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Transcript : Adding Intelligence To Location | Devashish Fuloria @ Geo IQ
In a data-rich world, companies that can leverage data for better decision-making are at the top of the value chain. GeoIQ helps businesses make smarter customer decisions by using location data. Devashish talks about how he built an API business that leverages big data and machine learning to help consumer internet companies make smarter decisions.
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[00:00:00] Devashish Fuloria: Hello everyone. I'm Devashish Fuloria. I'm the CEO of Geo IQ.
[00:00:05] I was born in the mountains, I'm from Uttarakhand. It was a town called Pauri Gharwal, beautiful place surrounded by jungles. So we grew up interacting with a lot of outdoors. I used to do well in school. It was quite clear cut that there were two options. Either you go and become an engineer or you become a doctor. I did not want to become a doctor because there was a doctor at home and I see what kind of work that means. So I by default I ended up being working towards, engineering and one of my uncles was a professor at IIT Kanpur so we always had, I had been visiting IIT Kanpur as a parent.
[00:02:12] So there was some sort of aspiration value associated with that, but nothing especially, which was just focused specifically on an IIT. Even today, if I look back, I feel, if somebody would've guided me I would've been a geologist, not an engineer but that's how life takes you. So IIT was an absolutely, great for learning. I joined Tata Motors got an early job. It was into sales and marketing. One year, I think I was based out of Bombay, but traveling north, south, east, west across the country And the interesting thing was I was a salesman and I was selling these axles, under a trailer truck, the axles. And I was somewhere in Rajkot and one mechanic asked me a question about the quality of the wells, and I had no answer to it. And I said, okay, so what really did we do with the degree? I mean, we absolutely did not have this sort of practical knowledge about loads and real world voting. So at that point, I started thinking about, higher studies. So this opportunity came from Imperial College London. So I went, I had an it was a straight admission to PhD, no masters, quite cool. The entire PhD was, based on that. It was an eye-opener for me. I said, you can solve complex problems, but you have to keep going back to the basics
[00:03:41] Akshay Datt: and what was your micro specialization? Typically in PhD there's like very micro area, very small niche in which people become the best in the world in that small niche.
[00:03:51] Devashish Fuloria: So it is very interesting because now I was looking at defamation of semi solid alloys. So when you are making or creating some sort of an alloy, obviously you have to heat it up, it's liquid, and then it starts slowly solidifying and then you define the properties based on the rate of that solidification. So my entire PhD was in that zone of semi-solid when it was not fully cast, it was not fully liquid, but lot of property changes were to be controlled in that phase. And I was focused into that specific, 50 degree temperature zone where a lot of things used to happen. So that's, that was very interesting. So I spent some time with the UK government as well as part of a policy group. Got a great view of how the policymakers think about things that six month has still established my views even today, which remain true on electric vehicles as a how geopolitics comes into the picture what the government would be thinking about, before applying a policy.
[00:04:59] But post that, I went into an engineering consulting where I was serious about this job because there was a massive cricket pitch just outside the office, beautiful ground. I said, I would like to work at this place because has is a cricket ground. I used to play a lot of cricket and the director, um, who was interested in, my work was primarily interested because he saw somewhere in my CV that I'm an off spinner. And he said, Hey, we want an off spinner in our club. So apart from all the work, this cricket thing was also, somewhere feeding into the whole decision making.
[00:05:34] But then I was again working in the structural integrity of oil refineries, airbus structures, ship structures. Uh, It's primarily in Europe. So it was a consulting company, which used to tell these bigger oil companies, what sort of monitoring mechanisms you need to employ to avoid certain problems. And it's all based on technology. So now from image, one of the key transformation that I did was the algorithms that I was writing on images on converting 2D images to 3D structures. It's called tomography. I applied the same thing on sound now. So now in all these structural integrity, it was a sound signal, which was being used as a measuring tool. So now how do you generate a 2D image from one dimensional signal? So that's where, same principles were applied in real structures. So there were images being generated. There were neural nets, which were, the name of the game back then. No AI back then. Because a lot of these algorithms were based on how do you identify any changes in structures based on these signals. the best part was spending, a couple of days inside a 380 just skeleton of a 380 at Toulouse.
[00:06:51] And then you realize how flimsy a plane is,
[00:06:53] Akshay Datt: I guess after this is when you came back to India and you had a six years engagement with sports. Tell me about how that happened.
[00:07:02] Devashish Fuloria: So I just walked into the offices of Cricinfo, in Bangalore, just to ask questions, just being curious. How do they work? How do they operate? I went there Once I went there, second time I went to Bombay and the editor Sambit uh, said, come over again. And then, and I was just being curious. I had no plans of working at in cricket. But then he said that, Hey, why are you wasting my time? If you want to work, just come and work and spend 15 days with us and we'll see. And at that point of time I said, oh, okay. So This is also a possibility. So I spent 15 days at Cricinfo, really loved the place. And that 15 days became three and a half years very, very quickly. So, it's a writing slash editorial job. So partly the team is out. So you are a writer. If you are in the on the desk, then you are the editor. Fantastic job working with an international team, working 24/7. I don't remember ever taking a holiday for Independence Day or Diwali or anything. I mean, we were just working and through. So really fun period of my life. But then I was also picking up signals that, okay, Cricinfo is Cricinfo because it has a lot of data and, I used to love playing with the data myself.
