ClimaCell

Great. So I'm Rei Goffer. I'm one of the co-founders and Chief Strategy Officer of Climacell. I started Climacell with two friends of mine, Shimon and Itai, who were both, like me, coming from Israel, and a very long military service, in the Air Force and Special Forces.

And basically, all three of us had this pain from our service, which is not getting accurate high resolution weather data in the way we thought you can get it. And you know oftentimes it was just annoying, and maybe messing up with your day plans a bit, so changing flight schedule or stuff like. But then some other places it can be really dangerous and scary.

And so we met here again in Boston about three years ago and decided to start a company around it. All three of us were doing our MBA's here, at MIT and Harvard, and that pretty much first week of school we were like, OK, what do we do that's not-- It's kind of boring, we need to start a company. And we started Climacell literally the first week or second week of school.

And doing that while at school was actually a great experience because we got endless support from both schools. And in MIT, specifically, we got into the Legatum Fellowship. And we got a lot of support from different faculty at Sloan and in other departments at MIT. And we're still in very good connection with many of them til today.

The way weather forecasting is built, traditionally, is that you have data, so the observations. And these observations are predominantly generated by dedicated sensors, or so things that were built and designed for the purpose of sensing the environment. And these sensors, like satellites and weather stations, these sensors are very, very expensive. And so what you see is that they exist, but mostly in developed countries.

And everywhere else in the world, which is basically where billions of people live, you don't really have them. Or you have very few of them, they're not really connected. And as a result, because the data is not good, then the forecast and all the predictions that are based on this data aren't good as well. So in places like India and Brazil, and obviously Africa and Southeast Asia, you have quality of weather forecasting that is lagging decades behind what we see here in the US or say Western Europe.

The way we solved this problem is looking at it from a completely different angle. And saying, instead of actually building and deploying more sensors, which is something that would cost billions of dollars, we're looking at how we can repurpose existing things that are already out there in the millions, and basically turn them into weather software using just a software approach.

So one example is that we're using wireless signals. So wireless signals in cellular networks and satellite networks, and turn these signals into weather data, because these signals are actually sensitive to the weather. So when it's raining, for example, you can see subtle changes in the signal. You as the user never see that, because your device compensating for it, but if you look at the signal itself you can actually extract very valuable information from it. And there is no hardware deployment for that, you basically tap into existing networks that also exist in very underdeveloped places of the world.

[MUSIC PLAYING]

REI GOFFER: Weather touches pretty much everything, right? Every industry is sensitive to weather in one way or another, and of course, we as individuals are very sensitive to weather.

The thing with the developing world is that most communities in the developing world are actually more sensitive to weather than we are here in the US, because a, they have less physical protection, b, they have less financial protection. So there is less developed insurance products in these countries. Typically their economies are more based on agriculture, and agriculture is more rain fate.

So all of that together is basically saying that these people are much more sensitive to weather, and they get products that are 40, 30 years behind what we have here today. And the ability to solve that problem, bringing cutting edge weather forecasts into developing places, and solving emergency alerts, bringing better flood predictions, bringing better data to farmers, this is what keeps us excited.

JetBlue is one of one of our costumers, and actually is also an investor. They saw the product and they decided to join the company as an investor. We're serving them all over their US operations, many other airlines as well. We can talk about the Patriots actually, it's a different kind of use, it's a sports team, but they're also very sensitive to weather. Every rain out decision is millions of dollars. Every game that they know the weather better than their competition, they can benefit from that. So that's just a few examples.

So at a very basic level, the way it works is that we first generate better data. We observe the environment better than anyone else. We have millions of new sensing points. With that data, we can now run much more accurate forecasting models, so we have a better prediction of how weather is going to behave in the next hours, the next few days. That's the engine.

That engine is then feeding different types of applications. For example, in aviation we have a dashboard that's built around operating a hub for a big airline, and it has all the data that if you're operating a hub, that's what you'll need. So you have the flight data there, the ground movements, all the delays, et cetera. If you're a sports team, you have a tool that is enabling you to send push alerts to the different stakeholders, vendors, et cetera, through that management system.

We also have a consumer app and a mobile app, that if you're just someone out there, not part of a big company, you'll still get access to this data. Think about micro forecasts, the difference between someone telling you there's a 40% chance for rain this afternoon over Boston, to someone telling you rain is going to hit your building at 1:37 and at 1:53. That's a difference.

Now how does it impact? If you think of the ridesharing company, they want to know exactly when demand will peak, where, how many drivers are going to be available, what's going to be the impacts on traffic, right? That's just one example. Or any type of on-demand service, basically.

