of the vehicles.

max: 27:54

It wasn't our story, but Zetri took care of it very quickly and we started to drive and we started to have not one vehicle, two vehicles or four vehicles like we had before.

max: 28:05

We had a 50 vehicles fleet and we started to have not one vehicle, two vehicles or four vehicles like we had before.

max: 28:06

We had a 50 vehicles fleet and we were capable to have that heavy amount of data which we call today big data, and it's AI and machine learning approach which brought us in the situation where it doesn't matter if you don't drive at night or you don't drive weekends, but at least during the week. The information that we gather is very much different from what is actual information provided by the regular authorities and they report so that they you know issue yearly and they report so that they you know issue yearly. And that difference brought us in this position where we have, from one side, a very regulated device, that by authorities, we have a great partner for the fleet and for the deployment of the city and we have a data which is a golden data, because this data can be used both historically, in real time, and it can be used not just by authorities or like AQR platforms, but it can be used at the highest point of the commercial businesses, such as Google, swiss Re or any other, or Apple Apple Health, if you wish.

simon: 29:26

Yeah.

max: 29:28

So at this point, yeah.

simon: 29:29

The thing that springs to mind, max, when you were talking is, I'm guessing the kind of the light bulb moment for you was making air quality monitoring mobile, and for me, the image that keeps popping into my head is what happens with google maps.

simon: 29:47

These days, when you're driving, the traffic data that you're seeing is a culmination of not only traffic cameras but also the live gps data that's coming off every google device that's driving around in a car at any one moment, and these days, as most of us driving around, you'll also get a notification on your phone to say is there still a broken down vehicle on the hard shoulder as you pass it, and it's you, you're providing it contextual information.

simon: 30:12

So everybody kind of gets this. I think there's this mental image that that sticks with me when you're talking that, rather than having these, as you say, fixed nodes in nodes in an environment which may or may not be in a good position and often not in a very good position, you now have this web of vehicles constantly moving around a city, basically capturing the ambient air quality and ambient environmental conditions in that city constantly, and you build up a picture. So you get this. You get much better coverage, but you're also getting a time orientated um picture of how that city pollution and environmental conditions are changing throughout. So I imagine it's a much more interesting picture from a data set perspective that's absolutely right.

max: 31:08

It's a. It's a completely different picture. It's very similar to the hospital analogy. When you come to the hospital, you don't do treatment immediately. You first do diagnosis and you know kind of I'm trying to understand what the problem is. Then you set up the treatment In the same way.

max: 31:27

We do not against static sensors. We do not against existing platforms. They just need to be rewised and reinstalled according to our heat map, because it makes a lot of sense to put static sensor in a place where you have pollution, instead of just compromise on areas. And you know placement of static sensor, it's always compromise. You never have like 100% the place where you want to put the device. It's always electricity, internet, approval from the owner of the place. There is always a compromise and sometimes this compromise can be one meter higher, two meter left or right, and it's already creating a lot of difference.

max: 32:10

I can give you an example. We had a collaboration with one static sensor in the United States, in the city of Burlington. We equipped a school bus with kids that were picking up the yellow one. We actually used the yellow device from the Swiss post so it matched the color. The first week we're starting to drive, the bus arrives. We follow the bus very interesting pattern. He picks the kids it's very similar to postal car, if you wish Picks the kids, brings them to the school. So the device of the partner static device is placed just in front of the entrance of the school where the bus is dropping the kids. And once we're there we compare our data with the static sensor and we have very similar numbers. It's really aligned like 95%. But after one week I realized that bus continues, goes behind the school, just behind the corner, stop there and then leave the place, and in that place PM10 was higher than 300, while here it was 18 or normal.

max: 33:24

So I'm asking you know who is responsible? What's going on? And they said look, there is two groups of kids young kids and older kids. Young kids are dropping off at the first place and the older kids drop behind the school. And I said what's going on over there? Can you send me some picture? So they sent me the pictures of the parking lot and I see that there is a surface of asphalt which has not been properly made, et cetera. But I asked them can you send me the pictures of internal bus? And after I saw the picture I said listen, you have a carpet over there. I think kids are jumping on the carpet and creating this cloud of dust. Can you just clean the carpet and let's measure. They clean the carpet. It drops into 60 to 70% in the pollution. They just clean the carpet, all right, so it's a preventive.

simon: 34:19

It's an actionable.

max: 34:21

Yes, on a bus itself. Of course it's remained higher because some sort of parking issues, but you know so is that, is that one of the challenges of of mobile monitoring is is noise, uh, data noise, you know you know it's a challenge. It's this is what we want to see. Yes, yeah, we want to see traffic pollution yeah but I guess there's also confounding factors.

simon: 34:44

So, for example, you have to be wary of location of the exhaust from the vehicle and placement of sensors, for example. So you know there's no point having a sensor that all it's doing is picking up the pollution that the vehicle itself is generating. You're picking up ambient air pollution plus a large part of whatever the vehicle is creating. So I guess there must be a. There's a trade-off somewhere where you're trying to figure out how you is the goal to measure ambient air or to measure the pollutants the truck. It's a bit like putting a CO2 sensor on a desk in front of somebody. Am I just measuring my breath?

max: 35:23

or am I measuring the CO2 in a, a desk in front of somebody, or am I just measuring my breath, or am I measuring the CO2 in a room?

simon: 35:27

You?

max: 35:28

know, but that's the problem with indoor and outdoor. You know, simon, when you speak about ambient indoor it's a controlled environment you can understand and quantify amount of air you have inside of the apartment or office. That's easy to measure and that's easy to calculate when you go outside. There is no such thing as ambient, because the technology of the sensors today we're capable to measure only what I see when I'm on the vehicle, what I see on the right, on the left, from behind and in front. That's it. I cannot see more than that.

simon: 36:08

So we're trying right now, but you're not going to put a sensor by the exhaust tailpipe, are you, of course?

max: 36:15

not. It doesn't make any sense because people are not there.

simon: 36:19

There is a placement issue. So if you've got buses, for example, with exhausts at the front of the vehicle that come up the front like a truck, and you've got a sensor on the roof, you're going to be picking up exhaust fumes as the vehicle's driving, as opposed to an exhaust tailpipe that's at the back. Um, you're going to get a different exhaust pattern from the vehicle when it's stationary and in traffic compared to when it's moving through the air. There must be significant noise, I guess, in the data set that over with the ai and over time the algorithms have to be able to sort that out in some way, I guess uh, well, what you just described called TRAP traffic-related air pollution Today TRAP is not being measured properly, just assumed right by a number of vehicles, by certain shared these polluted vehicles, polluting vehicles.

max: 37:14

The problem is with the traffic is because when you measure particular street in particular city, that pollution is correspond only for that street in most of the cases. Very rarely you can find pollution from that street on other parts of the city. So that particular information is important for the people who live on that street on other parts of the city. So that particular information is important for the people who live on that street, walk on that street or I don't know, stand on the bus station or whatever, okay, or riding the bicycle, for example. That relevance. So that's what we're trying to provide at this point. And again, back at the time we would take university and make some PhD study about this whole story, which we have a lot of them. But today, thanks to AI, we can work out this predictive approach and learn by the example how things are behaving. By the example how things are behaving, because we cannot use the amount of uncontrolled air, because you have so much air surrounding you and so many unpredictable conditions, like winds, for example, wind is a very important factor in air pollution, but because you don't have a lot of wind stations and again, it's just an assumption the wind speed, it's very difficult to identify how in New York, for example, in the street canyoning, it's just an assumption. The wind speed, it's very difficult to identify how in New York, for example, in the street canyoning, it will work. Sometimes it can clean the city, sometimes it can bring other things from other areas, like Sahara Sand, for example, what we see in Switzerland a lot, in Europe a lot.

