Please welcome Senior Manager, Investor Relations, Cleo Palmer-Poroner. Hello, and welcome to Planet's 2023 Investor Day. We're so glad you could join us today. Now, before we jump in, I'd like to remind everyone that today's presentation contains forward-looking statements and statements about our long-term targets and goals. Forward-looking statements are subject to risks and uncertainties, as detailed in our SEC filings. We encourage everyone to review our filings, which are available on our investor relations website and the SEC's website. Additionally, the slides from today will be made available on Planet's investor relations website following the event. We encourage everyone to review the disclaimers included in the accompanying slide presentation, as well as our filings. At this time, I'd now like to hand things off to Co-Founder and CEO of Planet Labs PBC, Will Marshall. Hey, thanks everyone for coming. Welcome to Planet. It's really great to see you all here. We've got an action-packed lineup for you all today. Here's the agenda. So I'll kick things off. You'll hear about our product and our go-to-market focus from Kevin Weil, our President. You'll hear from Robert Cardillo, who's in from his native D.C., to talk about some of the insights about in the defense and intelligence business. Andrew Zolli, sorry, ditto for sustainability, and he's just back from a week at the Climate Week in New York, so he'll have some stories from that. And then Ashley will round out the day with our financials and our path to profitability. Peppered in here, we've got a couple of demos, three product demos, including one on our new Sinergise platform, the company we acquired over the summer. And then there's four customer discussions that you'll hear firsthand from what customers are seeing value in our products and services. Perhaps most of all, you'll get a flavor for the team here at Planet and the quality of the team that I'm proud to be part of here. And also, for those of you that haven't seen the lab, for those in person, you'll get to go downstairs and see, see our lab. It is really cool and one of the most distinguishing pieces about Planet, and I think it's hard to understand Planet without seeing that, so you'll really want to see that. Before I get going, I want to acknowledge two things. Firstly, the heartbreaking events happening in Israel and the Gaza Strip. We're working with our crisis response to try to do our best to support customers and partners, our governments, media, and humanitarian organizations, but out of respect for how early and sensitive those events are, we're not gonna be talking further about that work here today. Secondly, I want to acknowledge that it's been a challenging year economically. We've faced some headwinds here at Planet. Of course, we're not the only ones facing headwinds, but the environment has affected us. For Planet, it really comes down to two things. Firstly, execution. We have a huge stack of opportunities for our business, and it's our job to go and execute upon them. Today, you'll be hearing about our focused execution. Then secondly, Planet's business is still really not fully understood. The market hasn't understood what kind of business Planet is, and you'll hear more from me shortly on that. And it's also not understood what products we're providing to customers, and you'll hear from some of our customers about the value that they get. The opportunity in all this is that we are becoming more efficient and focused as an organization. It's making us better. And the opportunity is also that there's huge value to create for our shareholders. So overall, there's three messages we would like to convey here today. Firstly, that we're focused on durable growth towards this massive market opportunity we have in front of us. The tailwinds driving Planet's business are tremendous. I'll talk about some of them, sustainability, peace and security, the digital transformation. These are huge opportunities to go after. Second, we're focused and getting more and more efficient in our path to execute upon them, both on the product side and the go-to-market side. We've made real product improvements. We're making real go-to-market efficiency improvements. And finally, AI. AI is a real accelerant to our business. There's a lot of hype about AI. In our case, it's really real, and it is providing critical value to our customers today and is a significant market opportunity for Planet in the future. So I'll talk a little bit more about that. So I'll talk a few slides on each of those three points. First, before we get into that, I want to start with a little bit of a background on Planet, for those of you that are new to Planet, but also hopefully a valuable recap for those of you that have followed us for a while. So Planet, when we founded, set about this mission, to image the entire Earth every day, tracking changes, making changes visible, accessible, and actionable. And when we went public, in 2021, we did so as a public benefit corporation, because we believe what we're doing is really critical, and we want to bring it about in the right way. We also believe that our business and our impact are highly aligned, and that's important, and that's reflected in our PBC. Our daily scan fleet of satellites is what does the legwork for this. It's unique and remains in a class of its own, and that is what's opening up the new markets that Planet is entering into. And Planet, at a glance, you can think of it in these ways. We have these two powerful datasets, the daily scan I just mentioned, and then the high-resolution tasking system. We have a large addressable market, each one of these areas being billions of dollars of market opportunity to go after, and we've got a highly scalable business opportunity. That is, each image that we take can be sold multiple times to multiple customers. So the direct margins of doing so are very, very high. We're a software-like business in terms of the financials as a result of the one-to-many nature of our data business. I find a useful analogy here, I've spoken about this before, is a Bloomberg Terminal, but for Earth data. We deliver customized information streams to enable our users to make smart decisions, and that's why the analogy to Bloomberg makes sense. It's not perfect. Different from Bloomberg, we have a proprietary dataset. They mainly aggregate public source, stock data, and so on, whereas Planet, of course, has its own proprietary data. We also... They also primarily serve finance, whereas we serve a wide variety of use cases, so in those sense, they're different. But just like Bloomberg, data feeds enable people to make smarter decisions, and our data feeds go deep into people's workflow, into our users' workflow, making them very sticky. I wanna talk a little bit about what kind of business Planet is, and really, it's a data platform business. So I think that that's the way to think about Planet. This is a point that's often misunderstood. Let me be clear about what we're not. We're not an aerospace company. Our satellites are incredible, and they provide an infrastructure that generates a unique and proprietary dataset, but we don't sell satellites. What we sell to our customers is data subscription feeds. You know, comparing us to a satellite company is like thinking of Google as a server company. Google are really excellent at making servers, but that's not their product. Their product is search and other things. What we're closer to is a software company, SaaS company, but really, we're a data platform company, and there's a-- there's some subtler distinctions between data companies and software companies, and that's the point of this slide. Data businesses are rare, so you'll hear less about them. There's thousands of software companies, of SaaS companies, but there's only a handful of data companies, and that's why it's worth noting a little bit about. But when they're successful, they're very notable companies such as Bloomberg, that I just mentioned, but also Google, MSCI, and others. They are all really data companies. So that's the first feature about them. A few other features I would call out. Secondly, it's all about the data. It's about gathering and accumulating a necessary corpus of data that's distinguished and differentiated from others, and that's often very hard for companies to go build that, in our case, launching a whole fleet of satellites to get it, right? But once that point is reached, which comes to my third point, there's various really huge barriers to competition, and fourth point, those benefits accumulate over time. So as we add new datasets, for example, it's often one plus one equals three. And there's network benefits of those, of users on our platform. There's data gravity benefits of people on our platform, so there's compounding benefits as we get going. So that's a distinction of data companies and software companies. And finally, they're very sticky. I mentioned this point. They're very sticky flows. I would add a further point that, you know, the AI and data are really best buddies, but data is king, and I'll talk a little bit about that when I get to AI. So that's a little bit about who we are. Now, let me talk a little bit about the scale of the opportunity. These are really the fundamental three tailwinds driving Planet as a business. The first is digital transformation. This is continuing to drive the adoption of new datasets and software solutions by various different markets, most large-scale industries. This is basically about helping customers across industries make digital efficiencies, optimizing revenues, reducing costs. This is... A canonical example of that is our work in agriculture, where we help farmers and agriculture companies improve crop yields and decrease use of fertilizers and things like that, so increase profitability. That's digital transformation of the agriculture sector. Then, there's sustainability transition of the economy, and it's an increasingly pressing matter. To just give one example, and I'll show some slides on this later, it's in disaster response. We just had the hottest summer in on the Earth's history, or at least in modern era, and it's leading to myriad climate-induced extreme weather events. The U.S. alone spends hundreds of billions a year responding to those sorts of natural disasters, and this can be reduced by quick response, which we can help with, and also, even better, preventative work, which our data can also help with. And then finally, the need for in the area of peace and security is growing, too. We've got an increasingly polarized world. We've seen the war in Ukraine increase awareness about the need for global situation awareness, transparency, and accountability of events around the planet. Let me make the opportunity a little bit more specific through this by sharing this third-party assessment that was done of just the civil use cases, civil government use cases within the E.U. context. They did analysis of what Planet's data could do in domains like disaster response, agriculture, and so forth, for the E.U. And just in their context, they found that they expected it to generate between EUR 5 billion-EUR 14 billion of benefit to their users in the years 2022 to 2030. A significant ROI, huge number of jobs just on the back of Planet's data. Now let me turn to some of the execution. And we're really proud of some of the developments in the last 12 months, but I'm most excited to start with an announcement about this guy, which is our first Pelican spacecraft, which I'm pleased to say has arrived at the Vandenberg Air Force Base for launch next month. So it's a major milestone for this program. We announced this program two years ago. The high-res continuity and increased capabilities of Pelican have been really exciting. It was announced two years ago. That's incredibly fast, and we have our first demo on the launch pad today. I'm incredibly proud of the dedicated team that has got us to this point using our Agile Aerospace approach. As this is really a tech demo mission, so it'll be testing out the integrated design of our platform, and we'll learn a lot from the mission. As you may recall, the Pelican is our replacement for the high-res system, the SkySat system, but it's also gonna be more capable, higher resolution, higher revisit rates, lower latency, and less expensive. And getting to this milestone is a huge step. So that's the first thing. But followed closely behind that is our first Tanager spacecraft, using the same spacecraft bus that's going up. And we've got an exciting update for that, too. The NASA JPL developed payload, the hyperspectral instrument, is not just in our lab, it's integrated into the first Tanager spacecraft downstairs in the lab today. And that's scheduled for launch next year. For those of you that are not familiar, Tanager is our hyperspectral constellation, and it has over 400 spectral bands, capturing many phenomena that the human eye can't see. The Tanager has been funded by our partners at Carbon Mapper, and the main mission is mapping the world's CO2 and methane point source emitters around the Earth towards our sustainability mission. It also has many applications in advanced intelligence, agriculture, biodiversity, and other things. It's really a new area and a new field, because really, there hasn't been commercial hyperspectral instruments before. And I'd be remiss to talk about spacecraft without talking about our flagship fleet, the SuperDoves. It seems run-of-the-mill, but we continue to improve and update that fleet. We just built 36 more satellites. They are also at Vandenberg on the launch pad, and this is run-of-the-mill for us to build these things. But just to re-remind everyone, this is the largest fleet of Earth-imaging satellites in human history. It produces our proprietary daily scan of the Earth that is the backbone of our imagery archive and the backbone of our commercial opportunity. And these spacecraft are truly in a league of their own in terms of cost performance and speed to manufacture. Again, for those of you that are in person, you'll see that downstairs in the lab today, just how special that is. It's a completely unique capability in the world. Here are some of the product milestones. Let me talk through some of the major things happened over the last 12 months. I've talked about the first two already. I'm not gonna go through all of these. Kevin is gonna talk through many of them, but I do want to touch upon the Planetary Variables. It's really exciting that this is enabling all sorts of new customer opportunities. I'll be speaking at the end of my section with someone from Swiss Re, who is using one of our Planetary Variables, and you'll get to understand that a little bit more. I wanna touch a little bit more on the Forest Carbon Planetary Variable that we announced this summer. They'll be coming available later this year. This is a groundbreaking dataset that will provide information about forest change and carbon capture at the nearly individual tree level on a quarterly basis for the whole world. That is gonna enable it to... this data is what will underpin carbon markets, conservation, and regulators in this space. Frequent, broad-area, yet granular data are crucial for making this transition. I want to underscore the significance of this. The sustainability transition of the planet—of our economy, because of the planetary boundaries, is a multitrillion-dollar transition, and it's not just that our dataset is relevant to it, it is foundational to it. In this case, I firmly believe that companies and countries across the world will have to balance their carbon books just as they have to balance their financial books. And in fact, regulations are already moving