[00:08:22] So I thought data brings in the engagement and something similar could be done for other sports as well.
[00:08:29] And that's when I started thinking about that, okay, now I need to go back to building things. And the first thing I thought of was that, okay, let's apply the same philosophy to amateur sports. And that's when we created me and other co-founders we got together, we discussed this idea that, hey, can we build communities around sport?
[00:08:50] But with data as a central element to it. So we created this app which would allow people. Challenge each other. Record each other's score. And it used to automatically build their ratings in the background. So imagine if you're in Bangalore and let's say there are 5,000 badminton players in Bangalore.
[00:09:09] Even if you have not played a player, you would know how good or how bad the player is based on, few interactions from other matches, et cetera. So there were some algorithms involved in the backend, but the whole idea was to bring in engagement with some data. So that's what we did. We did it for one and a half years couldn't scale beyond Bangalore so that at that point of time, and there were, with co-founders, people had some other pressing needs. And so that had to be shut down and wrapped up.
[00:09:42] Akshay Datt: Building a advertising led business like this would be it was going to pay itself through advertising.
[00:09:49] Devashish Fuloria: See, at that point of time building the first product, you probably are not thinking that far. And at that point of time, the only thing you wanted to do was to increase the engagement. Now there could have been many business models. I think we were a bit immature back then. Business model we thought of would've been around getting all the tournaments into the system and asking the tournament organizers to pay for this platform because they would get all the players into the system. So create an engagement system from there on. We also we did make some money out of it, but again, it wasn't looking scalable at that point of time.
[00:10:27] Akshay Datt: Okay. So then what now, like you are like probably out of your personal funds also, you must have put in your savings and out of a job also, like you must have been in that fairly lost kind of a state.
[00:10:39] Devashish Fuloria: Yeah. We had raised a bit of money even for Ze Ladder, but yeah, the money runs out. But I think what so I wasn't really keen on, getting back onto a job straightaway. One thing which I'd done in 2011 and I really cherish it, is taking that three, four months off to just, slow down a bit and think without any unnecessary rush. And so I took some time off. And I've really loved these breaks in between because I've spent a lot of time back in the hills. So I came back and there were a few thoughts again in mind. I started speaking to some of the mentors here that this is what we want to do. This is there are three or four possibilities, something in the B2C space, something in the B2B space at that point of time my current co-founder, Tusheet, he was also going through that zone of flux and I've known Tusheet, you know, over the last seven, eight years. He is a junior from IIT Kanpur, so we were spending a lot of time brainstorming on what works, what doesn't work, getting feedback from the mentors and, it was basically, that exercise of iterating through multiple ideas and, seeing that, okay, this seems like a good possibility.
[00:11:57] And that's when we entered into working into building Geo IQ.
[00:12:02] Akshay Datt: And what was the possibility you saw? What was the gap or the problem statement that you wanted to solve?
[00:12:08] Devashish Fuloria: And around that time uh, Tusheet was, in a freelance capacity working on a problem. It was one of the big time retailers in India who was trying to spread across tier two, tier three cities and open 5,000 stores, 6,000 stores. They made these big announcements in media.
[00:12:26] Akshay Datt: small format stores,
[00:12:27] Devashish Fuloria: like small format stores. Yes. But their early success rate had been really poor. So only 24
[00:12:35] Akshay Datt: which sector was it in? What was the product?
[00:12:37] Devashish Fuloria: Retail. Retail
[00:12:39] Akshay Datt: I mean within retail, like food, clothing, what, like,
[00:12:42] Devashish Fuloria: general retail, so Reliance fresh kind of thing. So 25% of their stores were surviving after two years. And they would say that, Hey, we know the Metro City, so we know where to open these stores, but how do we decide where to go in, in, in India? And so the the question that was coming was that, where is the data? How do we take data backed decisions? We are left to the mercy of what the ground team is telling us. And, they would push their own agenda, so there's no centralized information.
[00:13:15] So we started looking into what exists externally. What is not part of the company's system, but what exists externally. So we looked at, government data. We looked at satellite imagery, we looked at map data and just started layering everything up on a map. Now, question was about location. So we started stacking all the information that we could get on a spatial database. So that, let's say if you want to take a call on Meerut on some street and you want to understand what's happening around that location, you start getting certain kind of ideas. It was quite powerful because this sort of external data systems haven't existed in India till now.
[00:13:57] And India is a data poor country, so people collect their own data, but outside that they don't really know much. So this was the holy grail that we were after, that if we can make this real world data, real world information easily available in a manner in which people can extract intelligence out of it I think we would add a lot of value. So that's what we started with broadly that. The entire country's data in a single place available through maps available to everybody. That's how we started.