max: 38:59

So what we're trying to do is, first of all, we're trying to step outside of current approach with AQI. We think that air quality index it was good, but thank you, goodbye. Now we defined basically three main parts. First is gases, which is traffic-related air pollution. Second is dust, where the PM is important and thanks to our great partner, we're also capable to measure below one micron, which is very important for how much air you actually inhale. And third is microclimate, which is temperature, humidity, atmospheric pressure that we can add into gases temperature all together in order to create profile by demand. Because if someone asks me can I drive here with the bicycle, I will use certain parameters into combination of that particular answer to him. I will never say yes or no. I will always say between 9 am and I don't know 12. It's not good for you to be there, but afternoon it's perfect or something like that.

simon: 40:09

It's always by hour so what's the what's the tech, what's the hard? What are you actually measuring? So the devices that are going on vehicles that they're measuring, as you said, particulate matter, you're measuring some traffic pollution. So is it nitrogen dioxide, that kind of thing?

max: 40:25

Yeah, we have sets of sensors which, by the way, this whole story is under patent right now, because we indeed come up with something amazing. We measure NO, NO2, ozone 3. We measure CO, which is very important, being completely forgotten, but it's a very, very important gas. We measure also CO2, but not from the climate perspective, but because we see a lot of interesting correlation between presence of CO2 in tunnels under the bridges with CO and with NO2. It's a very interesting correlation, how it works all together and how it's delivered to the city. Then we measure. We also have VOC sensor, just in case we want to train it for smell of gutter, beach strikes or something like that. We have PM sensor, which is PM. We have bins between 0.3 to 0.5, 0.5 to 1, 1 to 2.5, 2.5 to 5, and 5 to 10.

max: 41:24

Thanks to our amazing partner, which supports us and helps us from that perspective. And this device is very amazing, especially at the mobile. From our perspective, it's the best device today existing on the market and also it provides us a lot of options to not just measure the size of the particle but also see the quantification at the particular size, so we can distinguish between a train station or construction in the city or roadworks or vehicles, and once we have that huge amount of data, adding to that data additional third-party information, You're speciating the particular matter in some way, or you're just because of the nature of the particle sizing, you're able to speciate it in some way, like so you're able to look, see the difference as you drive through a construction zone for construction dust versus a zone that might have a dumpster fire or something happening or heavy traffic yes, absolutely you can see the difference in the particle, and is that just down to size, or is there some other thing you're picking up in the it's actually.

max: 42:38

It's actually the not.

max: 42:40

It's a number of particles with, in particular, range within a particular size In a simple word in train station you can see 0.5 higher in numbers than PM10, basically, in roadworks it's different and in construction it's different. So from the citizens that live in the city they don't care For them it's really not important. But for our processing analytics and integrating a proper solution through the existing platforms, it's very important because if we want to talk to the companies of wearable devices, then we will very much so understand how much particles you actually inhale when you exercise or when you run or when you I don't know cycle across the city.

simon: 43:33

And so I probably interrupted as you were going through the list there. So we've effectively got CO2, co, no2, particulate matter. No, are you looking at ozone as well in?

max: 43:49

what you're doing. Yes, absolutely. If you don't have ozone in your sensor, like one of our advisors says, you don't have your measurements properly done. And ozone is very important, independently and together with both PM and gases.

max: 44:10

We notice actually that some trees emit ozone and in Central Park in New York basically we saw a lot of ozone, high levels, which is ground ozone and very toxic for people who are just walking around. I would say that it's a controversial story, but you know it exists. I think we need some time to investigate and see how it actually affects, but today we know that only when you have high traffic, which is high NO2, o3 is dropping. So basically you need pollution to reduce the level of ozone.

simon: 44:50

So it's indeed controversial. It's reacting. I mean, that's the trouble.

simon: 44:54

Ozone is dropping because there's chemistry going on and you may not like what you see. I mean particularly ozone. Reacting with the trees is a classic one. You know. I can't remember what it is, but it's the piny and I think in a lot of trees, for example, that's where you get that blue mist, you know you get. You get the pm, the visible mist from the trees, you know. As a result so and I know there are certain trees that give off quite a lot of pm in in certain circumstances.

max: 45:19

So what is important to sorry, you go ahead, no, no, go on, not finished. What is important to say is that and this is what we realize in Sparrow, and kind of a message that I would like to make is we cannot change cities today and we will do them, make them better, but at the same time, there are so many places in the city which are completely clean or better than existing. We just don't know about them. And when we drive through the city we identify between 60, sometimes even 80% of the city is just perfect, you know. Perfect from you know perspective of current city's standards. So instead of trying to fight cities or regulations or going on different passes where you will get into the conflict, we can individually offer to every city ability to take informative decisions based on the data. Because if you take your kids to school or you take the bicycle, still you want to walk and not using public transport or your vehicle, it's safe to do. You just have alternative routes. You just have healthy paths, what we call Because every city has that, and the larger the city is, more areas and more places where we can redirect. So in some cases, in London for example, they made this low emission zone. Low emission zone is, you know, area where you don't have any cars. So it's easy to define low emission zone, which is you place sensor over there and everyone is happy. But the problem is that you don't remove vehicles, you just redirect them into the different areas, and then I would really like to measure the places where these vehicles have been redirected, because you're not literally decreasing the number of vehicles in the city. So what we're trying to do is define the entire city like a zone in which you have good places, medium and bad places, and if people are sensitive and it's important for them and they want to reduce their personal exposure on a daily basis, they will use alternative routes and the city will be changed. Actually, the Google Maps that you just mentioned for the traffic can also show that it's not only time that you just mentioned for the traffic can also show that it's not only time that you will spend in the traffic, but also amount of pollution that your vehicle will absorb, because filters are not good enough to filter everything and many people just open windows or sometimes they open ventilation to let air in, and this air is polluted. So some people which are sensitive and, with their parents or kids or family. They will just avoid going there. So what we will do is we will redirect people into those places and we will decrease the traffic and we will decrease the presence of people in those areas and overall, you will have less cases of the health issues. Because this is what we are trying to do we're trying to make people healthier in the city just by adjusting their behavior.

max: 48:38

This exactly goes into the story of my son that when he got allergic to wheat we went to the doctor. The first question he asked where do you live? We showed him the address. He opened the Meteoswiss map with all the pollen. We said you will live in between the fields of wheat. You have to move. We said we can't move. He said all right, then you have to do that type of prevention.

max: 49:05

And we used that type of prevention, reduced by 50% different. You know, usage of pharmaceutical. It's just prevention. Prevention works sometimes much better than actual treatment. So it goes exactly the same for air pollution, and this is our mission. Our mission is to bring the city in a level where every street will be scanned enough in order to provide enough information for making informative decisions. That goes to the beginning of our discussion when I said we would like people intuitively wake up in the morning and check not just weather and news but also air pollution at their particular street, at their particular neighborhood, and then maybe into the city, because city sometimes is not really important for your life to round off the hardware question because, um, I'm always fascinated by that part as well, and I did pick up on something that you said.

simon: 50:04

You were also talking about capturing other environmental factors, not just air quality. So you were measuring things like noise as well and trying to work out I guess you can pick up the signature of potholes and bad surfaces and good surfaces and so on. So you're capturing some contextual information with audio. Are you doing video as well? Are you capturing images of the environment that the sensors are in at any particular time? Is that something you're doing?