in this direction, both here in the U.S. and the E.U. And the first step towards balancing your carbon books is gonna be measurement. You can't do it without measurement. So this is a critical capability to underpin the carbon markets, to make them real, and it's towards this multitrillion-dollar transition of the global economy that's necessarily coming to us. Let me also talk a little bit about recent customer wins. Over the last 12 months, we've had a load of new customers. Here are some of the highlights. I'll just touch on three. The NRO, this is one of our strongest partners, as you probably all aware, through our EOCL contract. But this new win was to do with the hyperspectral data from our Tanager program. So they're exploring data there and synthetic data, and it's really exciting to have them on board with that new mission. Secondly, I want to touch on the U.K. Rural Payments Agency. So that's a civil government agency looking at use of our data for sustainable agriculture monitoring in the U.K. So that's a really cool use case, and it's very rich and repeatable to other governments around the world. And then finally, I want to mention PG&E. So PG&E are using one of our Planetary Variables that enable them to monitor power line encroachment of vegetation. In an automated way across thousands of kilometers of power lines. And this is rinse and repeatable to other utility companies around the U.S. and across the world. I gave a more general sort of business opportunity before, but I wanna give you a sense of a specific customer ROI. And I've called out this case 'cause I found out about it recently, when I met the Indian Prime Minister, and we discussed the state of Odisha, who has been using and implementing a program of subsidies involving more than 1.5 million farmers just in their state. And the initial program rollout was stymied because there was all these middlemen that caused massive amounts of fraud, claiming subsidies on behalf of farmers and various other things, and they needed a verification system, basically. They used our data, and it's been an incredible example of... Really, the results speak for themselves, which you can see there. Already in the first year, they saved $200 million worth of savings on their subsidy program. They basically eliminated the fraud and the middlemen from this program, across these millions of farmers. It's pretty cool. I wanna end by talking about AI. There's a lot of hype about AI, but for us, it's real. It's an accelerant. That's the bottom line. That's the way to think about it. It's a huge moment, obviously, so let me just step and talk a little bit about that. The pace is something we've never seen anything like, and the value to Planet is it's unlocking the value of our data. It's the ability to pull out and extract our information from our dataset. Our dataset is particularly suited to AI 'cause our daily scan is a consistent database going back many years to train the models on. And I wanna be clear that AI without data is useless, and data without AI is very useful. So data and AI are best buddies, 'cause AI can help extract value out of the data, but it's clear who's king. I'll note that all the tech titans are releasing code in AI, but none of them are releasing their data. They all know where the key asset is at. Everyone can train their AI on data, the text of the Internet. But proprietary, differentiated datasets is where it's at when it comes to AI. That's where it's at in terms of solving important problems, and Planet has one of the most compelling datasets for solving important challenges around the world, with which to train AI upon. Now, this basically the AI is enabled by our data, as I said, and it comes from this huge moat, and here's some of the stats about the tremendous amount of data we collect. I'm most excited about the last one. There's 2,400 layers of imagery for every point, on average, around the Earth's land mass, and this is a moat that grows with every turn of the Earth. That's what really differentiates us. Not only do we have many times more satellites than any other company, or Earth imaging satellites than any other company, but we also have orders of magnitude more area coverage than any of the incumbents in our field, and this is what unlocks the tremendous value in various markets, from agriculture to others. I also want to be clear that the AI opportunity is not new to Planet. It's something we've been building out for years. We knew out of the gate that we had too much imagery for the human eye. We were gonna need analytics. And it stood to reason that we would need analytics. So this is something we've been building out for the whole history of Planet. And here's some of the major milestones. On the top, the AI milestones, and on the bottom, the Planet milestones. In the AI side, at first, you know, the major development there, one of the early successes of CNNs in the. It was in the field of computer vision, enabling the identification of objects in imagery. First, it was cats and dogs in pictures online, if you recall that sort of phase. But that core technology, of course, could be applied to satellite imagery, to find objects in there, and that's exactly what we did. And we set out in 2018, the vision of what we call Queryable Earth, which is basically taking every image and identifying all the objects in it, the roads, the buildings, the ships, the planes, and in effect, indexing the Earth. So just as Google indexed what's on the Internet to make it searchable, we're indexing what's on the Earth to make it searchable, and suddenly, with CNNs, that was becoming possible. But in preparation for that, we actually did some really important things. One is we redesigned our satellites so that the scan fleet to be what we call AI first satellites. All the extra spectral bands that we added were for machines, not for humans. The first fleet of AI satellites. And we really were pioneers in another thing, which was standards to do with analytics-ready data for the industry to adopt, so that all the pixels are self-similar and calibrated and so on. And then on the back of these AI developments, the next thing that came along was the Transformer model, which then led to all the large language models and what we've heard with ChatGPT and so on. And what they are doing, in short, is a massive accelerant, even to the CNNs, in terms of getting to our Queryable Earth vision. And this year, 2023, we really was the first time this, all of this stuff came together. The use of generative AI models developed by our partner Synthetaic, in particular, on top of our data set to locate this high-altitude spy balloon across the U.S. That was the first time it all came together, but now we're already seeing some really fascinating use cases beyond that. Generative AI is technology that, you know, barely existed six months ago or a year ago and is transforming whole industries. As you can see, it's important for Planet, but we've been preparing for it for some time. Now let me show you a little bit about the difference in practice. Here's a couple of real case examples. This was some of our work with more what we call classical AI in a real case of the terrible fires that swept through Lahaina in Hawaii. We had images before and after, and we had trained that building damage assessment tool for Ukraine context with our colleagues at Microsoft, and we've quickly applied it here in the context of Lahaina to do building-by-building damage assessment that was available within hours to first responders on the ground in Lahaina. But this was based on a model that actually had taken months to develop this building detection. The difference with large language models, and I'm just gonna flash up this one slide, was one of the first use cases of it, is that you train once, and then you can search for anything. And it's really hard to grapple with that out of the gate, but it's incredibly powerful. So you train a model, then you can look across large regions for anything that you've decided in that moment in minutes. Here in China, there's results from a few of our quick searches for ships, for solar farms, bridges, you name it, just across that entire region. We did it as just a test case. And you can imagine how important and powerful it is to have individual analysts able to look at large regions for new events and so on, whether it's in the area of disaster response or peace and security and so on. And yet, large language models are really, not only able to do this, but to do it with low compute. So we were able to do this with a small model. So once you train it, it's quite low compute, which means you can do it many times with low cost. And this is really just the beginning in a way, because large language models are now moving to multimodal versions, where they can be experts in text, video, audio, et cetera, which enables them to get meaning between them. And then, so let me just put it in the context of one of my earlier slides. I mean, one way to think about it is that the AI is enabling us to easily query that data stack, right? And it is enabling us to get our SAM closer to our TAM, if you like, in terms of who we can get value to, because people that were traditionally had to be experts in satellite data now no longer have to be. And then, because of the point I was just making, it enables more queries faster and a bit like, a one-to-many imagery model, this enables a fundamentally, a sort of course analogy, a one-to-many query, model. So there's a lot of margin in that potential there. So that's enough about AI. I'm gonna just, end by coming back to where we started today. We can't talk about all of that exciting and incredible progress we've made without talking about some of the... and addressing some of the recent headwinds we have and what we're doing about them, more importantly. So we've... You know, I think really the way to think about it is that we have this massive opportunity, set of opportunities in our pipeline, but it's our job to execute it. Pipeline is not revenue. We have to turn them into contracts. And so we did over the summer a lot of focus work to focus our teams on the large growth opportunities. We reduced our cost infrastructure. And my last slide is just gonna be on where we're focused going forward, 'cause we're very clear and focused across our executive team, and you'll hear from some of them today. On the product, we're developing our next generation of high-resolution fleets. You've heard about the Pelican. We're scaling our platform. We're accelerated by the Sinergise acquisition, and we're unleashing AI onto the PlanetScope data. On our go-to-market, we've focused our direct sales teams on large customers in our core vertical markets only. We're serving all the other vertical markets via our global partners, and we're shifting the smaller customer opportunities to automated self-service platform via the Sinergise platform, which both automates that for those small deals, but also frees up our sales reps around the world to focus on the big deal opportunities. And finally, on the financial side, we're focused on durable and efficient growth. We made a commitment to making, getting to Adjusted EBITDA profitable by the end of next year, and we're focused on building a high-margins business, delivering sustainable cash flow. You'll hear more about that from Ashley later today. In all, I feel we're very honed on scaling the opportunity, and challenging though the economic headwinds have been, is making us a better business. So in closing, let me come back to what I said at the beginning, which is, the three things I'd like you all to take away from today. First, we're focused on delivering towards our high-growth opportunities, towards our huge market. Secondly, we're focused on getting more efficient and focused, like I just mentioned. And thirdly, AI is an accelerant to our business. So with that, thank you very, very much. So with that, it's my pleasure to turn it over now to our first customer conversation, which is gonna be with Marcel, the Head of Agriculture Product Center at Swiss Re. Swiss Re use case is really cool. They've been doing automated parametric drought insurance, protecting farmers around the world, leveraging our soil moisture content, Planetary Variable. Marcel is helping to pioneer this use case, and I thought it'd be really fun for people to hear from this use case. So, he has a few slides to share, and then we'll have a brief chat. Marcel, are you there? Over to you. Yeah. Good, good morning. Morning. Or evening for you, maybe. Yeah, evening here. So good to see you. Let me tell you something about drought insurance around the world. I'm representing the company, Swiss Re, and I'm part of the agriculture department. Next slide. So Swiss Re, who we are? We are the number two reinsurer in the world, and we write around $44 billion of premium, and we insure almost every insurance company that there is in the world. We're based in Zurich, but also have a lot of offices around the world. But to be honest, I represent a small part of that. With only 30 people, we are focusing on agriculture, and here we have around 400 clients around the world. Next slide. I have a bit of echo. What I'd like to show you today is about drought insurance. So drought insurance is a super important topic for farmers, but of course, also for the world's population in overall. So what we see statistically is that the number of droughts, heatwaves, is increasing around the world, and that every year we see a lot of news headlines about different parts of the world where there is problems with drought. This is increasing and there is honestly no end in sight of this trend, and it's our task to make this insurable or to keep that insurable for our clients. Next slide. So what's the difficulty for us with drought insurance? So how classically, drought insurance works, in a non-digital way, is that you send a loss adjuster from the insurance company, he goes to the field, he looks at 1 by 1 m of the fields, he looks at the corn, he look at the wheat, and then he estimates what is the loss, for that farmer. This works relatively well for around 20% of the global fields, but now, there is a huge demand from different organizations, governments, farmers around the world to also ensure the other 80% of crops around the world. To do that, we need an accurate, objective, transparent way, to estimate what is the loss of the fields. The easiest way to think about this, what we call index insurance, is, for example, with a weather station where you would do a payout in case if, for example, there is, within the summer, less than a certain amount of rain, which you see here at the bottom. So if it rains less than 100 mm, you will pay out. Now, this is in a way, is a bit too simple. So what we have been looking for in the last 10, 15 years is a solution that can help us to do this more accurate, in a more global way. And with that, we landed with Planet. Next slide. So, with which data set we are working with Planet? We were receiving a data set called soil moisture. This is one of these Planetary Variables we all talked about before. So we get from Planet globally, real time, every 100 by 100 m, every day, we get a data point for how much soil moisture there is in the first 10 cm of the soil. Now, what's so great about this solution? It's global, it's high resolution, but the best thing for us, it works with radar satellites. So even if it's clouded, we can see through the clouds, and we can still see what the soil moisture is and what the payouts for the farmers should be. Next slide. So maybe to go a bit more in detail, what that product typically look like? So we have a crop cycle. It could be corn, for example, in the