[00:14:31] Akshay Datt: What were the data points? You said like government data. What exactly was this? Like the kind of data that you were putting onto a
[00:14:38] Devashish Fuloria: So if you look at government data is very rich. There are questions about, its uh, uh, vintage, census. Something as simple as census as 2011. It hasn't happened. But if you take the other argument, there is no other survey, which is as comprehensive as census, and it forms the basis of everything now. So now census just doesn't measure population. It measures maybe 200 things about each and every locality, government calls and votes.
[00:15:10] Akshay Datt: Each and every household, basically for every household, they will ask you, how many children do you have? Age of children, what do you earn? Do you have a car? Do you own the house? Or are you living in a rented house?
[00:15:21] Devashish Fuloria: Yes, rented house. What is the quality? very subtle questions, but it's a big question bank. And then it publishes it in an aggregated format. Says that in this board, in this village, these are the broad parameters now government talks in votes, but the world talks in pin codes and localities. So there is no match. So therefore there was first value was that, hey, can we help people speak to this database first? Government also publishes lot of growth numbers. So now these are more recent numbers. What is the growth rate of population, this little village, that village, et cetera. So now you apply growth indicator so you get, updated numbers across India.
[00:16:03] Then we also, now this is broad information, aggregate it. We said, to combine it in a form of a pin code, we first need to break it down into smaller unit. So we use satellite imagery to identify where buildings are. And then we broke all these numbers because these were all population based numbers onto buildings. So now we could go down to hundred meter by hundred meter.
[00:16:24] Akshay Datt: So the way to break it down into buildings is just say if one ward has hundred buildings, and so you just divide that by 100.
[00:16:32] Devashish Fuloria: You break it on the rooftop hundred or basically on the rooftop area,
[00:16:37] Akshay Datt: Okay. Okay. Okay. Proportionate to area. Okay.
[00:16:39] Devashish Fuloria: Proportionate to area. That also allows you really good separation. In terms of, Hey, is this an army land, agricultural land? This is where the population is concentrated. You start getting a really, really fine grain picture of how the population is spread out and what sort of socioeconomic backgrounds you're coming from. Now you start,
[00:16:59] Akshay Datt: and the technology exists for satellite image to be tagged as residential farmland. Commercial
[00:17:06] Devashish Fuloria: that is where algorithms come into the picture. Very generic algorithms exist, but we had to tune some of these algorithms to our needs. Then recombine it with all the other data points. It, there's a lot of cross stitching happening here between different databases and then break it down to these really small units. Now if you once,
[00:17:26] Akshay Datt: and you had the you were able to map a ward like say South Delhi Ward for example, so you were able to map that on a map that this is South Delhi ward,
[00:17:37] Devashish Fuloria: yeah. This is what the government is talking about. So those are. .
[00:17:41] Akshay Datt: And that, that, that is something you have to manually do for each ward, or is that like available, like the ward location?
[00:17:48] Devashish Fuloria: Government has those these are spatial files, geospatial files. They're called shape files. These are polygons. You convert them, fill in it with data, then break it down further it's surprising that people don't really know what are the extents, You might call South Delhi as intruding Lajpat Nagar and Sarojini Nagar, but somebody else might say that South Delhi for me ends here.
[00:18:12] So there is, people don't use these standard methodologies, even pincodes. It's a postal department shape. So we were breaking it down all these data points to units and then recombining. Now you, once you've broken it down, now you can recombine it and initiate it. so the one thing which, you know, very early thing that we released was we released population on pin codes because we had broken it down. Now we recombined it. It'd be surprised to know that every company works around pincodes as some sort of base, but nobody knows in India what a population of a PIN code is because these are different shapes. They might be overlapping . So now the only place you will find population of a pincode is Geo IQ's database because nobody had done it before.
[00:18:58] so that, you know, these are little things which start creating little impact. But I think it was in the second year that we started getting some sort of a thesis on how this data has to be used in a more uh, productized manner. That's how we started building it. Even that step has gone through a couple of pivots, so.
[00:19:18] Akshay Datt: Okay. Second year is, which year?
[00:19:20] Devashish Fuloria: This is 2019.
[00:19:22] Akshay Datt: Okay. Okay. So what was your thesis in 2019 that you found about,
[00:19:26] Devashish Fuloria: so 2019, we said, Hey, if we are giving people location data, the most important manner in which location data makes sense is in maps. So you basically, we gave them, clients, a platform where they could just see everything layer by layer, population as a layer, this as a layer. So it was a tool that we created for business analysts to quickly identify where their kind of hotspots or, tax spots were. It was primarily data as a service, but on maps there were parallels across the globe. One of these American companies, Esri has been doing something similar for 50 years.
[00:20:09] It's a, more than couple of billion dollar revenue company, a fairly legacy, legacy based player. But they have their own file formats. They have their own, business analysts, et cetera. But we thought that we can just simplify it and just preload the maps with data points.