max: 50:39

So video is a very complicated story. We couldn't do it in Europe with GDPR and all these things, but right now we're shifting to the United States and here we're going to add but not just the camera, we're going to add infrared camera and also LIDAR, because we aim to scan facets of the buildings and identify leaks of heat or other leaks which can help city to lose less energy, because leaks are it's a pure loss of energy. Whether you're hitting a home or trying to cool it down, it's always great to identify not just leaks but also, because we have repeated kind of movements, we're capable to identify evolution of those leaks and predict when it will become critical. So spend less money on reparation right now, not just at the single house, but in the entire urban inventory of the city, if you wish. And again, today with AI, it's relatively a very simple job to do to this evolution. So, yes, we aim to add cameras. We also aim to make a pro version of the device where we're going to identify presence of chromium, identify hops, which is hazard, air pollution, also benzene and ethyl and H2S, so2, wide range of things. The reason is because we have a lot of demand for the model devices, because no one is really getting enough data and enough important analytics from the static sensors. That's the simple reason. But we're going to keep the device as is for the current setup and we're going to make also the version for the scooters and bicycles bicycles and also maybe even personal, to add into the backpack if you go to the fitness club or you run, in order to make personal assessment.

max: 52:36

We see some personal devices today, but we don't see that they perform at the same level of our hardware. At the same time, matching with our data from the mobile devices and integrating the personal data would make a lot of sense, because then it's not only your breath but also how the pollution affects in the places where you're present at the time. Yes, we also measure, besides air pollution, additional stuff, and the you know city is like considered a live animal. Yes, we also measure, besides air pollution, additional stuff, and the city is considered a live animal and organism in which everything is interconnected. So noise is a very important part. We still don't see a lot of market cases because everyone says it's important for them but no one really wants to do anything about it from the market perspective because it's very difficult to identify the market platform for it. I mean you have yoga classes where you can find some silence, or you have headphones or Dyson try to do some sort of things, but there is still not really the market for it.

max: 53:43

I think ordering and sleep story will be more open for that part, because you know, if you spend a day in a very noisy area, definitely it will be affecting your sleep. You don't have to be a doctor for it, it's a personal experience. But yes, we measure noise. We have five microphones we're trying Right now. We just I mean we had the experience where we try to integrate second level microphones like more expensive, more as it can really identify the different channels and the talking and and make some ai. It's surely there is not enough great tech and it's really complicated to do that in a way so breaching the security, for example, of people who are sitting and talking in the car. Simply we can record the conversation. It happens. We just remove that technology. It was very controversial.

simon: 54:39

And I guess that's one of the challenges. I guess is where you're at the frontier of this tech. In this particular situation, one of the challenges must be validating the efficacy of that approach. So it's one thing to be sure yourself that what you're getting is good data, but at some point that needs to be validated against third parties in some way so that you can say, look, this is comparatively. In some way so that you can say, look, this is comparatively.

simon: 55:06

Because you said at the very beginning of this talk that we're seeing very different data to what you're seeing from the static sensors. Well, does that mean yours is wrong or theirs is wrong or they're just different? And how do you determine that? And what do you test yours against? I mean, all of that must be something that's a big factor here. You know, because I know for a fact air quality sensors at velocity perform very different to static air sensors. So you know even things like optical particle reading. You've got to be very careful about how that sensor is placed and how it picks up those particles If it's moving through the air at 50 kilometers an hour on a road that could present a technical challenge.

simon: 55:52

So I imagine that's been a really big piece of this jigsaw puzzle is getting confidence in the data that you're collecting.

max: 56:02

Yes, absolutely. That was our largest challenge, like I said, in 2023, when we realized that our existing device back at the time was working in a way almost like static. We were sampling air but because we drove so much, we realized we have to change our approach from sampling air into measuring on airflow. And when we from sampling air into measuring on airflow and when we just let air into device and let it just pass, just control the air, measure what is in there and also increase the resolution of the measurements from 1 minute 60 seconds into 1 second resolution for gases and 5 to 10 seconds for PMs. That change makes us, shifts us into the direction where we find the proper settlement inside of the device which makes the microsensor work almost like a referencing station.

max: 57:01

That's why, when we spoke with an environmental agency in Geneva and we sent them raw data, they said it's just impossible to get that level of data from the sensor. So we invite them to the office and they came, the entire team of environmental agency like aq spec, but in geneva very they very professional. They worked like 20, 30 years. We did full presentation. We showed them, we opened the device. We show how it works when we explain them exactly what we patent today about airflow, and they all agree and they all said we never saw that before with micro sensor. And they all agree that it's a very good way and they gave us mandate, if you wish, from from that perspective yeah, I'm intrigued in how you validate that.

simon: 57:44

I mean, is it? Is it at a level where, effectively, you have reference, static, reference level, static air quality sensors and you literally drive past it with your mobile sensor and you check, at the point that you measured it, where it's in proximity to the reference grade, that you've got a similar number? Um, or are you arguing that they're always going to be fundamentally different numbers?

simon: 58:12

a non-static measurement is always going to be different to a moving measurement so like how do I know, at a very basic level, that the numbers I see from the Sparrow devices are reflective of the ambient air at that point when you drove past it or through it?

max: 58:32

should I say so, first of all, my last meeting with AQSpec in Los Angeles. They showed me that they're building a mobile platform, so we very soon will have capacity just to put our devices inside of the mobile platform and validate them across day setup, and it would be just enough. We're done.

max: 58:51

But, until then we come up with what we call collocation comparison report by Sparrow, which is very similar to collocation comparison approaches, which done with regular static sensor, but have our own sense, which you just described. Very similar. So we choose two stations in proximity in one city. We put two devices on each station and we first do comparison between the devices to make sure that we're at 99, almost 100% of the performance between them. Then we compare each to the station and after a certain period of time we let mobile device drive between them and when it gets into the radius effective range of the referencing station we compare, but not with the referencing station. We compare with our own devices, because referencing station is a completely different animal. It takes time and kind of alignment until our device is microsensor and the station can be compared. That's how algorithms work. But then when device is mobile, it's very simple to work with our own device. Like works, like an adapter, if you wish, for the referencing station.

max: 1:00:09

So, it creates a very trusted approach. We call it Sparrow Flock because they work in flock more than one or two, so they work in collaboration between each other. We did those types of comparison in Switzerland and we got amazing results, like almost one-to-one from our device perspective. So if you trust that our devices are the same and that's why we built our own laboratory, we do our own calibration all devices pre-calibrated exactly the same from our laboratory, unlike referencing stations, by the way you can trust that they all perform exactly the same. So we cross-validate it with the authorities, with the third parties. We just give them the device and we send raw data. We do not process any information as is. So we collect it and we send it over and in many cases we were getting a lot of interesting emails and surprisingly interesting emails from these environmental agencies in Germany, in Israel, in Switzerland, where we did those tests that they were very, very surprised by the level and quality of the data. So for us it's a good sign.

max: 1:01:26

In September we're going to provide our devices to Acrespec. In order to Actually the reason for September, they said that during the summer they don't feel any NO2. There is almost no presence of NO2. So we need to start in September. In September we're going to provide our devices over there. Did this validation once and then I think it will be just a yearly confirmation of what we do.

simon: 1:01:53

Really fascinating, really fascinating. While I have you, I just want to briefly talk to you about UltraProtect, a partner of this podcast. Look, they're not here by accident. Like the podcast, they are passionate about driving changes in our indoor environment and are an all-round great company to deal with. They have years of experience in the industry and a team of people I have leaned on on many an occasion for advice and insight. From continuously tracking air quality to specific sampling, they analyze and provide actionable insights for the built environment. Specializing in dust management, they provide amazing products and services that minimize risk and improve environments, from construction sites to offices, to manufacturing settings, through to solutions around ventilation aimed at improving the environment in the long term. It's a company well worth checking out. There are links in the show notes and on Air Quality Matters sites and, of course, at Ultra Protect UK.