U.S. We know that by May, the farmers has planted, the crops start to grow, and then these we divide that crop period into different phases. We define moderate drought, severe drought, extreme drought. We define payouts. We look at the historical data. So the great thing of this Planet data set, it comes with 20 years of history, and this allows our actuarial team to accurately create insurance products. Next slide. So this slide really brings it all together. So we created an app that directly connects through the API with Planet servers. The satellites flies over in the afternoon and in the evening, the farmers, the insurers, the Ministry of Agriculture, everybody can see what's the current soil moisture in the country, in the different municipalities. And then we see here in the graph, so if there is a deficit of soil moisture, then there are automatically payouts created. So this is an end-to-end digital insurance system based on the Planetary Variables of Planet. We are running this now, we started some five, six years ago with the first pilot. We are running this now in 15 different countries, on every continent, around the world. Maybe to finish with the last slide here. So the last project we have been working on, the last 1.5 year, is based on PlanetScope. So this is not the soil moisture data, this is the optical data, where for farmers in the Horn of Africa, we measure the greenness of the crops or of the grasslands. This is a big program sponsored by the World Bank, to help the cattle farmers in these three countries. It works very nicely, and it's planned also to be expanded to other countries in the world. Really, what I said, transparent and objective with a historical view and a real-time view, being able to pay to the farmers, the money that they need, when they need it the most. With that, I'd like to hand over to you, Will. Thanks, Marcel. Thanks. So we've got a few minutes for a couple of questions. Why did you start working with Planet in the first place? The main reason was really the data. So without unique data set, cooperation would have not started. So you look at different providers of data, you look at weather stations, different satellites, and then you look for what's the best data around, and then we ended up with the Planet team. That's maybe the first thing. And then, as a second step, you need to feel a real partnership, you feel that you do things together, that you are willing to try really to capture that market jointly. And that was really a good match. So yeah, so far, we're happy with the cooperation. Can you first tell us, touch a tiny bit on the ROI for you as a customer, and how were you doing before, and how does this help you save money or improve efficiency in whichever way? What is the value proposition expressed that way? Yeah. So the main thing is to go into markets where we were not before. So to make a drought insurable, you need a lot of experts, and those experts, they are not available. You have a lot of countries like Brazil or Queensland, Australia, with millions of kilometers of roads, millions of hectares, and it's just physically impossible to serve those. Also in Africa, we cannot, without the satellite data, to cover those. So in the past, maybe 15% or 20% of the crops were insured, and we're really trying to push that to 40% or 50% of the global number of hectares. Great. Thank you. That's very helpful. So, where do you see our collaboration going over time? I mean, it seems like there's a lot of scaling potential in both the use cases that you mentioned. How do you see it scaling over time, and what other products and services are you interested in? Yeah. So here, two things, of course. First, on the agriculture side, so I think we can still grow much further, insure more clients, maybe get more precise in a way. We also see a big potential in a hybrid model to kind of support the markets like France or Germany, where or U.S., where we have a lot of these experts, but still we can help them using the satellite data to become more efficient, to be more accurate. And then as an insurance industry, I mean, a lot of the topics you said, digital transformation of the insurance industry, AI, sustainability, all these topics are key, right? For a company like Swiss Re, for our clients- Yeah. This will happen, right? It's just a question of time. Honestly, insurance industry, maybe not the fastest, but we get there. Shocking. Well, thank you very, very much, Marcel. Thanks for joining us here today and taking your evening to join us live. With that, I am now going to welcome to the stage Planet's President of Product and Business, Kevin Weil, who came to us from small organizations like Twitter and Instagram, but now he's got the real deal. All right, thanks, Will. Hi, everybody. At last year's Analyst Day, we talked about our strategy to make daily change visible, accessible and actionable, and to build a great business as we do. I'm gonna show you how we're delivering on that and the positive impact that it's making with our customers. As our product line has expanded, it's become increasingly clear that we have an opportunity to move upstream in the value chain towards delivering solutions, not just delivering data. I'll talk about how we're reorienting our go-to market, and how our acquisition of Sinergise and its geospatial cloud platform, Sentinel Hub, is helping us focus our sales team on larger customers. I'll show you a demo of Sentinel Hub and how it speeds time to value for us and our customers. I'm excited also to show you our partner, Synthetaic's generative AI product, which is highlighting the value of our daily scan of the whole Earth to D&I customers around the world. I'll end with a glimpse at where we're going next. But I wanna start here. I have this poster that hangs in my office at home, and it says in big red letters on a white background: "Good work, consistently, over a long period of time." It's something I learned working with Zuck in my time at Instagram and Meta. Words that he said on an earnings call years ago when he was asked about Facebook's success. And this poster, to me, is what delivering for customers really means. Your strategy sets your general direction, but then you put your head down and you do good work focusing on data and on customer feedback. We're not perfect yet, but I'm really enthusiastic about how customer-driven Planet's becoming, and I think there's a lot of value building under the surface that isn't all visible quite yet. So I've said that making daily change visible, accessible, and actionable guides our strategy. Let me remind you how we think about that and show you some examples of the progress that we've made just in the last 12 months. I'm also gonna give a few sneak peeks into where we're going next. So making change visible, it's all about our unique Agile Aerospace capabilities and the proprietary data that they create. We operate many times more satellites than any of our competitors. We make them faster and more cost-effectively. And we built Pelican at a rapid speed. You just saw it sitting at Vandenberg, ready to go out, our first tech demo, and we did that all while building out a new hyperspectral satellite with Tanager. We moved quickly from identifying a market opportunity to testing and building and launching. And when we do, we automate all of it. We automate satellite commissioning and management, and that allows us to scale up with customer demand. In just the last 12 months, we made a huge amount of progress here. Will talked about the Pelican tech demo. The Tanager payload is already here at the lab. It's being assembled as we speak, and we have a fantastic response from customers in our early access program. We continue to improve the quality of our underlying product. We've made multiple major improvements to band alignment on PlanetScope this year. In addition to lowering latency, increasing our data storage efficiency, and quite literally, PlanetScope gets better for our customers every single month. Ditto SkySat. We shipped software that improved the constellation's capacity, made it more efficient, and improved image quality and latency as we did. The best part about doing this kind of foundational work is that it doesn't just improve our core product, it improves every analysis, every model, and every decision that the product powers. Planet's ability to make data accessible to businesses and the governments around the world expands our TAM well beyond the traditional geospatial market. We just talked about our core data, a daily 3-4 m global scan of the Earth, SkySat high-res tasking, and now our archive of over 1 billion images going back over seven years. But the even bigger opportunity is in moving up this data pyramid, from imagery to data and APIs, to machine learning, time series, and Planetary Variables. The history of this industry is humans looking with their eyeballs at imagery, but the future is about computers running algorithms and analysis to automatically identify objects and patterns and time series. We're seeing this with our AI products, where we can automatically identify new roads and urban development or ships in open water across millions of sq km, or with our partner, Synthetaic, which I'll show you in a little bit. Through our acquisitions of VanderSat and Salo Sciences, we're offering Planetary Variables, which are new categories of products that fuse our optical data with that of other sensors with different characteristics: microwave data, LiDAR data, even radar or SAR data, like you just heard. And these products, they don't look like pixels. They're measuring an actual geophysical property, something like land surface temperature or soil moisture or forest carbon. They're shaped more like a matrix or a time series, something that anybody who knows how to use Excel can use. This is why with each step up this data pyramid, we're not just reducing time to value for customers, we're also meaningfully increasing our TAM. We made a lot of progress here, too, over the last 12 months. This spring, we completed the integration of VanderSat and relaunched their core Planetary Variables as part of our API. We rebuilt our analytics pipelines, making them faster and more accurate, and in the process, we extended our ship detection models out further into open water. We've developed new AI models that upgrade the accuracy of our cloud masks, and we have more coming soon. We're in a beta period with a new product called Analysis-Ready PlanetScope, which is a temporally and spatially consistent, radiometrically harmonized, co-registered version of PlanetScope that's all tailor-made for time series analysis. As we do all of this and we build up the data pyramid, we're making it easier for others to use this work as a foundation for their own products, like you just heard from Swiss Re. They're standing on our shoulders to make more sophisticated products more quickly. As we do all of this, we have an opportunity to speed time to value even further for our customers and partners. Because so far, everything that I've talked about has been about data, right? We've talked about PlanetScope and SkySat. We've talked about Tanager and Pelican, even AI detections and Planetary Variables. That's half the battle. The other half is in workflows around the data, and as mundane as that sounds, it's fundamental to driving adoption and retention. So just like we wanna build up the data pyramid so that folks don't have to continually recreate the wheel on analysis, our customers are asking us if we can simplify the way that they integrate and understand and manipulate data. So imagine you're an agricultural customer. You're getting PlanetScope scenes, you're getting Planet Fusion data, and you're getting SkySat images. But now, what if you want to quickly generate a time series of NDVI values across all of your fields, filter by country, and group by region? What if you want to correlate soil-water content and crop yields over time? Or imagine you're a defense customer. You have broad area PlanetScope, you have SkySat tasking, and you have ship detection. But maybe you don't want to just detect ships, you want to count them over time, you want to establish a baseline, and you want to alert automatically anytime that value goes anomalously high or low. So what's needed here is really like a cloud software platform for the data pyramid itself. And each step we take along this path is something that our customers can start to do with a few clicks, that they used to have to hire software engineers and check in code to get done. So this makes our platform stickier, and it makes it more valuable for our customers and partners. This is probably where there's been the most work under the hood, a lot of which is gonna come visible over the next 12 months. But some that you can see today are, for example, we are making weekly improvements to our account management and quota systems. These are small but critical changes that make our customers' lives easier. We're providing customers real-time insight into tasking, including weather previews, real-time quota management, sun angles, and more. We've upgraded our GIS integration so you can do more with Planet data from within Esri or QGIS or Google Earth Engine. And of course, we completed the Sinergise acquisition, and that's worth spending more time on. So let me pivot to now looking forward, and, I'll talk a little bit more about Sinergise. So Sinergise, as a company, started 15 years ago. They were doing geospatial consulting, and as they built solutions for their customers, what they found was that they were solving the same problems over and over and over again. And so they did what any sensible group of engineers would do, and they automated those pieces. That helped them serve more clients and serve them faster. Now, you rinse and repeat that for years, and what they realized they had, after a bunch of iterations, was a powerful geospatial cloud platform. They named it Sentinel Hub, they opened it up for public use, and they began charging customers for storage and compute. They call that processing units, just like AWS. Now Sentinel Hub has these top-notch geospatial access and analysis capabilities. They leverage Planet's data. They also include other commercial data sets like optical and SAR and more. And of course, they have all the public data sets like Landsat and Sentinel. Whether you're a data scientist analyzing daily agricultural data, a web developer building a new EO solution, or you're trying to integrate SkySat into a GIS app or into an iPhone app, Sentinel Hub helps you get there faster. I'll show a demo here. I'm gonna show you how easy Sentinel Hub makes it to do analysis. Here we are. We're the government of Indonesia, and we're trying to monitor and enforce deforestation. We're gonna start by looking at publicly available data over the area. For example, Sentinel. Sentinel scans the Earth every five days at about a 10 m resolution versus Planet's daily 3 m. And Sentinel Hub makes it easy to quickly look at a time-lapse of any imagery over any period. And as we do that, we're like, "Eh, it's really cloudy." Like, there's some deforestation happening, but it's... There are a lot of clouds that get in the way, as often happens in tropical regions. So now we'll switch to Planet data. And when we look at the time-lapse, what we're gonna find is we see the power of daily imagery, 'cause suddenly you start to see much more granular change. You have fewer clouds, and one of the things you can tell is there's definitely clear-cutting happening here. Now we go in further, and one of the ways, if you're looking at imagery, to make vegetation really pop and really stand out versus non-vegetation, is to leverage Planet's