[00:20:26] So you just sell in layers and access this information.
[00:20:29] Akshay Datt: One data point is obviously population. What else?
[00:20:32] Devashish Fuloria: Then there would be obviously a lot of socioeconomic parameters, but then there would also be. ,
[00:20:38] Akshay Datt: what kind, like car ownership, house ownership, like that
[00:20:41] Devashish Fuloria: car ownership, house ownership. If you're looking at countrywide thing or countrywide data spread, you also look at material of house, cement or thatched roof. So you know, the, this, is it connected to sewage or not? These sort of parameters give certain indicators about the kind of population that you're looking at. Then there would also be business locations. So we had started crawling all business websites to see where their stores are. So that would also give us an idea of where certain kind of business clusters were, is this this shop on this particular street is only apparel heavy, or what other kind of businesses are existing here?
[00:21:26] Then you start layering it with real estate information. Okay, there are apartments here, lots of apartments everywhere, and the real estate value. So now real estate value becomes a very direct indicator of, on this particular street, how rich the people are . So nobody tells you their income, but you have to use these secondary indicators. So you think of it, if it's C1 Street and the real estate value is shooting up, and suddenly you see a lot of brands coming in the vicinity. So now you have not only figured out that these are rich people, but these are rich people who are going out shopping in these kind of shops and in these kind of restaurants. So that sort of graph starts building about places. So even without knowing somebody in that street, the
[00:22:11] Akshay Datt: you were able to scrape this from like listing sites, like real estate listing or testing or business listing, yellow pages, those kind of sites.
[00:22:18] Devashish Fuloria: Yes. So you just basically, you find anything which has an address in it and you start putting it in this database and make sure that you're making those connections so that it becomes a very strong graphical database.
[00:22:32] Akshay Datt: And so once you form this thesis that you wanna build this then what did you raise funds to build it Because this would need more investment. You would need a team.
[00:22:40] Devashish Fuloria: Team. Yes. We raised a, a small seed round from a bunch of angels in 2019. Again, things were not very neat and established at that point of time, but we needed some bit of focus. And at that point of time we also realize that data is not the only thing which will make you make a business out of this. Because, hey, I have created thousands of data points, now for a client it becomes difficult to choose what is important, what is not, what the need is an answer specific to their question. And what we were seeing is that people would, choose, take the easy options out. They will only choose population, some income statistic, but nothing else. So at that point of time, we are working with one of the scooter sharing companies here, which had lot of their own location information. They had scooters spread across. They knew where there were more bookings happening, where their scooters were getting stolen, and they wanted to understand what makes a location bad for them. And our team was, the DNA of the team was very data science oriented, very machine learning oriented , we said that, Hey, if we have to now tell these guys, what is good and what is bad for you, we ask them, tell us what you know already.
[00:24:01] Bangalore, you know where more bookings are happening, where theft is happening. Let's not bias our choices here. Let the model figure out, let the model interact with everything that we have and everything that you have. And, this will figure the exact location, locational parameters. So we did that and we figured out certain very interesting insights out of that, that their scooters were getting stolen, where the roads were very narrow. That was one parameter out of these thousands, which get picked up. Their scooter was getting stolen where the population density was extremely high. Their scooter was getting stolen where the density of auto repair shops was high. So now this whole equation was getting built based on what they knew and based on this data. So that was when again, the next stage in our development started happening. We said, data is fine, but some bit of AI has to come into the picture because we as humans won't be figure able to figure out what is important out of these thousand parameters. So we, we said, but let's start with what we know.
[00:25:06] Akshay Datt: Now, you must have been providing this as a consulting rather than as a SaaS at that stage, because it was so complex that a regular user would not be able to make sense of it.
[00:25:17] Devashish Fuloria: And so we were in the middle of this, and then Covid happened, and then all these, projects were went on a standstill. And at that point of time, we had a seven member team, smart people, fast working people, and people were quite passionate about
[00:25:35] Akshay Datt: did you have enough money in the bank to survive?
[00:25:38] Devashish Fuloria: We had some money. And so the question at that point of time was that, what do we do about Covid? And people were, quite passionate about, all these lockdowns and what was happening. So we realized that people were asking, when the lockdowns happened, people wanted to know where are these containment zones and where they're not. And government was releasing this information, but government was
[00:26:02] Akshay Datt: Containment? What's a containment zone?
[00:26:03] Devashish Fuloria: Containment zones were where government was putting these blockages, where there was high intensity of, let's say cases. So they were saying nobody goes in and out of this zone. So they were blocking out certain parts of the city. But,
[00:26:17] Akshay Datt: Which would impact the e-commerce players because they would not be able to do their deliveries and so on.
[00:26:22] Devashish Fuloria: Absolutely. E-commerce, food delivery, everything was dependent on that. Now, government was every day releasing this information in PDFs and they were telling that this area is blocked out. Now, what is the extent of that area? Nobody really understands. So we said that we understand this.