Now think of another example, energy space. Your demand for energy from the grid is very, very sensitive to your very micro level weather. If it's hotter or colder around your block, you'll consume less energy than the other block. Knowing that data down to that resolution enables now the utilities and the grid operators to optimize even better and save millions.

[MUSIC PLAYING]

REI GOFFER: So I think what separates us is we have a very different DNA than any other weather company out there. We were not coming from the same place. We're not a group of meteorologists who just tried to add some incremental changes. We look at things completely differently. The fact that we're the only company in the world that is using software-based sensing and have millions, literally millions of sensing points that nobody else has, we have a much better understanding of what's going on in real-time. And with that, you can just do phenomenal stuff. So you can do better modeling and you can definitely deliver better products.

I guess the second piece is that our partners is what really makes us unique. So we have Ford Mobility as an investor in the company, as a partner for development. We have National Grid, which is one of the largest utilities in the world, as an investor and a partner. We have JetBlue as an investor. So in each of these industries we have a very strong player that is actually working with us on developing solutions for that industry and then taking them to the market.

What we look for in partners are companies that believe things can be done differently and have the right mindset for startups and for disruption. So if you're just looking to minimize risk, and you're not willing to bet on a new technology, you're probably not the ideal partner for us. We'll get to work with you at a later stage. But if you understand that things move really fast and you want to be part of that change, then you're a great partner for us.

And we have such partners in the mobility space, in aviation, in the telecom space, in government, even in energy. So there are big companies today that understand that the pace of innovation is different and the best way to be part of it is to actually partner with companies like us.

I guess our biggest challenge right now is just growth, we're growing really, really fast. And making sure we maintain our culture, which is unique and is really what brought us to that point at such velocity. Containing that velocity. And I guess the second one is just focus. We have endless opportunities everywhere in the world, every type of industry, small businesses, enterprise, everything. So now, deciding what's the most valuable opportunity right now and what we can maybe address a week later is the biggest challenge. We're trying to do pretty much everything.

We have, I guess, a very unique team. Pretty senior people, not necessarily in years of experience, just in caliber. And we really believe in bringing really strong people, not being afraid of bringing super strong people and of dilution of responsibility. Actually we look at it the other way around. We want to bring the strongest people we can find and empower them to then go and just build things that we didn't even think about that will just grow, because that's the only way you want to go.

If you need to be in 20 different countries over the next year, develop five new technologies, and penetrate into three new verticals, that's the only way to do it. So our team is comprised of people from the best universities, on the R&D side, from MIT. The majority of them, actually. A lot of folks from the IDF like us, but also from the US. A lot of folks from the big tech companies and other really lucrative brands, but just also people that have a proven track record of doing something impossible.

Two years ago, we were three people with a PowerPoint presentation and nothing else. We are 70 people today. We raised $70 million to-date. So that's the trajectory over two years. And we have partnerships, as I mentioned before, across pretty much every industry sector and many different countries.

We believe that the path to growth is continuing on that partnership road, and we today are working on partnerships with a few of the largest companies in the world. In the tech space, in energy, mobility as well, and some really big governmental institutions around the world. That's the way to grow.

For us, being part of STEX25 is a great opportunity. We looked at the list of companies that are engaged with MIT, we have something to do with pretty much every one of these companies. And having the opportunity to get access into each and every one of them is great. Being industry ready, we're serving customers from day one, paying customers, really big companies.

We believe that the right way-- once you have a partner that is a really big company, and partnered with MIT-- say what the solution is and we can build it. We can build it fast enough for them to play with it when they need it.

My message through to the ILP companies is that exactly the reason you're partnering with MIT is to get to know companies like Climacell, and get to know technologies like the one we're developing, and be ahead of the curve, ahead of your competition in adopting them. We're very fast in executing, and very creative when it comes to building partnerships with really big companies, that's also in our DNA. So we're not afraid of navigating between different stakeholders in big companies and structuring the right partnership that can actually work for everyone.

[MUSIC PLAYING]

REI GOFFER: Sure, so the way we look at our products, basically, the base is the API, that sort of machine-to-machine interface that's making all the data from our engine available to your software. So if you're developing an app, or if you're running an algorithm for navigation, or for a ridesharing company, or if you're an insurance company and you need data feeds, the API is the solution for you.

And that API is very unique. It offers not just the regular data sets for weather, but it also offers visual tiles. So you can actually layer our unique precipitation layers, or wind, or whatnot, on your apps. So for example, if you have a drone flight management system and you need to show the weather on that map, we do that rendering work for you, just take the right pieces from the API, and so on and so forth.