near-infrared band with NDVI. And in Sentinel Hub here, you can see, you can construct combinations of bands in different ratios called Band Math, and you can see the results in real time. You can even make this quick slider that's giving you a quick before and after preview. All right, so now let's get more sophisticated. Sentinel Hub exposes its own imagery-aware scripting language to developers called Evalscript. So developers can write quick scripts that do custom transformations and logic. I'm not gonna go into all the details, but the point is, Sentinel Hub makes it easy for anybody to write algorithms that run MapR educe style across all the imagery in real time. In this case, to do custom logic on deforestation, and here you can color based on the time the deforestation occurred and turn this into more of like a data viz output. So analyses that previously required you to set up huge imagery pipelines, you can now do with a script in the cloud, and you can iterate in real time over terabytes of imagery. And it's been my experience, my entire career, that when you tighten the loop between questions and answers, you make more innovation possible, and that's exactly what Sentinel Hub enables. So now you can continue to iterate on this Evalscript. You can build business logic into it. You know, say, you're tuning thresholds for what's considered okay and what's not. You're highlighting the most egregious bits here in red. And with that, you can stream the result directly into a dashboard, into a GIS program, even into an app on somebody's phone. And the other cool thing is, this was a three-minute demo. I probably showed you, like, 5% of the power of Sentinel Hub. We only just touched on Evalscript. We didn't get into time series analysis. We didn't talk about batch processing. We didn't talk about their upcoming ability to host machine learning algorithms. Hub and Planet together open up a world of possibilities, and you're going to increasingly see them not as two separate things, but one deeply integrated cloud platform for our customers around the world. Sentinel Hub also shines as a way to explore and analyze Planetary Variables. So all of our Planetary Variables will be integrated and available within Sentinel Hub this year, including some of the new ones that we're really excited about. The upcoming forest carbon products that Will touched on and that Andrew Zolli will go deeper into, and this new automatic Field Boundaries product. Field Boundaries are fundamental to any agricultural workflow. So whether you're talking about precision agriculture for, like, a large, ag tech like Bayer, ag insurance for Swiss Re, or anything in between. So you have, for example, the CAP program in Europe, where European countries are providing $50 billion in subsidies for farmers who follow sustainable agricultural best practices. These subsidies are, of course, at a field level. So each country, in order to recognize these subsidies, needs to know their field boundaries by field across their entire territory. And if you look today, what they do is they ask farmers to upload them, or they have people, you know, looking at imagery and segmenting fields by hand, both of which are time-consuming and error-prone. Our automatic Field Boundaries product can do this all automatically. And one interesting thing was, this was in our roadmap as Planet. And when we brought the Sinergise team on, they had already been working on it because they had felt that demand from customers as well. So now we've got our teams working together to finalize this product, and we've seen a lot of interest from a bunch of customers already. And lastly, on the product front, let me talk a bit more about AI and why it represents such an incredible opportunity for us. Before Planet, there was no such thing as a daily scan of the Earth. It didn't exist. It's a new capability for humanity. Planet's data set has recorded macroscopic change across the entire face of the Earth for going on 2,500 days now. But in doing this, we've created a new problem, which is there is so much data, 13 trillion pixels per day, that we bring down from our satellites. So if you care about broad area change, you need this data. But how do you slice through all the terabytes to get at exactly the bit that you want to know about? You probably don't have seven years to manually look at the entire world every day, but if we could do it with AI in two minutes, I'm pretty sure that would work for just about everybody. There's a revolution happening in AI right now that's making all of this possible, and it could not be a better time for us. So I want to show you a bit about what AI is capable of, and some of which, some of these things we've developed internally, others are our strategic partnerships with folks. So let's start with this. These are detections over the 3 million sq km of the South China Sea. With AI, Planet and our partners are able to provide insight into where ships at sea are on any given day, even where there are aircraft in flight. So in this view, what you're seeing is detections from the South China Sea over one week in July. So let's zoom in. Here's an image captured by one of our Doves. This is a 450 km patch of the ocean, part of that original 3 million sq km that I was just showing you. And if you're tracking maritime activity, there are things in this image that you need to know about. But without help, you're trying to find kind of a needle in a haystack, right? It's time-consuming, and it's error-prone. Now, it turns out there are three objects in this image. Can anybody spot them? Some people are seeing one. There are a couple that are there. This is where our AI capabilities shine, right? We can quickly locate what's invisible to the naked eye. We can scan the 3 million sq km, not just this 450 sq km patch, but 3 million sq km, and locate objects of interest in minutes with individual confidence scores about each detection and additional metadata, like the object speed and the altitude. So go one level deeper. You can see what these are, and you see Planet's AI lets you hone in on the insights you need, so you don't get caught by surprise. You don't miss something that you needed to know. Now, I'm gonna give you another look. In this case, this is work that we've done with our partner, Synthetaic, and it's gonna show you what's possible with Planet data. This demo is gonna leverage generative AI, and what they do is they enable searching in real time. So this demo is about looking for missile silos. And technically, these are... Actually, we're gonna be looking for environmental covers that protect the missile silos. People call them bouncy castles. But the model knows nothing about missile silos. In fact, we're literally starting with a web image search over here on the right and finding an image off the web, putting a little square around the bouncy castle, the missile silo cover that we want, and then we hit go. And now, what comes back, and in real life, this takes a couple of minutes, takes a couple of seconds here in the video. It's a set of hits across the area in question of things that look like what we searched for, and they're color-coded by similarity. And one interesting thing is to note the variety here, right? We searched for a rectangular, white object, and what we're getting back are rectangular things, square things in different colors. We're seeing circular silos, and we can quickly fine-tune our search, this is what's happening here, by telling the algorithm which matches are the kind that we want, and then we leave alone the ones that we don't. We can save this search, we can sort of create different categories, and now we refine, and we can start exploring the results over here. So we've gone from a web image search to this in about a minute. So let's see how this looks. All right, first detection. That looks like the thing that we searched for. Keep going. So does the next one. Right? And you can zoom out a little bit, and you start to see there's a regular pattern of these objects in the desert in the middle of China. Now, we can zoom in somewhere else, where we found another bunch of detections and see what we see. And we find in this case, these are the circular silos that we found. So the algorithm is really finding different variations of the original object that we queued it towards earlier, which, again, came from a Google search. So, to recap, this is a search for an object the algorithm had never seen, was never trained for, across millions of sq km in China. The results are looking pretty good, especially for a first pass that took us two minutes. Then you can continue with this kind of iteration. We could go back and tune on the right-hand side what we're searching for, giving it finer-grained understanding of the results that we like and the results that we don't. This kind of human-in-the-loop AI is exactly what our defense customers are looking for, right? Because we can combine the best of what an analyst can do because they bring context, they have local understanding, they have mission awareness, they have a lot of context, and we combine that with the best of what computers can do, which is about massive computation to detect patterns and similarities. If you, if you think about what I just showed you, this was looking backwards, right? We, we sort of in four minutes, we fine-tuned a search looking backwards. But now imagine playing this forward. We've got this search that we've trained on, on missile silos, in this case. And now imagine that each day that Planet imagery comes in, so an image of the entire world comes in. We automatically run that search over that imagery, and it's going, and it can alert the customer for new detections of new missile silos being created automatically, daily, across any expanse of land on Earth. Right? We literally just showed you how to make this possible within four minutes. And that's four minutes that, you know, before the combination of Planet's daily scan of the Earth and Synthetaic generative AI, this had never been possible. The other thing to remember is this was a search for missile silos. We started with an image on the web. We could have equally found runways, planes, flights, cooling towers, bridges, anything else you can think of. And although I gave a bunch of examples in defense, this broad area search extends to civil government and commercial use cases as well, right? Imagine tracking construction of oil derricks or mining activity, geologic activity, floods, and more. It's a new world, and these AI techniques underscore and complement the power of Planet's daily scan. Just to touch on AI more broadly a little bit, GPT-4, I think if, if any of you have played with it, it has probably blown your mind. But I don't think we're even at the steep part of the S-curve yet. We're about to see GPT-5 coming soon from OpenAI and Microsoft. We are about to see a new foundation model called Gemini, coming from Google. You have Facebook fast following with their sort of Android strategy, keeping it open source. The world is accelerating, and it's about to get faster because GPT-5 and Gemini are both going to be multimodal models. That means they're going to understand imagery and video just the way GPT-4 understands text. Now, they don't train on our data. I'm sure they would love to, but imagine what you could do if you combined a model that could understand and describe and point out novel features and imagery with the world's most comprehensive and up-to-date imagery of the whole planet. So it's a super exciting time in AI. It's a very exciting time also to have the world's most comprehensive and up-to-date proprietary geospatial data set. So that was a lot on our product strategy, right? The improvements that we made over the last 12 months and some of the exciting stuff that's coming soon. I talked about our philosophy of execution and iterative improvements and the compounding value of getting a little bit better every single day. This stuff really matters to our customers, and here you can see a consistently growing customer base. And by the way, this does not include Sinergise's customer base yet. But more than a growing customer base, it's a happy, growing customer base. Our net promoter scores are now consistently in the 60s. And remember, that's on a scale of -100 to 100, where zero is a good score and 50 is an outstanding score. And our CSAT rates are now, consistently nearly at 100%. This is a testament to the work that our team's doing day in and day out on product improvements, as well as across customer success. It's also an indicator of our ability to drive retention and upsell among our existing customers. Another great indicator for retention is customers adopting multiple Planet products. As you can see, there's a consistent trend, consistent upward trend towards adopting multiple Planet products over time. So, for example, you have traditional geospatial customers. They start with high-res tasking, but over time, they find their way to PlanetScope or various AI solutions like automated road and building detection. Other customers start with PlanetScope and base maps, and they realize that they need Planetary Variables, or they want to tip and cue off of change, and they start using high-res tasking. So the fact that we have multiple constellations, the fact that we have a broad array of products like Planetary Variables and AI, it, it creates surface area for us for retention, for upsell with customers. And this is strategic because upselling current customers is a really efficient way to grow. And the fact that we are consistently achieving an NDRR of over 115% the last couple of years shows that we're getting traction there. But obviously, our recent growth rates have not been at the pace that we expect for the business, and addressing that's been a top priority. So we spent a lot of time reflecting and a lot of time analyzing data. I'm not satisfied. None of us are satisfied, but I'm also a big believer that success comes from how you handle adversity, and so we've been taking these headwinds, and they've been a learning opportunity for us. For one, as a young, growing company in a new market, we were in the habit of following every lead that came our way. So, you know, we know our data is useful in just about every vertical, so it's no surprise that we have leads, you know, kind of across the spectrum. But when you follow up on all of them, that, you know, makes sense when you're small, but you start to be a $200 million revenue business, you can actually inhibit scale. 