[00:26:40] Akshay Datt: It's not that same problem. That it's not a pin code that they're sharing. So it's not usable by a business because businesses ask customers for pin code,
[00:26:47] Devashish Fuloria: They would block out a building. Now where is that building? What is the extent of this building? And they would come up with definition like this building and 20 meters around it. Now, from our perspective, we know where the building is and 20 meter around it is a circle that we have to draw. So we were able to very quickly read all the government information across different cities, across different states. And within two days we build this very simple API, opened it up to the world saying that, you give me the geo coordinate, the latitude, longitude, I'll tell you if it is inside or outside. That's what we were doing. Within a week 2000 companies had signed up for that API, including Amazon enclosing, including Flipkart, InMobi, all sorts of companies. So we said,
[00:27:36] Akshay Datt: okay. What was InMobi's interest here?
[00:27:38] Devashish Fuloria: InMobi wanted to give separate advertising channels. They wanted to target based on geos as well, so they wanted to understand, a different messaging might work. So a lot of people signed up for this. Many people used, and if you remember people, there was offices started opening in June and July, so then businesses wanted to know if their employees were coming from Containment Zone or not. So they, they started using the, a lot of people started using these APIs.
[00:28:06] Akshay Datt: For an e-commerce company, which is taking my address how do they get the latitude longitude? There is a, like a solution for converting an address to latitude, longitude.
[00:28:16] Devashish Fuloria: They have the lat, long from your device and they also have the lat long from having done past deliveries at that address. And if there was just an address then we would convert it using one of the existing geo coders. So that's what happened. But we knew that it was a,
[00:28:33] Akshay Datt: And this was a free to use thing? You were not monetizing it.
[00:28:36] Devashish Fuloria: It's a free to use. We tried to, but then we said it is ephemeralal this won't go beyond three, four months, but uh, you know, the question is what insights is this giving us? Initially we had thought that people do not have lat longs, and therefore we were giving information on maps. Now when we realize that people have Lat Longs of the users, so we can give answer on Lat Long through an API. Now that became the very interesting start of what Geo IQ is today it's because some of the financial companies had also used that API and then they started asking us that, Hey, but you also have this other data. Can you supply this other data in the form of an API as well? Because for us, user personalization, understanding the user better, understanding the risk better, we need as much debt as we can.
[00:29:29] Akshay Datt: And this, you're talking about lending focus companies like before they lend money, they need to do a risk underwriting and this would help them in risk underwriting?
[00:29:38] Devashish Fuloria: Yes. So now, you know, you think about where people were using it before was primarily for expansion, for doing pin code level analysis. Now they started asking questions on a specific user. If this user lives on a street, what do I know about this user? I know these thousand new things about this user. So that's where we started experimenting with, a couple of FinTech companies.
[00:30:03] We started seeing that hey, this actually starts predicting risk. And this starts predicting incomes. This starts predicting where are you going to face problems in collections tomorrow? But it is all happening at a user level rather than a broad level. Now this started making a massive sense for us because if you're looking at a user level, the volumes are higher because, everybody's dealing with thousands of user. So for me rather than selling maps now, we started selling that, Hey, I'll give you these data points per user for X rupees. That x rupees is a small number, which makes sense to the business. Because it has a massive value for them, and it makes sense for me because at X rupee into, hundred thousand users is a massive bill for me. So now everything started making sense from that point onwards. So therefore, in, post covid, our entire focus shifted to financial services where now all this location information that we are building gets delivered around the user. Imagine let's say you go to a bank and you are asking for a loan, and the bank asks you, Hey, you're Akshya, give me your pan. Give me some, your bank statements this, that, et cetera, Adhaar card, these signals, they're trying to understand you. They would ping bureau in the background. They would do all these. They also ask your address right up front, but because they ask your address, because of that address, now they know, thousand more things about you now.
[00:31:36] And what happened is that in the FinTech boom that was happening in the last couple of years everybody is going out to reach out to the new, to credit user base new to credit user base have very weak information basis, and that's why you're going out to them. These companies also need some bit of information so that this data becomes suddenly starts becoming really powerful. That hey, let's say people have downloaded my app. One person is in Chhattisgarh in some village, one person is sitting in Ahmedabad, How do I compare these two users in real time? And that's where this, locational information started becoming extremely important in the financial systems.
[00:32:21] Akshay Datt: User information based on lat long is like one of course would be maybe that for that specific geography you would be giving some sort of socioeconomic indicator, like this is a upmarket area, or it's a down market area or high density, low density. What gimme an example. What kind of a picture can you paint through just one lat long?