simon: 1:02:58

Now back to the podcast your vision to create a kind of ideal city from an environmental intelligence standpoint. Can you paint a picture of what that city looks like? So a city that's doing this right. What does that look like in your mind's eye?

max: 1:03:23

I will start answering or diving into that subject by a simple example, very painful examples, that we saw in New York. We work with Esri Maps as a great GIS supporter. We got access to the third-party information. In New York we find a map where there is statistics about hospitalization of the kids under age 17 due to the respiratory crisis, like asthma etc. And it was covering partly Harlem, partly Manhattan, and when we cross-correlate with our data, it was very obvious when you have more pollution, you have more hospitalization. Where you have less pollution, you have less hospitalizations. But when you check where the emergency room is located, you understand that it's located in the place where you have less pollution and less hospitalization. So the kids should have to drive with the ambulance from that area and sometimes it's a matter of minutes and in New York with the traffic it's a very painful story.

max: 1:04:26

So what I'm trying to say is that ideal city for us is where people get access to the information in any possible way. That's the reason we would like to work through any possible channel and also real-time data we provide free of charge. It's like weather. We will offer our information address-based. You open whatever app, you will always have that info on the screen Whenever you leave and open your eyes or expose into the city different channels of communication to the city, different channels of communication. But we would like people again taking in consideration the environmental aspect in the city. When do you do everything Sport? You know everything that I already mentioned and even more. Of course, I would really love for the city authorities to remove that emergency room or maybe build another one just in the place where you have more pollution.

max: 1:05:30

But this is what we call data-driven decision. In order to do that, it's not easy. We need to collect enough information for at least, I would say, two years in order to make proper assessment and proper settlement, in order to make proper assessment and proper settlement. But today it can be done much easier and faster with AI. We see fascinating things which we can turn every Sparrow node, sparrow device into AI machine learning on the place and already delivering decision-making analytics. It's our next development. But today, the ideal city is where people have access to our information through as many channels as possible in ways that will help them to change their life accordingly. Very similar to sports activity, very similar to having MRI in the hospital when you come to the hospital, you have X-ray this is the static sensor we should consider and you have us MRI. So of course you can detect a lot of things, but you can detect them early. You can detect them and you can come up with the proper treatment and then rehabilitation period. So before, during and after um so, as a business, then what?

simon: 1:06:50

what's your product? Uh, is it the data, is it the hardware or is it a mix of both? When, when? When it comes to the commercialization of this, because you just mentioned that you know the idea is is that data would be free to access. You know, somebody pays for it at some point, real time so what? Is the product is, is it, is it? Is it data, because data is the new gold really or is it? Is it the hardware?

max: 1:07:17

data is a new gold, that's for sure. Uh, we currently, first of all, we're going to offer only real-time data free of charge. So historical and analytics everything will be done at the subscription approach, which is we would like to have a very tiny portion of the subscriptions for different platforms In the simple way of Strava charge. I don Strava charge $10 or €12, whatever they charge, we can have like 10 cents out of this subscription. It would be just enough for us to offer them information that will help them to build the proper trajectory in the city when they exercise in Strava. So our goal, an ideal situation, is data as a service.

max: 1:08:07

We would like to offer our data on marketplaces, on a lot of today maps, because it's not only Google Maps, you have a lot of other maps. Maps works very well with us. In some cases we're going to offer devices if the city proactively would like to engage with us. We have some cities in Germany, for example, that are working with us from the device perspective as well. But I can tell you that once we set up the first setup with the devices, the amount of analytics we provide, then we monetize only through analytics. We do not put new devices just because we have enough data.

max: 1:08:46

So, yes, data is a new goal and, yes, we would like to work as a data company and we will take care of the fleets, take care of devices, take care of the data and all these things which are behind the scene. We would like to remain this kind of aggregator and harvest of the data. We don't want to go into the platform. Our goal is not to build our own application, our own thing, because it doesn't make any sense. You have a lot of existing platforms.

simon: 1:09:19

Yeah, not another platform syndrome. Nobody needs another dashboard. Um, it's the, it's the. You want to be the intermediary between the hardware and the, the data to service?

max: 1:09:32

yeah, we would like to continue to make sure that the data is eligible, properly done and properly set and because we're going to work across every city in the world. So we we definitely cannot expect the Swiss approach, but we have right now capacity to send our device in Africa and have proper data without any problem because our device are again pre-calibrated by us. We know how to do and most likely we're going to open new laboratories and new places of production, depending on the demand, because it doesn't make any sense to produce everything in Switzerland, especially that we're shifting to the US right now. But we're going to maintain the quality of the data and the quality of the delivery and the presence, because we need to identify how many units we need per city. So with minimum units we will get proper coverage. You don't have a coverage? We'll make sure we fulfill the gaps.

max: 1:10:34

So it's all AI and machine learning today. It's very, very helpful For us. It's just tools. They work, they're great. It's like chat GPT magic. But imagine plugging chat GPpt 5 into sparrows directly. How much interesting emphasis you can have?

simon: 1:10:52

yeah, it's interesting. You mentioned coverage. Do you have a sense, uh, already what the minimum level of coverage you need in a city is how many nodes you need moving around, um, in what kind of areas to get a good picture? You've done enough pilots and projects now to know that a city of this size needs 50 nodes at any one point moving around or something. So you can map that out for districts and cities and towns and things about the kind of minimum entry point yeah, very good question, because at the end of the day, it's all about hardware and deployments, etc.

max: 1:11:36

So we did. We do have enough experience after millions of kilometers been driven and covered. At the same time, every city has their own kind of DNA, their own special approach. Sometimes it's city divided by the river, sometimes it's just one city, sometimes they have different centers. It's very, very divided.

max: 1:12:00

So we come up with an approach which is called two-phase implementation. We place and that's what we're doing right now we're placing 10, 20 units per city with taxi drivers because they have a lot of coverage and they drive a lot. And thanks to, again, ai model, we're capable to take all considerations like population density, length of the roads, presence of different aspects. Like I just mentioned. Once all that process, we come up with an amount of units we need per square meter in the city or per kilometer to be covered. But this first phase, it's already enough to get a good picture and the clear sets of data for pediatricians, for example, that treat kids with asthma. This is one of our focuses, which we currently do. That's already now, but in the future then the second phase will be to fulfill the areas. If we speak about cities like London, for example, we would assume that approximately 200 to 300 units in mixed transport between taxi to postal, between Amazon or FedEx or DHL, et cetera, that would be just enough. City like London?

simon: 1:13:19

London with you know great and with those commercial organizations like the couriers, for example. That's just commercial arrangements with them to stick your devices on top of their vehicles, and they're always open to business. Those kind of organizations? Absolutely yes, it's a very easy story, yeah, so who is the customer initially in this story? Then, when you target a city, who's likely to be the first customer? What do you call it? The anchor tenant in a building? What's the thing that gets the project off the ground from a customer perspective? Is it the city hall, the council, city council? Is it a private organization of some description? Is it a private organization of some description? What's been the story so far of what that kind of anchor tenant is to get a project off the ground in a city?