'Cause when you expand into too many different verticals at the same time, it leads to time spent on proofs of concept and small deals. It, you know, you can lead to non-standard deals sometimes that that tie up internal teams and create opportunity cost. It even makes basic stuff like sales enablement and education a tougher thing to do. So to motivate some of the changes we're making, I wanna walk you through a bit of data from our pipeline. So this is one breakdown of our pipeline. The colors here show the verticals, so civil government, defense and intel, commercial, and research. The pie chart on the left shows you a breakdown by vertical of our—all of our qualified pipeline opportunities. The next two on the right show you the mix for opportunities greater than $100,000 in ACV and greater than $1 million in ACV. In order to be counted here, a deal has to have already been systematically qualified and scoped by a member of our sales team. You can see a lot here. I mean, first, one of the things you see is that we really do have a strong mix across defense and intelligence, defense and intel, civil government, and our commercial verticals. When you look at the seven-figure deals, you can see that they've shifted towards government opportunities, which is reflective of the mix shift that we've talked about on our recent earnings calls. When you look at the six-figure opportunities, there's a strong mid six-figure deal set, with commercial actually edging out civil gov for the most. And then on the left, you see this incredible number of small commercial deals. Nearly 60% of the total pipeline by deal quantity, not by dollar value. And the challenge is our tendency has been to spread ourselves too thin across too many of these smaller deals. So as we took a deeper look at this data, we saw an opportunity to simplify how we're going to market in service of faster growth. We're focusing our go-to market around three core principles. The first is reducing complexity. So the main thing here is that we need to standardize and automate small deals as much as we can. Today, any time a salesperson spends on a $15,000 deal is time that they could, and probably should, be spending instead on a $250,000 deal. So we're leveraging Sentinel Hub and other partners to speed our process on small deals. In the future, we're gonna be directing a large number of our smaller deals to Sentinel Hub, with its powerful APIs, its self-serve documentation, and that future is coming. So as of this morning, Planet's offering new packages that allow customers to buy multiple different sub-$50,000 packages of PlanetScope data and Sentinel Hub processing units directly from Sentinel Hub's website, with transparent pricing, without ever needing to go talk to a salesperson. This is great for people to get started quickly, but it's also an avenue for these smaller deals that doesn't require a salesperson, doesn't require papering a deal, negotiating terms, and so on. So as of today, it's available via a wire transfer, and pretty soon we'll open up credit card and PayPal. The second principle is to scale through partners, especially in our non-core verticals, right? We know our data is useful across nearly every commercial industry, but we're never gonna be experts in everything, and we're never gonna have solutions in every market. But our partners can. So we have a large partner ecosystem today, but we've often asked them in the past to front-load the risk with an upfront purchase. That made sense when we were smaller, but at our scale, we can look to better share risk and reward. That means aligning our business models with theirs to make it easier for them to get started, and it also means more meaningful upside for both, for both teams when we win together. Then the third principle is about once we reduce our time spent on small commercial deals, once we offload more of our non-core vertical deals to partners, it means our commercial team can spend more of their time focused on bigger deals in our core verticals of civil gov, defense and intelligence, and agriculture. This is an enterprise sales motion, and we're increasingly focused on selling solutions in these verticals, not just data. So let me talk a bit more about this. As we've expanded our product portfolio, we have these higher-level products like Planetary Variables and AI. We're seeing an increasing ability to sell and package solutions, not just data. At our last Analyst Day, I talked about this as part of our intention, but we didn't have the product portfolio in market yet. With the launch of Planetary Variables and some of these strategic AI work that I talked about, now we do, and we're just getting started. So this is a shift for us, but it's one that we've been driving towards for a while now, and it's one that we're really excited about because it can meaningfully shorten sales cycles and speed customer adoption and time to value. And to be clear, we don't plan to develop every solution. When we sell a solution in which our data is a key part, we're driving demand and usage of our data, and we're helping the customer see value faster. So whether it's a partner solution, as with Synthetaic's broad area search or our own Planet Insights. A great example of this is with civil government. So we talked earlier about this $50 billion CAP program. SInergise actually has offered an end-to-end solution for this program called Area Monitoring. It combines Planet's data, Planetary Variables like Field Boundaries, and AI algorithms, all packaged in a single interface so that a country can manage this program across their territory. And we see similar opportunities coming with a program called EUDR, that's focused around deforestation that Andrew will talk about soon. Our partner, LiveEO, as another example, they package Planetary Variables and PlanetScope together into an interface, into a user interface, designed to highlight vegetative encroachment. So think about linear infrastructure, like power lines, where a stray falling tree can cause down lines, can cause forest fires, can cause billions of dollars in damages, can cause loss of life. So it's exciting to be at a place where our expanding product portfolio, with things like Planetary Variables and AI, is allowing us to build these more complex offerings, so both internal and partner-led, and it's helping us and our partners deliver solutions faster and more repeatably. So that's just a quick look at where we've been and where we're going and how we're focusing our go-to-market. I want to end real quick by reiterating our long-term vision. We began as a satellite company, but our future is as a software platform company. And today, we're partway there. We're a data company, and we're a pretty impactful one. As we really open up this market, building up the data pyramid with Planetary Variables and AI, and building a geospatial cloud platform around it, together, these all augment our proprietary data. They make it easier to use, they make it more powerful, they build sticky workflows and create retentive customer relationships. These kinds of products enable us and our customers to go from selling data to selling solutions, which are easier to understand, and they're more directly connected to a customer's goal. It's an exciting time, and with Planet data being used to address some of the world's most important, you know, pressing geopolitical problems and climate-related challenges, this is a future that we're extremely motivated to create. Thank you. Now I'm excited to introduce you to Paulina Zubatov from our commercial team, and Mark Rosenberg, who is a research data manager at CAL FIRE. It's gonna be a great conversation. Thank you. [audio distortion] Hi, everybody. As Kevin mentioned, we've got Mark Rosenberg with us today. I have the pleasure of working with this gentleman to my right. He, through his work at CAL FIRE, has taken on many roles over his 30 years in forestry analysis, including CAL FIRE's fuel reduction tracking application, which we'll talk a little bit about today, supporting informed climate change policy around California. Then more recently, Mark has helped implement community wildfire preparedness programs within CAL FIRE across the state, within the Office of the State Fire Marshal. Today, he's now working with Planet. To kick us off a little bit, for those that might not know, can you tell us a little bit about what CAL FIRE is and what's the work that you do there? Well, thank you for having me, Paulina. CAL FIRE is an entity within state government of California. We're actually the California Department of Forestry and Fire Protection, branded CAL FIRE, and we have primary responsibility for fire protection across 31 million acres within California, a 100 million acre state. We also manage natural resources and regulate timber harvest on private, state and private forest lands. And we have the state fire marshal, who is concerned with fire safe engineering and community wildfire preparedness. My role within CAL FIRE is as a GIS manager in the Community Wildfire Preparedness and Mitigation Division. I have nine staff that work in, embedded within, several division programs, managing utility line clearance regulation, looking at defensible space around homes, looking at post-fire damage inspection. Tragic, but also something that we do. And we have community wildfire planning and mitigation, so it's our pre-fire programs. Sounds pretty simple, pretty basic. Can you take us back in time a little bit to how your relationship with Planet started? Sure. I've been paying attention to Planet for a while. Global coverage, daily imagery is pretty useful. We've had several, you know, meetings with Planet over the years. More recently, though, there's a lot of emphasis on fuel reduction activities and keeping track of them and reporting on those. So I signed up for an event at a conference with Esri, the software company, and you and I met- Mm-hmm. And talked about a project that I was thinking about related to our tracking our fuels reduction projects. We have a goal to get to 100,000 acres of fuels reduction a year, and so for that, we have to provide some reporting. But the most interesting thing about this project for me is transparency for the public on our projects. There's a lot of unmet information needs, and we're trying to address those by giving people a picture. And you know, it took a while for us to get a contract written and awarded, which is pretty typical for a state government. And I just remember soon after we started talking, you gave me a call. We had a fire up in Northern California, the McKinney Fire, and you guys offered to provide some imaging for us, and that was, that was a really successful collaboration with us providing smoke forecasts and helping to direct when appropriate times might be to take a SkySat picture of our fire and send it over to our Wildfire Integration and Threat Center. That, that fuel reduction, wildfire fuel reduction activity, monitoring it and assessing it, is the project that we're working on together today. That's the focus of our work together. Why is it important to monitor these? You already said visibility is a huge- Mm-hmm. -part of it, but what did that process look like prior to using Planet, and what does it look like now that you're leveraging Planet data? Yeah, well, we're still working on leveraging Planet data through our contract, so we're just starting evaluating, and then we're gonna pilot the imagery for a full year. Prior to, you know, working with imagery and our current status is really GIS mapping. We have an application, it's an enterprise geodatabase. It—we have field units, they go in and draw their projects over a map on screen, or they're taking their GPS unit out to the forest, where they're doing fuel breaks or prescribed fires, and then bringing that information back about what they did and drawing that in or uploading that to the application. So they're really not using any current imagery for their... We have two-year-old imagery- Right. that they can look at, but there's nothing really current to see where they've been doing their work. So it's a longer process, and sometimes, I know we talked about accuracy of what's being mapped. Yeah. Yeah, you know, if you can't see a picture, you know, they don't always do the best delineations on the map. Sometimes they, you know, they kinda cheat. They wanna draw the area that they're gonna do, but that's not what they did, you know, that month. Mm-hmm. You know, maybe even sometimes they don't get it all done in a year. So having a good picture about where they did get work done and what's left to do is really important, both from a work management perspective as well as from public transparency. Then if you could also, just to wrap us up, briefly talk a little bit about what that catalyst was between leveraging Planet data previously and leveraging it now. Yeah, there's, you know... With the increasing frequency and severity of fire in California, there's a lot of emphasis on working on the landscape before the fire comes, so protecting communities with fuel breaks or working out prescribed fire out in the more wildland areas. So, one aspect is, you know, the hunger for information about our activities and that the amount of activity we're doing has really increased. Also, I would say over the last five years or so, CAL FIRE has gone through and been implementing a strategic plan for geographic information systems and using that for decision-making, data-driven decisions. And so you're seeing, not just within CAL FIRE, but across the Natural Resources Agency and state government, sort of this emphasis on technology and technology solutions, and the ability to integrate that technology into our operations is increasing. And some of that's strategic, some of that's just the marketplace has really improved. And, you know, I've been using imagery for looking at fires for a long time. And you said, you know, be able to request some imagery, then you would download it. It would take, you know, hours to download, and then you would pull it up, and it was all smoky, and you really couldn't see what you were trying to see. But with streaming products and detections and a little bit more service from your professional services program- Mm-hmm. which is a real big deal for us. You know, when we tried to enter the market and use some of this technology previous to now, we really, we would've had to put together multiple contracts to get, you know, professional services on top of imagery, and now a lot of that is, built in with Planet. And you guys are able to provide a lot more service on top. And we're talking about image enhancement, algorithms against the imagery, and also delivery, 'cause as I mentioned- Mm-hmm. ... that delivery was really a bottleneck for us. Really quickly, where do you see your partnership with Planet going in the future? Well, I expect that our, the image solution that we're looking at will be really successful. You know, we're in the pilot phase now, but if we get adoption across the state with our units, and they find value, I expect us, you know, we'll be continuing to capture imagery moving forward, to provide a tool for our unit staff who are doing projects, as well as for the public to see where we're doing work. And, you know, the goal, our target is 100,000 acres, but the target for the state is 1 million acres. And I also could see a lot of additional requests coming for places where our fuel reduction projects have interacted with a fire, and we're trying to evaluate effectiveness. Mm-hmm. And so I think, you know, those will be places where, "Ah, well, we've got this contract. We're planning to do all of these," but every once in a while, we'll get a fire, and someone will wanna request imagery for that. That- I anticipate some of that- Some agility. Yeah. Well, we're looking forward to it, and thank you so much for joining us for this conversation. I'm gonna invite Kevin back up here. Thank you, Mark. Thank you for having me. Thank you. All right, it is my pleasure to introduce Robert Cardillo, who is our Planet Federal's Chief Strategist and Chairman of the Board, also former Director of the National Geospatial-Intelligence Agency. Robert? Thanks, Kevin. It's good to be back with you all. I was here two years ago for Investors Day, just after I had joined Planet. But let me give you or remind you about some context so you understand my perspective. I'm what's known as a lifer in this profession that we now call geospatial intelligence. I actually began that path, that career, early in the Reagan administration. Yep, I've earned this hair. And I was what was known as a photographic interpreter, and what that meant was it was my job to peer through a microscope onto a light table at a flat of film, actual film, that had been captured by a satellite that took that photograph, ejected the capsule, dropped that capsule through the atmosphere, deployed