[00:32:40] Devashish Fuloria: So for one lat long, imagine you are in Bombay. And Bombay, we know Bandra West and Bandra East are two different areas. So if I know somebody's living in Bandra West, I, even without going there, I can define that this person is slightly affluent. And the one in Bandra East is maybe mid income. And if I go towards Dharavi , I know it is lower income. This is our common knowledge coming into the mix. Now but even if I go to Bandra West, I know Hill Road is very bazaar kind of area. It is slightly lower income compared to Kata Road, which is extremely high income. So these, there are these minute separation between these places. How this data comes into the picture is like I explained earlier, density of buildings around. So if I know that this is a lat long, this is where the user is within a hundred meters, what is the average rent rate? What are the kind of restaurant, what is the average money people are paying in the restaurants in this area?
[00:33:40] Is this is this a rich neighborhood surrounded by poor neighborhoods, surrounded by rich neighborhoods? So you can do all these things, do all these things with the data that is coming into the mix.
[00:33:49] Akshay Datt: Do, Do you give this as like numbers or ratings a scale or because.
[00:33:54] Devashish Fuloria: They go out as ratings? Earlier I spoke about the machine learning bit, which you rightly pointed out was going out as a consulting thing. We productize that thing, because now we have around 3000 features about each location. I don't know how to choose out of that. Even our clients don't know what to choose out of that. We say, Hey, there is a machine learning tool. All you need to do is throw in the pattern that you definitely know. These are my 1 lakh users, out of which I definitely know these are high income groups and these are low income group users. I want to create a model I under, firstly, I need to understand why, what makes these rich people rich from a location perspective? What makes these poor people poor? And if I understand that, then I can repeat that throughout India. So they create a model. It takes 30 minutes to create a model. You basically approach the data, press a few buttons. It does all the analysis in the background. It generates a report that, hey, locational features are predicting it to such and such accuracy levels. Now you want to use it.
[00:34:59] Akshay Datt: So for a lending FinTech they would upload all the customers who have taken loan, what is the value of the loan, what is the repayment history and therefore, which is a good customer, which is a bad customer, which is a high value customer, which is a low value customer. And that data is read by your system, and then your system is able to, in future, give some sort of a recommendation that this is high likelihood of repayment or high likelihood of default.
[00:35:25] Devashish Fuloria: See, one caveat to working with financial institutions is that data previously is an important item. You can't share information. So what banks and financial players share with us or upload on our system, it's just a bunch of flat loans or addresses and their definition of good and bad. I don't need to know. It's a one and zero for me. Now, that one, and these definitions could be completely different from company to company. For me, it's a one and zero
[00:35:52] Akshay Datt: Is it literally like just good, bad, or is it also like shades between good and bad? Like very good average. Bad, very bad. Like,
[00:36:00] Devashish Fuloria: It can be shades, it can be shades as well. Very simply put it is something like one and zero. Now that question one and zero is different.
[00:36:07] Akshay Datt: It could be 0.5 and it could be 0.7 also. Got it. Okay.
[00:36:11] Devashish Fuloria: And now we start the model gets streamed on our system and then you can use this model at a scale.
[00:36:16] Akshay Datt: So you've trained the model then say because the data is coming to you with this rating of between zero to one. So next time you get a new lat long, you are able to give back a rating between zero to one.
[00:36:27] Devashish Fuloria: Absolutely. We tell them that this is 0.9, 0.1, 0.2. So on day one, they know what quality of user, not on day one in within half a second they know what kind of user this is without really asking any questions.
[00:36:41] Akshay Datt: This is like one use case very specifically for lending companies. Give me examples of other use cases beyond lending.
[00:36:48] Devashish Fuloria: Even in financial institution it is, there are multiple use cases. Now, this one and zero question is quite vast. It is right at the entry point. The same company says, I want to know one and zero in terms of income, because based on that, I will do my product recommendation. The second question that they ask is, which is a different question, another one and zero it is, Hey, if I've given a loan to this person, how risky or how not risky is this? There is another question then they would ask that Hey, I've given a loan to this person. Will I be able to make a collection if the need be? So what will be the collection propensity? So now there are multiple follow on questions. If you think of it even, product wise, for 10,000 rupe loan, a good and bad definition is completely different compared to a one lakh loan.
[00:37:36] So now there are, the number of use cases within these companies are numerous. The good thing for us is that all these decisions are being taken in near real time now. So people need more and more data. And so that's what's happening in lending companies, multiple use cases within lending companies. When you go to insurance companies. Again, very similar. Everything is very similar in terms of that. Now the question that they're interested in, fraud one or zero, how likely is fraud going to happen in the, for, at this particular address?
[00:38:09] Akshay Datt: And this is like for motor insurance and stuff like that, like
[00:38:13] Devashish Fuloria: motor insurance, even health insurance as well. Apart from FinTech and insurance the follow on use cases come in E-commerce. E-commerce has a major problem with returns and frauds and because that's a massive cost for them. And in e-commerce, when you go to the checkout, right at that point, when you type in your address, a model is predicting in the background how likely a fraud candidate are you? Only if the model says that you are highly likely, they say no cash on delivery for you. They'll say, okay, no return policy for you. That flow changes in all these things. Again, everybody till now has been looking at an individual level, at an individual's historical status level, and they would look at, e-commerce, they would say, is this card about to expire or whatnot, et cetera. But what we are bringing into the picture is that the real world idea around it. What does this person look like? And if there are, two fraudulent transactions, is there something common between them in the real world?