max: 1:14:14

When we started it was obviously the cities and smart cities department. That's why we went into ITU and all these entities. But we very quickly learned that working with cities is not a sustainable business. You really cannot trust, even if you pass the tenders. Air pollution is not part of the city story. It's part of the county, canton or federal level. It's never like the city story, very rarely when city takes care of it, because the referencing stations it's science. You need really scientists to manage it. So it's usually environmental agents your ministry of environment or whatever responsible for the setup and usually they take care of the regions. Again, they place start the referencing station not in the places where you have pollution, but just kind of a background setup. Sometimes you find them in the place the referencing station, not in the places where you have pollution, but just kind of a background setup. Sometimes you find them in the city, but very rarely, and they manage them 24-7. And there's a very, very big story. So working with them very complicated and not financially sustainable.

max: 1:15:20

So we decided to move into. First we thought about B2C. So we said, all right, we're going to deliver our data directly to people, build a Sparrow app, build all the platform. And then we just saw the amount of platform and, like you said, no one needs another dashboard. And then we said, all right, we feel very comfortable. In the place where we collect the data, make sure it's proper. In the place where we collect the data, make sure it's proper, we prepare the data initially and we place the data on the marketplaces where this data will be consumed by the companies that are building those solutions. So, if you wish, it's a purely B2B2C approach. So we do have the C aspect, but we deliver our data for businesses. So, in a simple word, if Strava would like to use Sparrow data, we don't have to work with Strava. We have a map in which we can place the data and they can just subscribe for the data on the map. Very easy. It's even easier than just building a separate widget or whatever, because map and this is what, by the way, static sensors cannot do, because they have one single point to offer we have entire GPS and every address as we are address-based B2C approach.

max: 1:16:47

At the same time and that's the reason I spoke about pediatricians we try to search for the focus where, today, we can address our information without thinking about coverage, without thinking about building additional platforms and something that will be very urgent. And we find that pediatricians, particularly pulmonologists that treat kids with asthma or adults with asthma they very similar to like my son's allergologist need an address. They also need an address and they would love to have an address and they would love to know what's going on at the place where the kid lives, goes to school, what is going on when it's not present in the doctor's cabinet. So it's like remote monitoring if you wish. And, moreover, they also use electronic health records. So essentially, they have a platform in which they type the name of the kid and the address of the kid and those electronic health records are very open and they have their own marketplace. So it's a very simple integration.

max: 1:17:46

So the market of the usage is huge. For example, in the United States you have 6.7 million kids with asthma and 25 million adults with asthma, which is a huge addressable market. So, from our perspective, $5 of 5 euro per address per month for the historical data and real-time data free of charge it's a very tiny amount but from the economy of scale it makes a very nice business and everyone will be happy from that perspective At a later stage if they want to use insurance to cover it or whatever. But today it costs like a coffee and Microsoft Office. It's the same amount of money they have to spend.

simon: 1:18:28

I mean and I know it's difficult in startup phase and early business phase In an ideal world, several years down the road, where you've got serious cash flow, you can directly invest in an area and then broker that data to the various customers. But I guess at the moment you need those first customers to be showing an interest at least, or signing up to invest in the hardware, to be going in and to start collecting that data. So it is very much you need those customers. You can say chicken and eggs, okay.

max: 1:19:03

Every startup has their own chickens and eggs it's always, but once you understand who is the chicken and egg, it's easier to solve that problem. Yes, we need initial investment into the hardware which we currently. That's why we're raising funds and we're also shifting to the United States where this huge market which we can work an economy of scale, because, you know, switzerland is a very tiny market. We did some sales last year but to prove that the concept of the data sales is work, but it wasn't good enough from our approach.

max: 1:19:42

I truly believe that once air pollution data will be accessible for a large amount of people at affordable or almost invisible price, then people will going to use it on a daily basis. That's the reason we made our device not only precise but also extremely affordable. We're the cheapest and the most affordable device. If we need to send devices to Africa or to India or to suffering markets, we don't have any problem with that. You know, just recently I fulfilled the call from Clean Air Fund C4 to Citi for a free town in Sierra Leone and they put budget of I don't know 200K or 100K, something like that, and they said please at least have eight sensors included into the RFP or whatever. For that money we can do 20 sensors. It doesn't matter whether it's Africa or India or whatever.

simon: 1:20:39

Yeah interesting.

max: 1:20:40

We made an affordable approach because everyone has the right to access that information. It doesn't matter where you live. Air pollution is everywhere. You can find pollution in Great in Beverly Hills. You can find pollution in great areas by the lake in Switzerland as well. It all depends on when, where, where and what type of pollutants so, uh, that's why this approach does it take long to build a picture of a city?

simon: 1:21:15

is it something that, once the first node hits the ground, that it's 24 months later before there's a decent picture, or is it six months? I mean because you know you get the real-time data but start to build that because I guess cities are expert. You know you think of cities like new york, with massive climatic changes, or places like delhi, where you get, you know, seasonal impacts of things like stubble, burning and high pressure inversions, and you know that there's all sorts of factors that over a season or over a year are going to have big impacts on a city. Um, like, is this a? Is this as a life cycle? Is this the kind of thing that that you that takes a year to kind of get up and running, a couple years to get up and running, or you kind of up and running in six months in a city? What's the kind of time frame for this?

max: 1:22:08

when we start to measure the city with, uh, this first phase, like I said, 10, 20 units per city, we're already starting to get a very quiet, good picture within, first, I would say, four weeks. But uh, it's not good enough to really say that we understand the city, first and foremost because of the seasons. You know like nine pregnant women will never deliver in one month. We need to measure every season and every 365 days is just a must to have. So, of course, we're trying to use already existing information and, by the way, we are open to collaborate with static guys a lot and with existing information a lot, as long as they prove to have solid data, not like providers which don't care about the sensors. And there is a lot of interesting information that already can be integrated. And, again, ai helped us to do that story. So, before AI era and all this stuff, we were saying we need one year to measure, one year to compare and then third year it will be ready with proper assumption, analytical products. Now this all can be twisted in 365 days and then we already can start to do a lot of interesting stuff. So, basically, it doesn't take that much time to turn CT into CT with data that are real and street-level and address-based and cross and cross, integrated through a lot of platforms where it's needed to be, from real estate to insurance, from city council as well to health apps and longevity.

max: 1:23:58

Did you know that dogs also expose air pollution? Dogs are what sorry dogs also expose for air pollution animals? You know pets? Yeah, in the city it's. I found english, uh, in in, I think, in syria university. It was some study about it and they did an extensive study and it makes sense.

simon: 1:24:22

You know they load, they go outside, so they expose for the pollution it's, uh, it's actually surprisingly an area that I've spent a bit of time looking into, for a very strange reason. But, um, animals, that the the impact of environmental quality on animals is quite interesting and they've always been quite fascinating. Sentience for human health, because their metabolism is often a lot faster than ours, um, they are impacted by pollution much faster. So you know, you know, the canary in the coal mine is the classic example of that is that birds are notoriously susceptible to air pollution and respiratory illnesses and diseases. Dogs and cats are at pollution level the entire time.

simon: 1:25:10

The interesting thing about animals is they're not in control of their own environment, so they are sentience for an environment they're not in control of, so at all sorts of levels. There's a really fascinating study, I think from mexico city that looked at the impacts of particulate matter on children and dogs in the city post-mortem and and some of the impacts of it. So yeah, I mean animals. It's a really big thing. The other interesting thing about animals, particularly pets, is humans will often spend far more on their pets than they do on their own children. So it's actually I would it's a market.

simon: 1:25:49

I mean, don't tell my dog that, but it's a market that's been in double-digit growth, particularly in the United States for the last decade. Nearly the spend on animals. They call it the humanization of the pet industry.