a parachute, got caught by an Air Force transport somewhere over the Pacific in a trapeze. That sounds incredible. I happen to think it sounds amazing because think of the ingenuity and the risk that it took to do all of those things. And the reason I tell you that story is, one, to let you know where I come from, but I tell you, too, is that in those days, now, that's just two generations ago, the value proposition was, I have an image and you don't. Now, this was all owned and operated by the government. It was a monopoly. It was highly classified, and so this was the Cold War. And so for decision-makers in the United States and our allies to be able to see something that the adversary didn't know we could see, or to hide something that they could not see, was the advantage. And I say all that to set up my discussion this morning because while the image is always critical, it's no longer sufficient for the advantage that we need to, we need going forward. So again, with that as background, I am the former Director of NGA and now, you know, new Chairman here at Planet Federal. I spoke that day about my excitement about what we were going to do over the next two years, and it was just really refreshing and rewarding to hear Will and Kevin and our customers show you what has been accomplished in the past two years. But my point for this talk is also to share with you my enthusiasm, my excitement for what's ahead. I am gonna focus on the government in general, but really narrow my conversation around that defense and intelligence community that I came from, and to share with you my anticipation about what we'll do together. With that, this is one representation of the difference from that world I described when I opened my talk. A lonely satellite operating in space at top-secret levels. We're in a period where you might consider the sensing of the planet to be nearly ubiquitous. Now, that's not quite true, but there's so much sensing that's going on now, that there is a risk that if you don't provide a frame of reference, if you don't provide that context, that you could actually confuse a customer or a partner through data inundation or overwhelming. We use the term tsunami of data in the government from time to time. When I was a director, we had challenge coins, and my coin had the agency seal on one side. On the other side, it had four words, and they were content, context, conveyance, and consequence. And the reason I chose those four words is as follows: As Will mentioned, at the start today, if you don't have the data, you don't get in the game, right? So, nothing happens without that data, and Planet has been and will be a data provisioning company. But as I said, and it's shown on this chart here, that data, if it's not framed, if it doesn't have a context, can, in fact, be confusing. Actually, Deputy Secretary Work one time called my mission at NGA, that I need to move the community from chaos to coherence. And this is exactly what we're trying to do here at Planet as well. And then, of course, you know, me knowing something but not being able to convey it to you doesn't do you any good. When I was in government, that conveyance usually was a piece of paper or a set of papers. They have a staple in the upper left-hand corner, and it would say, "Top Secret," at the top and, "Top Secret," at the bottom. You heard and saw Kevin demonstrate, and our customers demonstrate today, it's an iPad, it's an iPhone, it's a laptop. It's a signal on a computer screen, so a whole different way to convey. But at the end of the day, that consequence, okay, that outcome is why we're here. Can you make a better decision? Can you employ California fire assets in a way that save lives? Can you recover more quickly after a disaster? All of those things lead to the consequence that we're pursuing. So I mentioned my world. That's actually a modern look. You don't have the light table anymore. We have a screen. But the reason I bring it up is because, I'd mentioned before that we, we did some back-of-the-envelope calculations, when I was the Director of NGA, and I said: Let's, let's, let's total up all of the imagery that's coming into the building or into our agency in a given 24-hour period. That's obviously all the government-provided imagery. It's our allies', imagery that they would share with us. It's commercial imagery, such as that we were getting from Planet. Airborne, was included as well, being spaceborne. And then we applied the metrics of work that it takes to extract information from those images if you pursue the human model, the old Cardillo method back in the early 1980s. We did the math, and we said, "Okay, to get through all of today's imagery, we would need 6 million imagery analysts here in the agency." While that kind of excited me for a moment, I knew that the Office of Management and Budget wasn't gonna provide the funds for me to hire said 6 million. But it really made the point: We have to move to machine first, and we have to move faster. And oh, by the way, those calculations were done about six years ago, and I think it's easily 10 million now. So you know, there was some tension in the government about how and when to move to machines. How good are they? How reliable are they? Can they get us to the right answer with the fewest amount of errors? But as Kevin and Will have described this morning, the commercial technology in this space has not just caught up, but really gotten ahead. So anyway, all of this now allows us to pursue value in a different way, which is: How can we use computers to answer the questions they're really good at? What, where, and when? And save your humans, save your experts for the really difficult questions about why and what's next. And it's that human and machine, not machine versus human, that I know is the way forward. And so we're doing all that so that we can set up the next phase, which is: What do these detections mean to me? Do I need to add some confirmation, other sensors? What assets can I use to confirm the data that, or the detections that I'm getting? And now, what decisions can I make? So as we go from there, let me give you an example. This is a Chinese military aircraft. On the left side is 2019, on the right side is 2023. And if you look at the difference between the left and the right, you can see clearly to the eye, there's been some major improvements made to this airfield. And that's obviously of import, because those improvements lead to capabilities and capacities. But once you do that baselining of that construction and that expansion, you then apply that daily scan, that monitoring, to begin to develop patterns. What's normal? What's abnormal? How do I know when an exercise might be occurring? Has there been a pre-deployment for a future plan? And again, I'll say, even though I, you know, have lived in the imagery world the whole time, I'm also, well aware and appreciative that imagery only tells you part of the story. Again, it's the frame of reference upon which you need to add, other sources of information. But this, in fact, what I like about it, is the sequence shows literally that time machine, how the evolution of that airfield has occurred, in the past. And as a lifelong analyst, I know that those past events and realities can, in fact, be indicators of the future. So it leads me to this story, which has been well told, but the reason I wanted to, to bring it up with you here again today is to remind you of the difficulty of what you've seen and what I'm, I'm gonna show you again. On the 15th of January, on an island off the coast of China, Hainan, some group of scientists and/or military officials launched a lighter-than-air balloon. And they did it for, for some reason, for, for some collection purpose or for some, some surveillance purpose on the 15th of January. But when that happened, very, very few people in the, in the world, outside of those involved with the launch, knew that it had happened. On the 2nd of February, so some 17 days later, you probably saw on your Twitter or X feed or in your New York Times app, a Chinese spy balloon over Montana, with some pictures taken of this large craft, about 65 ft in diameter, with some sort of package hanging underneath it, slowly floating across the fields in Montana. Many questions, right? Diplomatically, to begin to ask from our State Department, militarily from the Pentagon, and from the intelligence community, the forensic questions came, and the first amongst those question is, where did this balloon came from? Now go back to this map. That's the challenge, right? You know where it is now, on the 2nd of February, somewhere over the skies of Montana, but you don't know where to look for it. As Kevin demonstrated in our partnership with Synthetaic, we made the case, the theory, that those prior locations of that object are somewhere in the PlanetScope archive. And again, using Kevin's math, we're now talking about 200 trillion pixels between the 15th and the 2nd. And by the way, given the size of that balloon, probably 100 pixels are what we were gonna look for. So 100 out of a couple hundred trillion. And again, but, you know, this, this was early days. We had not done this before. And, and as Kevin mentioned, we didn't have a model of the balloon and didn't need one, but we did need to tell that algorithm kind of what to look for. So imagine sketching out a balloon shape and then going through the kind of procedures that Kevin did with the silo example. Well, that led to this, and not only was it detected once or twice, but it was detected 9 times in the archive from that point on the 2nd of February over Montana, all the way back to the 15th of January, just off the coast of Hainan. I have to tell you that what—I mean, there's so many things that are amazing about this. It, to me, brings to life the theory of the value of the archive. Because again, on the 15th of January, no one knew to look for a balloon launch off Hainan Island. And yet, once we knew some 17 days later that we then needed to go back and look, the fact that we had the archive and the partnership, we were able to unlock that history, and unlocking that history became the value proposition. So to me, not only does, did it then answer that first question, where the balloon came from, but then you could push forward, and you could begin to set the algorithm to say: Have there been other Chinese balloons, or, or how many, and where did they launch from? When did they launch? Where did each one go? And so you can be proactive in the future to, again, set conditions to give you a notification when the next one might be launched. So take that capability. Let's go back to China. In this case, you've got three airfields, three different military airfields, and you apply that approach and the teamwork between Planet and Synthetaic. By understanding not just the gross activity at these airfields, but the types of activity, so transports, fighters, tankers, et cetera. And you'll see in the columns the different counts that occurred at different airfields because, let's face it, every military is a military of systems, and so nothing exists in isolation. So you may have tankers at one base that are responsible to keep the bombers at another base aloft during exercises and/or combat events. And you need to be able to know the activities of both, because they're coincidence, okay, and they're working together is what tells you, gives you indication. So that's what you see in kind of the main screen. I'd also like to direct your attention to the four images on the right. It says, "Unusual aircraft detected." Well, that's an example that this partnership with this algorithm is that, yep, we told it to look for tankers, and we told it to look for fighters, but it also found aircraft that didn't fit anything we told it to look for. And in my world, those are unknown unknowns. Didn't know to look for them, didn't know they were there, but the algorithm brought them back and said, "You might be interested in. I found something else with wings, but it didn't have the sweep or didn't have the shape that you asked me for." To me, this is very exciting about going forward because, let's face it, as much as we like to, you know, believe we know about adversarial capabilities and threats, there are always unknowns, and to have the ability to tease those out and bring them to your attention is what is possible now. I've been in the Pentagon quite recently with very senior members of their leadership, and when we showed them this example, the phrase we got back is: "Detections as a service." And frankly, I couldn't improve on that description. This is the breached dam in Ukraine that occurred some months ago, and you'll also recall that after the breach occurred, there was a great debate about who was responsible for the damage. And both sides accused the other. And I'm not here telling you that this image, okay, can dispositively say who was responsible, but again, it allows that frame of reference to provide other sources of information, to allow to have the international context, okay, for the conversation about attribution. And it's just yet another example of what Planet's been able to do to expose and to hold accountable what's happened in and around Ukraine, from the pre-invasion through some of the indiscriminate targeting that's been done by Russia. And then finally, I would say, too, that back to the Pentagon, that there has been more and more discussion about using this kind of transparency as a deterrent. Now, it clearly didn't deter President Putin from making his invasion, but you can imagine using this kind of light and transparency as a way to elevate the conversation in advance of future events. So I showed you this graphic up front, and as I've mentioned a number of times, we're very proud here at Planet of what we've been able to do in the past few years and about, and what we're stepping up to do here soon. I also wanna tell you that given my role at Planet Federal, this is in very much aligned now with where the government, especially the defense and intelligence community, is moving. Because, yes, will they continue to be procurers and buyers of our data, of our images? Yes, of course. But more and more, we're seeing the demand signal, and we're seeing budgets move to outcomes. And so we're there. We're listening. We want to subscribe to activity, and we want you to provide us with the results of the imaging and the analysis in ways that just inform us on the activity. And again, Planet will continue to serve both of those demand signals. And again, you think of that seven-year archive, it grows every day, and it gets more valuable every day. And as we identify other partners and other capabilities here inside of Planet to unlock that value, to me, the upside is very, very large. So we're pivoting, we're maturing in line with those customers, those key customers in our defense intelligence communities. And as they begin to solicit for those outcomes, our services become their solutions. So I, I hope you can see why I'm so excited about the combination of what you're seeing on the screen, which is the continued evolution of the Planet sensing capability that both Will and Kevin showed you this morning, but also about the way that we're deriving the value from that remote sensing capability. So it's both. It's both the sensing and the sense-making. And as somebody who's joined the community a long, long time ago to try to contribute to a more stable, more secure world, I'm sure I couldn't be in a better place at a better time than right here and right now. So with that, I wanna thank you for your time this morning as I welcome a friend and colleague, Mr. Dr. Jeffrey Lewis. Oh, there you are. Now, there's a stool. Jeffrey, come