This API now is being integrated into really big navigation apps that are in everybody's vehicles. Again, flight systems, drones, all the way to finance and hedge funds and these domains. So that's the first layer, the API.

Then on top of the API we have different types of products, of visual products, starting with B2B dashboards. So vertical, specific, both web and mobile solutions. So think of again aviation, starting with HyperCast aviation, that's a product suite for airlines. Starting with the hub, moving to the headquarters, and even the cockpit, that is giving you everything you need to know in that specific chair.

So if you're running, say, Logan for JetBlue, then you'll have all the data you need in that software. All the best weather data out there, with all the flight data. And then all the business intelligence that sort of merges these two data sets together. So things like, what's going to be the delay? Or how much time will it take me to deice? And, how many airplanes can take off and land in the next hour? All these answers you can get from that solution. So it's not just the weather, it's actually how the weather impacts me. And that's the entire thinking behind HyperCast and the B2B solutions.

The second type of products is consumer apps. So starting with a consumer app, which we're launching very soon, early next year, which will then be also integrated into our other day-to-day apps. So you know our calendars, and our assistants on the mobile phones, and sports apps and all these kind of stuff.

We work with a lot of ridesharing companies, we can talk about two of them. There is Via, which is a really big ride sharing company out of New York, and they rideshare taxes. And the way they work with it is basically it tells them, again, where demand is going to be, how their roads are going to look like, what the day is going to look like. And then they can actually take real-time routing decisions based on that and then do all sorts of other stuff with it. You can ask them about their algorithms and all that stuff.

Another example is Bird, so the scooter sharing company. And for them, again, the weather is the make or break of the day. If it's raining, nobody will ride scooters and they'll have to move them from one place to another. So being able to understand city, by city, street by, street, hour by, hour, minute by, minute, what's the weather going to be like? Will we need to take our scooters from point a to point b or should we leave them there? Are people going to come take them? What's ridership going to look like over the next few hours? These are the questions we're helping them to answer.

[MUSIC PLAYING]

REI GOFFER: You know, the really interesting thing about agriculture in the developing world specifically is around insurance. So in the US and in most developed countries, 100% of the farmers are insured, meaning if they lose their crop for some bad weather, not enough water or not enough rain or too much rain, they'll get compensated by the insurance company, and they don't lose their livelihood for that year.

That's not the case in the developing world. Uptake for crop insurance in most developing countries is 15%, in some cases even lower. The reason for that is that deploying crop insurers in these countries is very complex for the insurance companies, because they lack a lot of data. Insurance is always based on data. So the more you know about the history of the weather, the history of crops in the real time, what actually happened, the better products you can offer, the lower premium you can offer, and the more people you can attract.

What happens in the developing world is that you have such scarcity of data that insurance companies either don't even offer these solutions, or they do, but at prices that don't make sense for anyone. And a big part of it is weather data, because crop insurance is all about the weather.

And so if you can move from say having 1,000 sensing points in a country like India, which is more or less what the government has and what the crop insurance industry there is relying on, to having 100,000 sensing points, which is what we have, all of a sudden, you move from every farmer being maybe 20, 30 kilometers from their nearest weather station, which is not good, it's very far, to every farmer being maybe one kilometer from the nearest weather station, which means the weather data that the crop insurance company will have is very, very relevant to what actually happened in that specific farm, versus something that's 20 or 30 miles away. It can be completely different weather.

So reducing that friction, reducing that uncertainty can help crop insurance products become much cheaper and then increase the penetration and actually save a lot of money and help millions of farmers across the globe.

So precision agriculture is a big buzzword, and everybody is talking about it. But again, to make it work, you need data, because you need to know if it rained or not, and if it's going to rain or not before you irrigate. Again, today you cannot do these kind of applications in most of the developing world, which is where most of the food in the world is actually being grown, most of the crops.

We can enable these applications, again, because once you add this super high resolution weather data into the precision ag applications, now all of a sudden, you can make much better decisions on when do I apply fertilizers, when do I apply herbicides, when do I irrigate. And then your yields increase, your efficiency increase, and you reduce, again, the risk and the friction of very underdeveloped food systems.

It's a long process. It's not going to take one day. So it's going to take us a couple of years to really implement all these solutions. Because we understand that it's a long process, we started a lot of things really early, and all these long processes are already in the making.

So we have proof of concepts and trials and even paid customers in many of these verticals I just described, specifically in crop insurance and in precision agriculture around the globe. And you know, one, two years, you showed that it's working. And you have several, say, thousands farmer using it, and then the next year is can really boom. So that's the goal.