max: 1:26:07

Because you know you're feeding. You know the companies that do the doggy seeding. They're walking out dogs in New York they're now on IPO, so you know it's a very good business. But particularly in big cities large dogs they suffer a lot. Small dogs maybe a bit less.

max: 1:26:24

And I had a large dog. I had a traditional Ridgeback died five years ago. And I had a large dog. I had a traditional Ridgeback that died five years ago. So I'm very sensitive for the dogs, but I'm also sensitive for the greenery and I have to tell you that we are lucky to establish our setup of a startup in Switzerland, in the place where we are, because we're surrounded by startups that are very much oriented into the urban setups that are properly done, because there's a lot of things in Switzerland that are properly done.

max: 1:26:52

It's just the nature of that country. So we can learn by the good example, not only by the bad example. So there is a startup that they created, a device. Literally, you put two diodes and they can listen to the plants, to trees, to different plants, and I said let's try to correlate with our pollution data and your test plants setting up in the city where they had, in the city of Lausanne and we did this for a few months and they find amazing correlation between NO2 and PM10. And they come up with a report that says, in a very simple word you have pollutants that are affecting the plants and the life of those plants are decreasing because of that, of that.

max: 1:27:44

Now I understand why, basically, cities, and why you don't have that much new greenery in the cities. Not because they don't agree to plant it, but because it costs a lot to remove it when it dies, and it dies very quickly. So they initially think, alright, they have to spend 1.5 million euros just to remove that garbage. And if we can choose proper greenery, extend the life because of proper setup, because some greenery is not really affected by the pollution. So you need to choose the proper one, depending on the climate, on the setups, on the natural aspects of this particular region. We can bring much more greenery. And that greenery will bring a very simple thing which every city needs it's shadow. You need a lot of shadows. You need really to cover cities from the sun, because a lot of things happening because you have direct sun on them.

max: 1:28:44

Yeah interesting, both mortality and pollution and from my world.

simon: 1:28:51

Do you think there's a bridge in this data between outdoor air quality and indoor air quality with what you're doing and, if so, how does a company that is collecting mobile data, how, how could or would that interact with indoor environmental quality? Do you think?

max: 1:29:11

so for us who work in the laboratory and then going into the field and then back to the laboratory in and out, we have very controlled environment and we control by one ppm, sometimes really up to the one micron level.

max: 1:29:28

We understand the different, the link between the outdoor and indoor, and it was for us always obvious that if you have ventilation system, doors, windows, the setup of the building at all today means that everything you have outdoor find it way this way or another to the indoor.

max: 1:29:50

Yeah, and we've been very surprised because one of the German companies there's a big filtering German company. They understood that and in a very early stage and we discussed with them and we made a few pilots in 2023, which proved that if you know what's going on outside, but not in the city but at the street level, where the building is in proximity of the building, in proximity of the roads, of highways, of different things which happens like constructions, etc. That very much affects the air that's taken from outside, brought inside and mixed with the indoor air and basically, people can literally expose for the pollution from the outside without even knowing that if they don't have good filters or exchange filters within proper time, they open the ventilation at the time where you have less pollution, opened ventilation at the time where you have less pollution. So essentially, that brought us into the position where we're capable to provide for the indoor air quality company services, products and platforms enough information to navigate the internal indoor air in order to make it at its best quality.

simon: 1:31:08

I imagine at a product level, urban pollution perspective.

simon: 1:31:11

Yeah, yeah, and I imagine at a virtual level for product manufacturers, like filter companies. Virtually you'll be able to give them a lifespan of the products based on the particulate matter and other pollutants that they're going to be filtering from outside. So you're giving them a very granular level data set that, if they know the volume of air that's moving for a product and it's clogging up rate, you could provide them with very interesting information about servicing and longevity of products and filtration in in buildings as well, I imagine well the Well, the first thing commercial-wise, we can increase the sales of the filter by 30%.

max: 1:31:52

That's how they identify it. That's the first thing to say. Second thing I see a lot of people, especially in the regions which are heavily hit by wildfires I'm currently in the Bay Area, so it's a very, very important topic here they're all very much affected by the indoor air quality. They all have some sort of purifier, some sort of sinks, but it's all not really linked to each other. Some things are working, some things. They have an app, some other other purifier. They don't really change them properly, so it's not really properly done.

max: 1:32:31

Uh, at the I would say, home areas, you know, in offices it's relatively okay, but in general, there is a big, big gap between what people actually is doing and willing to do in order to make their home safer and with more clean air and how we, as a provider of this outdoor insights, can push them both industry and the people to reach that place where they have better air much faster. Because if you know that you can open the window right now to refresh the classroom where the kids are studying and CO2 is higher than 900 ppm and we say it's safe to open the window, then you know it's a perfect situation. But when you open the window just to let more PM10 gets in and kids just get you know sicker and you don't have clean air, then it really creates an even more problem-dense solution. So yeah, we do see a lot of correlation and we work to make sure that everyone will be aware and work and use our data in a proper way.

simon: 1:33:43

You wonder long-term, with data sets like this, whether it will have an impact on real estate value as well. These more granular data sets could feed into the value of properties because you now understand at a much more granular level the pollution at the street level, don't you? You know when we started in switzerland?

max: 1:34:06

started. Yeah, you're absolutely right. Of course, real estate it's the first things that comes into the mind when we talk about. But when we started in switzerland where huge real estate market, extremely important, very granulated and and and there is a lot of layers on this business, from just real estate up to analytical platform, from risk mitigation of big insurance companies like Wust Partners, etc. The first feedback in 2022 was I would prefer to hide those details from my clients and not to show them anything. That was the first approach.

max: 1:34:47

Today, the approach has been changed. Today, they think, in a way, if I provide that information to my client, first of all, he will think that I care about him and his family from a real estate perspective and second, if they are insensitive group. For them that would be a very essential story and I wouldn't be in a situation with my son like I was. You know, we just bought this apartment in that place so we couldn't move. But people who are not in the sensitive group, they will say, all right, is it polluted?

max: 1:35:20

All the time we say no, it's like 18 days out of 365. So only in the morning. Okay, I will put this, this and that and I have no problem to tackle this whole story. So they will not buy a particular real estate just because there is air pollution surrounding their city if they're not sensitive and they just live normal life. So real estate industry evolved and we can work today in better approach with them from website that can simply show the scoring of the asset, like with climate as well, with water flows, with noise, with how far it's from different school and other areas, and it becomes really, really easy to do that today in comparison to what it was before.

simon: 1:36:16

And it's very hard to interpret or to second guess how people are going to react to data anyway, because we're dealing with humans and people have different priorities and risk is not absolute to data anyway, because we're dealing with humans and people have different priorities and you know risk is not absolute, you know people's tolerance of risk is going to be different and so on.

simon: 1:36:34

I mean, I was, I was, I was just recording a one of my one take podcasts today, where you know where I review papers, and one of them was looking at the impacts of indoor environmental quality on real estate value, and one of the interesting asides in the study was that when they looked at the value of real estate, actually higher levels of pollution around the building was a positive factor for the value of that real estate, not a negative. And one of the things that they uncovered was that the reality is is, if your building is in a bustling, thriving urban center, um, it is going to have a higher value. People want to be in those spaces, um, so even though the pollution was higher, actually it wasn't the pollution's impact on the building real estate value. It was its location, its access to public transport and nightlife and the hub and bub of a lively city that drove that value. So it's interesting how, you know, even something like air pollution may not have the impact we think it's going to have. Um, because it may be.