on up here. Thank you. Jeffrey is a professor at Middlebury Institute of International Studies, and among many roles, Jeffrey leads a research team that uses public and commercial data, such as Planet's Earth observation data, to track the spread of nuclear weapons around the world. Yeah. Small topic. A weird gig, right? Yeah, indeed. Jeffrey, welcome. Thanks for joining us. Why don't you start by expanding a little bit on that top-level introduction? Yeah. So, as you say, I run a research team, and we make extensive use of Earth observation data to track the spread of nuclear weapons around the world. I mean, it's a classic problem because we all wanna know what's happening, yet this stuff is supposed to be secret. So we don't really have the option of just going to North Korea and saying, you know, "Hey, could you show us the nuclear missiles?" You tried, you tried that? Yeah, well, you know, other people have but you don't see them again. They just disappear after that. Like, we have graduate students, and there's some, like, Institutional Review Board things if you get them thrown in prison. So much safer to go ahead and look and study these things using satellites. Right. And so when, how did you first become associated with Planet? Well, I think I was associated with Planet before Planet existed because I knew Will and Robbie when they lived in D.C., and were working on all manner of space things. So, it really wasn't until Planet got going, though, that Trevor Hammond reached out and pointed out we'd done an analysis, and he said: "You know, we have this huge database of 3 m images looking at this particular thing you're looking at, and it moves. Would you like to see it move every day for a year?" And I was like, "Yeah." So, that sort of has touched off what has been, at least from our perspective, an incredible partnership that's allowed us to do things that I really never dreamed possible. I suspect, Jeffrey, being an academic and a researcher, you know, there's not a data set in the world that you wouldn't want to explore and to potentially leverage. What is it about Will and Robbie's and Planet's approach that's been maybe particularly valuable to you and the research team? Yeah, the thing that is hugely valuable, really, is the combination of the enormous amount of 3 m data and then the ability to flexibly task, if you, you know, if you're gonna tip and cue. So, you know, there's this whole wide world out there, and if you're only looking at the world through high-resolution images, like, you have to know where to look, and you have to know where to look in advance. Mm-hmm. And that, that can be really tough. And so having this kind of baseline capability where you're looking at, you know, the whole Earth almost every day, clouds being clouds- Mm-hmm. ... gives us the ability to monitor change in a way that is really remarkable because, you know, I think back to an early bit of research we tried to do with Russia's nuclear test site. This is in the news a lot lately because it looks like Russia might be moving toward resuming nuclear explosive testing. And we wanted to buy an image of that test site, which is way above the Arctic Circle. And so, in our pre-Planet days, we contracted with an Israeli company to get an image of the site, which didn't exist on Google Earth, and it took something like three months, $several thousand, and the satellite just made pass after pass after pass, and it was just clouds, clouds, clouds, right? We now monitor that site with Planet because we're getting daily 3 m images where you can see all kinds of change, and then that can cue you to know, like, it's now time to put a high-resolution asset on the thing, and they have a high enough revisit rate that usually we get a break in the clouds. I'd love to hear your perspective. Again, we heard from Will, and we heard from Kevin today about the excitement, okay, in the AI world, especially in the past six to 12 months, right? Apparently, this leap forward to capability. Have you been able to leverage that on your research team? Has... And if so, how has it helped you advance your understanding? Yeah, we are just starting to try to integrate machine learning and AI. But it's ultimately gonna be absolutely essential because the cool thing about the data is there is so damn much of it. Mm-hmm. Right? The downside of that is, like, your 6 million employee problem. I would love to have a 6 million-employee team. I'm sure that would be really good for my, like- That's a lot of grad students. It's a lot of grad students. I probably wouldn't oversee them all. But it's just too much data to look at with your eyes, right? Mm-hmm. A lot of what we do is signature-based. Mm-hmm. And so we really need to find ways to get through that data really, really efficiently, looking for those needles that we know are in this enormous haystack. Given your long, long history, pre-Planet history, with the company, given where Planet's going, right, with the next generation of high res via Pelican, which I don't know if you heard, is down at Vandenberg as we speak. So the tech demo will be launched next month. The Tanager sensor is downstairs as we speak, being integrated as well, for launch next year. What, what excites you about, you know, the next two, three, four years with, you know, as Planet evolves its capabilities? Yeah, well, there are a number of things that I'm really excited about. I mean, one thing is just the shift to multi and ultimately hyperspectral data. Mm-hmm. You know, a lot of times, I think people have this idea when you're... I mean, you, you know this is not true because this used to be your job. But, people with, like, normal lives think that you get a satellite image, you look at it, and you see the thing you're looking for. Mm-hmm. But it's not true. You know, my favorite example of this is the Cuban Missile Crisis. No actual pictures of missiles. Mm-hmm. Right? What you have is pictures of sites, right? Mm-hmm. You have tents, you have trailers, and so a lot of what we do ultimately is inferential. Mm-hmm. Having multispectral and ultimately hyperspectral data helps us do a lot more of that inferential work. Mm-hmm. So, for example, you know, if you're trying to deal with a Chinese missile silo that's camouflaged, right? The hyperspectral data can really help pull that stuff out. If you're trying to analyze a facility to know if it's operating or not- Mm-hmm. ... you're looking at, like, turbidity of water allows you to know which way the process flow runs through. So there is just this enormous amount of data that's available about the world that's getting captured. Mm-hmm. And I think a lot of that's gonna exist in terms of multi and hyperspectral. Yeah. I couldn't agree more. I especially with hyperspectral. What excites me about that is, even though we tend to add that word, imagery, it really is sensing. I mean, it's in the truest sense of the word, meaning it's taking measurements, okay, across those spectral bands in ways that we can't interpret. What makes me hopeful about it is there'll be no, even, no interest at all to try to go look at it. You know what I mean? Give it to the machine, right? Set your, you know, your algorithm and your approach to say, oh, no, I'm only interested if these three detects happen because those three things mean this chemical production or this crop threat, you know, for disease or whatnot. I always, you know, worry that sometimes that when we label things imagery, right, people even, "Well, let me go look at it," to your point, and find it. Gives me great hope that the hyperspectral will unlock answers we didn't even know to ask before. Yeah. Well, you know, this movie, Event Horizon? It's an old sci-fi- Yes, yes, yeah. Yeah, okay, well, that's 'cause we're old. We know this movie. But not everybody does. I always show, there's a clip I show to my students, where one of the main characters has gouged out his eyes, right? And he says, "Where we're going, we don't need eyes to see." Right? And I think that ultimately is the future, right? With we're not going to be looking at all this imagery because there's just too much of it. It's really just data, right? And when you start to think about what you're really trying to find out, which is what change is happening on the Earth, you're just-... Treating it as one giant data set, you are gonna look for what's normal, and you're gonna have a computer tell you when there is an anomaly or something that's abnormal, and then that's gonna go ahead and cue you in. And again, you know, I think one of the things I run into is people do not understand the fundamental value of the 3 m data, right? Because they're used to thinking of an image as something you look at, and so the sharper it is, the better it is. But ultimately, when you are a good analyst, you can know what to look for, even in moderate-resolution imagery. And then the other advantages you get of having an enormous database of it, getting it almost every day, right, those advantages really start to, you know, arise. Like, you know, we use that to track Chinese missile tests. Mm-hmm. Mm-hmm. I love the end of that vignette. I'm still stuck on the, just the imaging of the beginning of the gouging of the eyes. I mean, I just gave you nightmare fuel, right? No, I know. Can I also say, though, that I love what you said about, you know, the detection indicating an analytic thread or a theory of the case. One of the things I strongly believed and learned as an imagery analyst, that sometimes the answer is what's not in the image. Something's missing, right, that would need to be there or should be there to indicate a certain intention. You know, could be a, could be a supply vehicle they don't have, or could be they don't have the fuel to... and so—and look, I'm sure there's ways to tune the algorithms to help us, you know, what is missing from this picture as well. Having said all that, I don't want to miss the opportunity on this stage, Jeffrey, to put my former hat on and thank you for what you and your team has done. I'll be frank, there were times I would open up the newspaper inside my top-secret facility with my top-secret badge, and I'd go: "Oh, my goodness, what has Jeffrey done," right? You know, my immediate reaction, that shouldn't be on the front page. But then take a step back, and I go: Oh, no, no, no, it has to be part of the conversation. Are there things that the government will protect, right, for security reasons or you know have a smaller sliver of advantage? Yes. But I just wanted to thank you and your team for what you do to advance, one, the state of the profession, our understanding of the world, but do in a way that's educational in multiple senses, obviously, for the institution. But a long way to say, I believe strongly that the more informed our citizenry is, the better citizens they can be. So for all of that, I just wanted to thank you for that. And then please don't stop. Yeah. Well, I appreciate it, and as long as, you know, Planet continues to partner with us and provide such amazing data, we won't. We'll commit to that if you'll commit to your end of the bargain. All right. Thank you very much. Thanks, Jeffrey. Yes, sir. With that, I have the great pleasure to welcome to the stage another friend and colleague, Planet's Chief Impact Officer, Andrew Zolli. Andrew. Welcome. There you go. Hello, and, good morning. Good late morning to everyone here and, to everyone who's listening online. It's terrific to be here with you. I wanna just say I was passed a note that it is not technically required for you to gouge your eyes out to get value from hyperspectral imagery. We just wanna make sure everyone is on the same page. As our Chief Impact Officer here at Planet, I have the great pleasure of leading a team of technical experts in areas like biodiversity assessment and forest and land use and climate and scientific research and development, as well as humanitarian and human rights work around the world. This team works with every part of the company, the entire commercial organization, all of the other parts of the technical organization, to help realize our overall aspiration, which, you can see actually written in big letters on the other side of the front door at Planet, which is to use space to help life on Earth. Now, I do wanna say, just to weave a couple of threads together before I give you the kind of the main bulk of my remarks this morning, and that is just to call a couple of things out. First of all, sustainability and climate, in particular, are the subtext, and often in the foreground, of all kinds of customer conversations and all kinds of customer use cases that we actively work on today. You know, these considerations are at the forefront of agriculture. They're at the forefront of land use. You heard in some of our customer conversations earlier today with our colleague from Swiss Re, talking about insurance, the subtext of that work is: how do we ensure novel forms of risk on a changing planet? We heard about, with our colleagues at, at CAL FIRE, we hear about how important it is to reduce the physical risk of climate-exacerbated, wildfires, not just in California, but that's a problem all over the world. So I wanna say the things you're hearing are very much today. Those are today problems, they are today solutions, they are today considerations. But it's also the case that we're going to see all kinds of new markets forming, new applications, new drivers around these kinds of considerations. That's what I wanna spend a lot of my, my time with you talking about today. It's not just the stuff we do because we are a public benefit corporation, because we want to drive to mission-aligned outcomes, but because we are also establishing the markets and the approaches that will be used to scale solutions in the future. Scaling these kinds of solutions has now taken on an incredible level of urgency. That's the other thing that's happening. We're bringing these capabilities forward at a time when, say, the Intergovernmental Panel on Climate Change, the IPCC, which is the, the UN body of experts that gives us our consensus perspective on the context of climate change today, has told us that we are living in the most consequential decade with relationship to the climate that humanity has ever lived in. This is a decade in which we will either lock in or lock out the worst effects of climate change, for sure. And that means that every day that we proceed, and there are about 2,500 days left in this decade, that every day that we work is by definition, the day in which we have the most agency. And you can see, you know, that fact, Will referenced it earlier. We're. This past July was the single hottest month in recorded history. The ocean temperatures off the coast of Florida were measured at 101.1 degrees Fahrenheit. There were essays in the New York Times like: What's it like to swim in that water? And my favorite headline, which we didn't put up here, was from The Onion, the satirist at The Onion, who archly said, "Even sharks deserve a hot meal every once in a while." I hope they weren't talking about us. I'm assuming they were talking about fish, but you never know. But the real consequences of that kind of impact on the climate are mind-boggling. They will be measured in massive coral die-offs. They'll be measured in a dramatically intensified hurricanes and typhoons. Now, one of the other themes you've heard today that I wanna kind of draw some lines is like a couple of key words you'll hear over and over again, and one of them is about signals. It's about creating demand signals. It's about creating reliable signals from this massive information, which we've now collected. One important part of the subtext of the narrative of AI