[00:39:16] Akshay Datt: What is your revenue like currently this year, what do you expect to close at? Have you raised more funds? Talk to me about the monetization of this platform and the business economics.
[00:39:26] Devashish Fuloria: To start with, I think one and a half years ago, our investors used to question as that, Hey, India is not the market for this. You need to go to the US, because in India, nobody will pay you five 5 lakh, 10 lakh rupees a year for this. Now on an average, our customers on the top customer is somewhere around two crores and without lifting a finger. So that has been one of the major, changes that we have created in the system. It's of value.
[00:39:54] Akshay Datt: And is it based on number of calls? Like number of calls?
[00:39:58] Devashish Fuloria: Yeah. The bigger users would make millions of calls per month. So it is based on how many calls that you are, yeah.
[00:40:05] Akshay Datt: And just to clarify for our listeners, so calls is essentially every time they ask you for data,
[00:40:11] Devashish Fuloria: Yes. Yeah. Yeah. Every question on a lat Long is one call. So that that started happening we have gone beyond a million dollars in a ARR, which is an important number for any SaaS based company. And in the next this financial year, we are looking to cross around 3 million in ARR.
[00:40:29] The pipeline is fairly well established for that. I think the learnings that we took from in the last one and a half years have been very, very critical.
[00:40:38] Akshay Datt: you raised some more funds also right after that initial seed you did in 2019?
[00:40:43] Devashish Fuloria: Yeah. Yeah. In 2020 also, we took some funds from, couple of investors. And last year we got some of the marque names to back us. So Piyush Bansal, Kunal Bahl these guys jumped into the mix as well. So we have yet, we have, yet we are yet to go for a VC round the plan is to establish the revenues, establish all these things.
[00:41:05] Akshay Datt: How did you get such marque names and, tell me about fundraise was it challenging? What, what were the obstacles? What were the learnings?
[00:41:12] Devashish Fuloria: So you would've realized Akshay, because we've been selling a very new kind of product in the market, it takes time for people to understand. So last year when we were speaking to a few investors, we realized that, maybe now is not the good time because we still need to do a lot of work before we get into that hyper growth mechanism.
[00:41:32] Akshay Datt: What percentage of your revenue is currently from FinTech?
[00:41:35] Devashish Fuloria: Almost all. I think 95% is from FinTech, so that is our primary we, we are still working on the FinTech piece itself because there is a large pool of customers that we still need to harness and when you are selling something, which is helping them in decision making in real time the challenge is the sales cycle because You are becoming part of their realtime decision engine. It doesn't happen every day. It doesn't happen that they'll like it and they'll integrate it tomorrow. It goes through many sanity checks. So that is one of the challenges that we are trying to work on.
[00:42:10] Akshay Datt: Yeah. They'll want to do a pilot and the tech team will have to make a new algorithm and,
[00:42:15] Devashish Fuloria: yeah. Yeah. So that part is the bureaucratic part around it takes some time, but I think once you get in, because you are now integrated, you are hard to remove. We are fairly happy with how that has progressed.
[00:42:30] Akshay Datt: Work on them in pattern, like unlocking e-commerce also, because each of these will give you unique learnings and there'll be a certain amount of time the product needs to mature for that use case. And
[00:42:41] Devashish Fuloria: there, there is work happening in the background Akshay, and I think all we are trying to do is not put any revenue numbers into the mix for these different (inaudible) at this point of time. We want to understand those problems we are working in, with e-commerce players. We are working with some experiments around with guys like share chat as well, for example.
[00:43:02] Akshay Datt: Again and what's the use case for share chat? Like personalized news,
[00:43:06] Devashish Fuloria: personalized address news and per sonalized adverts because Share Chat is also an advertising platform now.
[00:43:13] Akshay Datt: Who are your competitors in this space? I've I've heard of this company called Next Billion, which is also into geo location, AI,
[00:43:21] Devashish Fuloria: if you think about globally, there are a few companies which are realizing the potential of location data. Also, 2023, cookies are going to go away. Then location is going to become even more important. But traditionally what companies, I stated earlier, companies like Esri, companies like CARTO in the us, they were taking a very map-based approach, business analyst based approach, and it was like a BI tool
[00:43:48] Akshay Datt: and like more service.
[00:43:50] Devashish Fuloria: It is a product, it's a SaaS based product, but it is, think of it as you're doing BI on maps. So they, they have been taking that route. But now we feel that the kind of route that we are taking where you know, we are not focusing on business analysts, but the data scientists because, our team has a bunch of data scientists. They have seen this problem day in and day out that, you want to involve this data, but you don't want to spend time working on this data. So if it just came in straight away, it would be fantastic.
[00:44:19] Akshay Datt: So is Snapdeal using you, like considering that Kunal Bahl is a investor?