max: 1:37:54

It may be reflective of other positives as people see them well, this type of real estate object will be sold this way, another, because there is a very particular clients buying that type of things. But again, it's enough to have a certain part of the sensitive group that this decision will be changed. But again, when I'm talking about sensitive groups in the city, they represent between 30 to 40% of the population, which is not majority population, which is not majority. But it's enough to already address a good portion of what we do and make great commercial success only with even half of that population. And if we can make lives of some kid with asthma better in the morning and make sure that some elderly lady will not die out of heat islands because of extreme heat and her family will be aware, in order to make certain steps, that it will not happen, for me it will be just enough.

max: 1:38:56

I don't care about someone who is buying real estate uh object in the city, that he will buy this way or another, it doesn't matter air pollution or not. Moreover, I'm more into protective and prevention stuff. If you can equip your house with all the things and make sure that you have proper uh, it doesn't matter air pollution or not. Moreover, I'm more into protective and prevention stuff. If you can equip your house with all the things and make sure that you have proper setup at home at vehicles, that you choose alternative routes, you don't do bicycles in the city, you don't use these bicycle lanes and you do all the things, then everything else is just fine. It's not really affecting too much. But again, if you have any family and you're part of the sensitive group, then it becomes a very crucial story and when it's become really a life-saving story.

simon: 1:39:39

So yeah, that's the ideal city, if you wish. And you mentioned overheating. I imagine this data is absolutely fascinating from an urban cooling, urban heat island effect. I imagine some of the data you see in some of the warmer cities where, like you say, sheet shading and green areas and dark areas all are really critical. I imagine you're building a fascinating picture of the granular heat levels within a city.

max: 1:40:12

Absolutely, because, again, today you don't have that information at the street level. Temperature is a lot of. There is a lot of very good modeling. I don't say it's bad, it's good, but it's still modeling and in many cases when we drive and we identify, we measure temperature every 10 seconds. So we identify sometimes difference at the same street of 10, 15 degrees within the same minute, within the same hour. And it's a huge difference. Because if we're talking about 30 degree and 40 degree, exposure for the sun at that particular level for certain groups of people is very, very problematic. Again, I'm talking about elderly, I'm talking about kids with asthma. If they need cool place, cool dry place, asthma doesn't like that, but otherwise, it's very important.

max: 1:41:04

But otherwise it's very important. So our data, when we include into this modeling, can really be very, very important for the decisions of the city to manage those sensitive groups, because in many cases cities do participate in managing elderly people and managing schools, educations etc. If the school has a schoolyard where kids are going outside and playing outside and it's within direct sun, that will be a completely different afternoon for them than the kids that have a shady and a nice tree and a cool place. It will be a completely different afternoon both from the grades perspective, total feeling etc.

simon: 1:41:48

And when it goes across large cities.

max: 1:41:51

then we speak about volume and then we speak about statistics, because then, at the end of the day, we'll calculate what will be the effect of this particular thing. And it's not even related to the air pollution, it's just temperature and humidity.

simon: 1:42:15

So you're in a fascinating part of the business life cycle. At the moment you're in that kind of post seed round expansion phase, growth. You've got that target, if I read correctly, of 10 cities in europe and 10 cities in the US to try and expand into and so on. That must present some interesting challenges, the difference between Europe and the US, this expansion, the growth phase for the business. I know it's the exciting bit as well, but it's also the bit that's where you need feet on the ground and salespeople on the road and you need to be doing the deals and shaking the hands and getting it done. So I know it's fraught as well, but this must be an exciting phase for the business to be getting this out there now and trying to scale.

max: 1:43:05

Skyo. The exciting part was actually finalizing in end of 2024 what we did since 2021, I would say, because it's practically since 2021. So three years of pure operation, driving in 30 cities in the world, harsh tests in plus 51. Right now we're running tests in plus 51. Right now we're running tests in Hong Kong with high humidity levels. A lot of interesting things. Decisive moment where we saw that we lost ozone, so we're not going to continue, and all this stuff. Fascinating part was acquiring real money for that data. That was amazing, that it works and it can be sold and it can be sold not only once but twice and many, many times and the evolution of the market at the same time, because if market wouldn't be ready like for many companies in that area back in five years ago, we wouldn't be in that position either. But right now, after all that things, we're ready to shift and we're ready to grow for the global things and for us, the most convenient geographical area to do that is United States. But you cannot work in the United States out of Europe, so I'm here now and you need to drive. I'm in Los Angeles measuring post fires with the Palisades and AltaDena in Malibu. It was extremely interesting results in comparison between CEO and PM and we will come up with a report for sure. We will come up with a report for sure.

max: 1:45:01

I'm in Beria, measuring San Francisco, measuring Palo Alto, which is a beautiful green little town but with extreme levels of methane leaks from the sewage and very high ozone for sure. So we're in New York, we're covering New York. New York is in my heart right now because I drove over I think 3,000 miles already in New York by myself, across the city streets with the devices and measuring proactively, and in some places it's surprisingly clean and nice. And in some places, which are supposed to be very polluted, they are, and they are very heavily polluted and there is a lot of vehicles. Like you said in the beginning, no one really really uses public transport I mean subway in New York, yes, but people don't like to get underground that way. And all the bicycle lanes again, currently in New York it's like suicide drugs.

max: 1:45:49

But yeah, we in the United States, our goal today is 10 cities. We actually aim to work with what they call asthma capitals. You have actually, if you google it, you will find that there is cities with a very high level of asthma for kids and for adults. Actually, la is part of them, so we're already starting to work with the first city called Allentown in Pennsylvania. We're currently in the reviewing three years setup in that city to help them out, because they tried literally everything from air pollution perspective and it's not really insightful. So they're really anticipating to work with us and with the hospital pediatrician and asthma related cases and, yes, we would like to set up.

max: 1:46:39

That's why we do the fundraising right now and it's very painful to do the fundraising at the current funding climate, especially when we are the company. A little bit in between, we're fit to every focus of the funding and to no focus, because we can work in real estate it's prop tech we can be health tech, we can be data, climate tech, clean tech, whatever. But when they started to understand what we're really doing, it's a bit difficult and challenging, but I'm sure that the right people will. We already got traction with First VC in New York so it was great. So we still remain with a certain amount to close, but I'm pretty much sure that we'll find the rest either in Bay Area or across the United States.

max: 1:47:34

But we also got a lot of interesting business requests in the United States like measuring very interesting distant areas, what they call post-industrial and army bases related laces with chromium, with heavy pollutants like hazard. That's why we come up with ProVersion to address these demands. So there is a demand for linear.

simon: 1:47:59

Yes, it's a more classic environmental monitoring from an environmental protection perspective. More heavy, yeah, more heavy and deeper.

max: 1:48:08

We're not even going to measure it. We're going to collect sampling through mobile monitoring and send the sampling to laboratories to identify whether there is a presence of whatever particles.

simon: 1:48:19

Yeah, I was going to ask you that actually. I mean, obviously this is a focus on urban centers, but did you ever kind of end up, by accident or on purpose, capturing the intercity environmental conditions as well? But you know, a bit like urban, extra urban and rural, I mean, I imagine that the same rules apply. There must be fascinating data to collect. Even though it's a lower level and, I guess, better in a lot of places, but you know, you do get pollution in rural and suburban environments, like you say, industrial areas and so on.

max: 1:48:53

It's accumulated I imagine as a as a map.

simon: 1:48:56

I imagine it's fascinating to see we work in switzerland.

max: 1:49:00

we have a lot of intercity data that we collected and we we saw how it works, because you have a lot of stone extraction areas in proximity to the city but still outside of the city and sometimes it's very much with the wind ending up in the in the main streets but uh, in in rural areas, like in the united states, we work, we also measured with some trucks uh goes like FedEx that goes between the city. It's interesting from wildfires perspective, for example, we can detect some stuff which start off in areas where you have wildfires risk. So it's possible to do that. But you know one thing at a time, it's just we cannot concentrate. We have oh yeah, yeah, yeah.

simon: 1:49:49

We can work with space. Yes, we can work with space.

max: 1:49:56

My dream is to work with NASA and with European Space Agency, because initially we had this project that we wanted to execute to translate space data into earth data, because sometimes spectral images and what they see from satellites it's uh, it's not clear enough in many cases and they need to translate it into verification, what we have on the ground, because when you see wildfires in Canada ending up in New York, it's definitely not just the intercity measurements. It's required a little bit more than just measuring the city pollution. It's a stratosphere approach and you know a little bit higher. Then matching all that together with drones and with that kind of scanning and understanding and monitoring of what is going on, because a lot of things we still don't understand.

max: 1:51:14

You know Sahara Sand arriving to Switzerland. Back at the time it was arriving one time every like three, four, five years. Now it's like five times a year, even more, sometimes like 15 times a year. So indeed it's a climate change, but we are strong. We have a lot of AI and machine learning and brain and good people that we can protect ourselves, protect our kids, our families and make sure that, even with the harsh environment, we live a very good life, better life.

simon: 1:51:47

Yeah, you kind of half answered my last question, really, which was around what kind of legacy do you and your co-founder want to leave with this business? If you're looking back in 20 years' time, what's your hope for this venture? What legacy would you hope it would have left? Or mark would it have left on the environmental space?

max: 1:52:11

we have, uh, two main things which we really dream. Dream of them, if you wish. First is that the data we collect will become the foundation of a lot of third-party involvements, like young people and academia and different organizations that can use our data in order to create new approaches and individual and kind of city-based. We truly believe in the power of cities, so we'd like to empower cities in a way that they will use that data to make what we call data informative decision. Because, you know, I have that experience of over half a million kilometers on the roads to see with my eyes what's going on and see how sensor performs and understand the link between what we measure and what we see. Because we couldn't have cameras. Yes, in eu I did that, but because I also needed to understand whether what we're doing is needed on or it just, you know, some additional business that just, uh, will be die after certain period of time.

max: 1:53:22

No, we are. We have a big difference from what is happening on the streets and what current information we do, but no one is to blame for it. Just like we didn't have MRI in the hospital before, just an x-ray, that's it. So, once the MRI appears, a lot of things have been changed. This is the change we would like to see from data perspective. See from data perspective.

max: 1:53:51

And the second thing that we would like to see how commercial aspect of our data make new businesses and new commercial opportunities open up for other businesses, because once they have access to that information, they can build new filters, they can build new I don't know purificators. They keep building new sensors. We're also thinking of opening our IP at a certain point of time to make sure that everyone can produce the sensors. What we did just delivered the data to us and everyone will be happy from that perspective. Of course, we would like people to get healthier, to see statistics dropping down from current continuous growth in cardiovascular diseases and respiratory problems. Elderly people issues all these things and longevity your life to your dog as well, by the way for just following a regular.

max: 1:54:50

You know areas where you have less pollution, so this is a yeah in the natural people to see the unseeable, and I think you know it's one of the.

simon: 1:54:58

It's one of the things about this time that we're living in is quite exciting. It's very hard for us to predict, as a broker of what you hope is valuable data, what somebody's going to be able to do with that data, what businesses they're going to be able to create from that data, what other data sets they'll be able to combine it with and learn and understand in our environment in new ways. You know, particularly with the evolution of ai, that space is just moving so incredibly fast that that that kind of ground level data becomes really important and almost impossible to predict its value actually at this stage because, um, who knows what connection somebody is going to be able to find between this data set and something you haven't even thought of? You know, and and the way the world is moving on at the moment and the evolution of sensors you know people tying in with other data sets is only going to become more and more of a possibility, I imagine my, my 11 years old and 12 years old boys.

max: 1:56:02

They have obviously understand that. They know a lot about spararrow and about what we do and they have their own ideas that sometimes they cannot even guess. How come they realize what we can do with it? And I think simple hackathon in every city can encourage people to work on solution and to see many creative ways to use the data. That's for sure. But, like I said, we have you know it's just the beginning and the AI story is very important. But it's also important to say that without data, without good data, all machine learning, all AI, all models, all kind of calculations, they're just philosophy, they're not real, realistic and you need a lot of great information and things can work only when you start to get a realistic information from the place where you are. I also encourage everyone to go more outside and to experience, to discover the city, even before our data arrives.

max: 1:57:09

There is plenty of areas in the city that have been undiscovered, and sometimes it can be even the street just parallel to where you live. The city is full of surprises. You will definitely find a lot of things over there. So once we will have the foundation of the data, like ChatGPT have its own training models, which is us. We train them every day. It's the same story for air pollution. It's the same story for our urban life, what we call the well-being of this whole story. Once it's fulfilled with data, it can unlock, it works like oil for the engine that can run faster and can really become a jet in a certain point of time. So it is very fascinating to see because, unlike you know, I'm obviously know a lot of guys from a business of air pollution that work there like 10, 15 years and they already so tired of fighting the windmills.

max: 1:58:10

And I'm so encouraged that we find ourselves in that position with Sparrow at this period of time and I always say that to my co-founder that we're absolutely lucky to be in the right time with the right product and just a bit, one step and a half ahead of everyone. So this is where we stand at the moment, because that's also the reason to provide real-time data free of charge, because today almost no one can really digest and do anything with it. It's impossible to sell, even for a small amount of money. But it's also kind of I think it's human right to have access to that information. So I think we will keep it real-time data free of charge as well, all the time.

max: 1:58:54

So, yes, it's very fascinating. It's amazing times. I think there's a lot of positive developments because we've been accepted very positively across many areas in business, in government, in authorities. Many said that finally they have something that they can look at it, and especially data scientists. We work a lot with them. We provide them with our one-year data in one city Like Geneva and Lausanne. They said that it's just a fascinating amount of information they can work on, come up with a lot of things, so we will make sure that they have a lot of it constantly and a very good quality.

simon: 1:59:37

Max. On that note, I think it's a great point to bring the podcast to a close. It's been really interesting talking to you this afternoon. I think it's been a fascinating perspective to look at a subject matter we talk about a lot, and from a different angle, and from an angle of a company trying to forge a new furrow in this sector. I think it's been equally as interesting to listen to. So thanks so much for your time this afternoon talking to me. It's been an absolute pleasure. Thanks so much for your time this afternoon talking to me. It's been an absolute pleasure, and we'll keep all of your information in the show notes for people that want to find out a little bit more about what you're doing, max.

max: 2:00:15

thanks a million. Simon, it's my pleasure. Thank you very much for providing me access to your amazing platform, and I watched all of your podcasts. They are really great people, very important subjects. You're doing a very good and very important job, and it's only human aspect Only us, can really do the change. So thank you again for this opportunity and, yes, everyone can find us on all possible platforms.

simon: 2:00:44

Thanks, a million Thanks for listening. Hold on a minute before you go and shoot off or onto the next podcast. Can I just grab your attention for one minute? If you enjoyed this episode and know someone else you think might be interested in this subject or you think should hear the conversation, please do share it and let's keep building this amazing community. And this podcast would not be possible without the sponsors AECO, ereco, ultra Protect, imbiote, 21 Degrees, farmwood and Eurovent. They're not here by accident. They care deeply about the subject too, and your support of them helps them support this show and keep it on the road. Please do check them out in the links under their quality mattersnet. Also check out the show on YouTube with video versions of the podcast and more. See you next week.