[00:44:24] Devashish Fuloria: some work is happening in the background, so again
[00:44:26] Akshay Datt: yeah. And what about Lenskart?
[00:44:28] Devashish Fuloria: Lenskart is using it as us in a very different use case. They are, think of slightly traditional use case, but in their store identification modules. And their question is that if my ground team is giving me thousand possibilities of stores what should I, where should I go out of those thousand, which 10 are important? So Lenskart is taking, again, a lat long based decisions in understanding the potential of the store. Then building another model to predict the revenue of the store. Then building a, another model to predict what sort of merchandise mix should be there. Again, everything based on locations.
[00:45:09] Akshay Datt: I can understand the revenue predictor because they would be giving you a lat long data for all stores with a zero to one rating on revenue. But for product mix how, how are they doing that?
[00:45:20] Devashish Fuloria: Again you would say that so they have different ranges of products. Some are expensive ranges, some are, so now budget ranges.
[00:45:28] So in certain areas you'd say, is this let's identify if this area is good.
[00:45:34] Akshay Datt: Okay. Like how much does budget product sell here? How much premium products sell over here
[00:45:39] Devashish Fuloria: yes.
[00:45:40] Akshay Datt: Got it. Okay. Very interesting. Are you planning to raise more funds? Do you need funds for growth or is there enough revenue coming in to sustain?
[00:45:48] Devashish Fuloria: Right now we are good, but I think We are looking at that the next year's target, the the 3 million target that we mentioned. And we want to raise funds post that, or at least when that the pipeline is absolutely clear cut, then we'll go out to raise funds.
[00:46:04] Akshay Datt: what do you need funds for?
[00:46:05] Devashish Fuloria: We need to access more data build the capacities to use even paid satellite imagery. Till now we have worked with only free satellite imagery that's an expense. And
[00:46:17] Akshay Datt: Give me an example. What is a, what do you get from a paid satellite imagery?
[00:46:21] Devashish Fuloria: So the resolution is the only thing. So in free satellite images, you'll get a 30 meter by 30 meter resolution. But you can go much finer than that if you start paying for, satellite images. Also we are looking to open the same platform up in the US market as well. And so we have released a beta, which is again being tested by a couple of customers in the US.
[00:46:49] But that will require a lot of work in investment
[00:46:51] Akshay Datt: I guess you must be looking at the world through a lat long lens. Like for every information you hear, like you hear about some crime, you would want to know what's a lat long for it. Or probably you would be wishing that all FIRs come with a lat long and things like that. Like it, it must have changed the way you view the world.
[00:47:07] Devashish Fuloria: Absolutely. The news, the moment we look at news, we see the, where is the city this is talking about can we pick everything and tag everything together so that we get a realistic picture. And very interesting piece on that, the kind of problems that you can solve. We were doing an experiment on American data and so we've found a sample which basically said that this is where the Republicans have win, won. This is where the Democrats have won now. So we threw in whatever data we had built up in the US market into this mix and thought, okay, let's try to predict the Democrat to win using these location parameters. So obviously the the first few impact parameters were obvious. They were race related. They were infrastructural, city or rural. Those sort of parameters came in. But somewhere down the line, there was a factor which said that in areas where the cases of diabetes are high, Democrats don't win there.
[00:48:07] So now this is basically the power of it, you throw every sort of data into the mix. It is not a causation, but it is a correlation. And, if let's say data teams are armed with these orthogonal interactions as well, they can design their product. In this case, they can alter their messaging completely.
[00:48:26] Akshay Datt: what do you mean by orthogonal here?
[00:48:28] Devashish Fuloria: Orthogonal means that you're talking about elections, you think about it and you say, what has this got to do with disease rates? So it is very far off, but when you look at the data, it's holds true for a lot of cases. So now you might go ahead and dig deeper and say that, Maybe there is a reason for this as well, but the first step is that it gets highlighted for you. And, the businesses can make more better sense out of it.
[00:48:57] They can explain it better, why it is important. In this case I think it is explainable.
[00:49:02] Akshay Datt: How is it explainable?
[00:49:03] Devashish Fuloria: I think we have seen that. Sorry, it's not just diabetes. It's diabetes is a factor. Obesity is also a factor. And we see that happening in Midwest, slightly more, not in the cities, cities. you can draw those correlation. Now, how would anybody use it? As, you might change the messaging around your campaigns
[00:49:24] Akshay Datt: Or the photos, the stock images you use, maybe you have slightly more.
[00:49:28] Devashish Fuloria: So if you get the signals, you can do something about it. Otherwise it is very specific to race income, city, demographics, those sort of.
[00:49:38] Akshay Datt: Politics could be a big source of revenue for you, helping parties to decide where to campaign because parties spend so much money on election campaigning. If they got this intelligence that focused more efforts on this ward, this city and so on, like that could be a real game changer.
[00:49:54] Devashish Fuloria: Yeah. Yeah , but who wants to deal with politicians at this point is a question ,