is our ability to create rapid, high quality, scientifically accurate indicators around which we can form new kinds of behaviors and fundamentally form new kinds of markets. That's because addressing this kind of planetary change is bigger than a single company. It's bigger than a single country. It's bigger, bigger than a single segment of the world. It requires us to scale existing solutions and create new ones, and fundamentally create markets and market-like structures where supply and demand can be met, where indications of what's working can help drive scale. And the data that Planet provides, and others, will be providing, plays a key role in helping these kinds of new market-oriented solutions to these kinds of issues of climate change form. It... The data we produce helps those markets operate in ways that are transparent, in ways that are coherent, in ways that are ethical. And I'm gonna show you a couple of examples of that. Now, technology and policy form the kind of trigger and scaffolding around the formation of these kinds of markets, and you can see this coming forward from Europe right now in a huge raft of relevant initiatives. Today, these include things like Norway's International Climate and Forest Initiative, which we've been very close partners with over the last few years, creating datasets that allow us to commonly understand what's happening in our forest. You heard reference to CAP, that's the Common Agricultural Policy. It's a huge program in which Europe is driving and incentivizing sustainable agricultural practices at scale. I'm gonna talk about this third one in a minute, about what's called the EUDR, the E.U. Deforestation Regulations. That's Europe's effort to require that, commodities and products based on commodities that are imported in and through Europe are not linked to deforestation. And then finally, CSRD, and I know it's a little bit of an acronym soup, but that's the E.U.'s Corporate Sustainability Reporting Directive. That puts new requirements on companies to actually measure their climate and sustainability-related impacts. Now, the thing to understand about all four of these is they are huge creators of markets. They are going to spur. Compliance with these regulations is gonna spur all kinds of technological capability, and it is fundamentally unachievable without the kinds of data and products that Planet is producing. Like, what we're seeing is the world saying: We have to move with all due speed across these next 2,500 days to fundamentally drive new behaviors across global supply chains. We have to incentivize behaviors that reduce emissions, and the only way to do that, to fulfill any of these obligations, is with the tools that Planet is producing. Now, could you roll ahead? Thank you. Now, the one I really wanna spend time on with you today is related specifically to the work that we're doing in forest. You've heard this is our latest Planetary Variables suite of products. And I wanna tell you about how they work, and then I wanna show you them in action in a moment, just for, in, in a few seconds. So today, as was mentioned, you can see a lot in a high-density time series about forest change, but we can see every act of deforestation as it's occurring. We can see the trees being felled, right? Because you're watching every single day. But in this new world in which we're gonna see these new regulations, it's important to understand other dimensions of forest change. It's important to get at this kind of structure of forests. So we've been developing these products that combine smaller strips of airborne LiDAR data. And so for those of you, I know that many people here know what LiDAR data is, but for those of you who don't, that's the instrument. We sometimes see them on self-driving cars here around the Bay. It's that rapidly spinning, it looks like a siren. What it's doing is it's spraying around the car and collecting points of information to build a three-dimensional volumetric model of what's around the car. It's what allows self-driving cars to avoid accidents. Well, if you take that instrument and you point it at the ground and fly it in an airplane, you can get three-dimensional strips of information about the volume of the forests that are below the airplane. And it turns out that there's really good correspondence between the satellite imagery that Planet collects at the 3 m resolution and those LiDAR collects. And we can use the tools of AI to take the places where we have both and then extrapolate to the places where we only have satellite imagery. And that allows us to build a volumetric, three-dimensional model of the biomass of the forest. Now, why is that important? It's important because when you understand the structure of the forest, you understand how much carbon is there, and that carbon is really important for carbon accounting, for instance. It's gonna be really important for helping to fulfill the mandates of the EUDR. It's also critically important for meeting the needs that we heard from CAL FIRE, about where are the fuels that might burn? So you can imagine how important that is to utilities, to understanding the kind of structure of forests, right? To understand where's the defensible space around my power lines. It's also critically important to understand when we're actually working on thinning the forests, when taking out those fuel loads over time, are those policies actually being fulfilled? How do we measure the risk of wildfire? How do we measure and insure against the risk of wildfire? Those are all critical questions that involve utilities, insurers, regulators. All of those actors need a common picture of what's happening in the forests. And that is to say nothing of the people who are issuing carbon credits today. There are all kinds of new markets around carbon credits, and I'm gonna talk about them in a second. But let's take a minute. Just, I wanna show you what these products look like. Could you just open up the video for us? That's great. So we're zooming in here on a piece of California forest. This happens to be a place where you've got commercial forestry, there's been fire on the landscape, there's been some illicit activity on the landscape. And, you know, as you can see, one of the things we love to do at Planet is to just show continuous before and afters. The fact is that we can train algorithms to find all the places where there's been change at this landscape scale. But then, by applying these algorithms over this forest canopy, we can actually see the three-dimensional structure of the forest carbon over time, and that time element is critically important. That's actually what you're gonna see here. This is above-ground carbon for this very same place, and that carbon is both an asset and a risk, right? It represents something that could burn. It also represents something that can be sold if it's managed correctly to secure carbon on the landscape scale. Now, there are gonna be all kinds of applications for these tools. I wanna share with you, this particular application was built by a partner of Planet we've worked with a long time called Upstream Tech. This is a product called Lens, and it's specifically designed to take forest change and landscape-level change like this and make it accessible to these end users. So we're gonna see all kinds of applications like this, and we're already seeing significant demand for these products from all these different sectors. Now, there are countless applications, as I said, for these, but I really just wanna highlight two. You can come back out of the demo if you wouldn't mind. The first thing I wanna talk about is involuntary carbon markets. Now, some of you might have heard recently that there have been really pointed questions raised about some carbon credits, and that those questions about their long-term climate benefits, whether or not they're really delivering, have caused crises of confidence for issuers. We've seen the market for carbon credits begin to kind of wobble a little bit. And these kinds of tools are going to be absolutely essential for ensuring that we deliver trusted, high-quality, science-grade credits, and that we monitor them over time to make sure that they're delivering. This is the thing that sort of acts as the guarantor of the market. That thing that Planet can do is not just provide transparency, but in so doing, provide trust. Okay, the other one I want to talk about is about the realm of EUDR. And just to give you a sense of how big this particular opportunity is... Oh, wait, sorry. Can you guys go back? Yeah. Sorry, I'm not sure what happened to this slide, so I'm just gonna tell you the statistics rather than show them to you. Starting in 2025, we're gonna see these regulations take effect, where the EU is going to mandate that companies that are trading in cattle, cocoa, coffee, oil palm, rubber, soy, and wood are not going to be allowed. They're going to be required to do a high-grade due diligence to ensure that the importation of these products or the production of these products or any products that were derived from them, so this is not just raw commodities, it's trees and paper, just to give you a sense like that. They're gonna have to certify independently that none of these commodities were produced as a consequence or in connection with recent deforestation or forest degradation. And the only way you can do that is with these kinds of geospatial tools. According to the European Commission and this organization, Eurostat, sorry, up to 1.2 million companies are gonna have these kinds of requirements on them. So they're gonna have to independently certify, and that certification is gonna have to run all the way through the supply chain. It's gonna cover $130 billion worth of traded goods, and the anticipated cost, just of the annual anticipated compliance costs, are north of $5.6 billion a year. So the E.U. has said we have to dramatically accelerate climate action, we have to leverage global markets, and we have to enforce climate-sensitive trade. And again, the only way to do that at a global scale, because it involves commodities from Indonesia and Brazil, it involves places all over the world, is with this kind of global monitoring capability. There are many actors. There's entities like commodity providers and aggregators. Those are the people that collect all those commodities, the shippers, the government bodies, they're all gonna need a common picture. Okay, so while the E.U. is creating new market imperatives, we're also seeing this kind of work, monitoring carbon, beginning to be extended beyond the realm of forest into other places. And I wanna tell you about the second one in the area of what we call coastal blue carbon. Now, that's the carbon that exists not in deep in the forest, but at the edge. It's the mangroves and seagrasses and salt marshes. It's these things that, where huge amounts of carbon are contained. In fact, actually, an independent scientific assessment suggests that these kinds of ecosystems secure carbon at a rate 10 times greater than the mature tropical forests that we're used to monitoring. In fact, a scientific assessment in Nature in 2021 valued the carbon that these ecosystems secure. Let's see if we can do this. Yeah, at more than $190 billion a year, ±$30 billion. Now, whether that number is at the high end or the low end, I can guarantee you one thing, it's going to get bigger, right? Because we're going to need to affix all this carbon, and again, carbon markets are gonna play a critical role. Now, we've seen already demand from corporates who are interested in investing in these kinds of areas, and Planet's data can act as a critical guarantor of the supply side. There's actually more demand for coastal blue carbon today than there are coastal blue carbon credits to be issued in support of that, meeting that demand. And in order to deliver against that, we're gonna see entities like Planet play a really critical role. Now, to figure out the details, we've been working very closely with The Nature Conservancy, the scientists and conservationists of The Nature Conservancy, to understand how our data can play a role in monitoring these kinds of assets. This is really forefront work. It's important to know that... If I could, could you go back a slide? Just one second. So mangroves have been mapped, but at low resolution. Seagrasses have just been mapped, but with no time series, and no one's ever done the kind of salt marshes work that we've done before. This is all at the very beginning of the next stage that we're gonna see. So if we go forward, this is what the most common public source of the measurement of mangroves in this particular case in Papua New Guinea. Now, using our data, this is from public data sources, and it's an open data set. This is what the scientists at TNC were able to discover. They actually were able to map three times the number of assets. And it's important to remember, those assets are all fixing carbon, but they're also potentially the sources of the issuance of new credits. That represents a different kind of climate financing pathway, and the formation, it's also critically important because if you have three times the assets to put on a market, you are going to attract market players into that market in a way that you haven't before. So really phenomenal kinds of outcomes here. Before I go any further, I wanna just pivot to the last little theme to give you a sense of these are like the elements of work that we're doing that are coming next. This is our preview of coming attractions. The last area I want to talk about is in disasters. So we have seen from across our ecosystem, from across our, our customers and partners in every different industry, an intensifying interest in understanding how our data can be used, how our tools can be used, to blunt the sharp edge, edge of disruptions, to prevent forest fires from occurring in the first place, to respond to them more effectively when they occur, and there's a really simple reason why, and you can see it in this chart. From 1980 to 2022, this is the increase in just the $1 billion-plus scale, large disasters in the United States alone, right? If we are solely in the business of doing payouts and trying to recover from a disaster curve that ever onwards goes up and to the right, we're gonna have real problems just managing our budgets at public agencies, at, in civil governments, in disaster funds, and in insurance. So we've got to be able to use these tools to effectively respond to disasters. There's a lot of reasons why climate exacerbates these issues. You know, it makes dry places drier, it makes wet places wetter, so it means they're more likely to burn and more likely to flood. But there's a big reason why we see that increased growth, and that's for this chart, which is called the bullseye effect. The reality is that on planet Earth, with every passing day, on an ever more crowded planet, there's just more people to be affected. There's more stuff on the Earth, there's more infrastructure on the Earth to burn. A disaster that cuts through a city in 1950 is going to cause substantially less damage than in 2100 or not, you know, 2025 or 2050, just because the cities themselves have grown as a consequence. But no matter the reasons why the underlying cause, the impacts of disasters have become a major focus, and we have been seeing this really interesting set of applications of AI to these disasters. Now, this is a story that I'm gonna pick up with what happened about 18 months ago when we saw the war in Ukraine. Within a few weeks in Ukraine, Planet and our colleagues at the Microsoft AI for Good Lab began working on a new system that allowed humans to assess raw satellite imagery to determine whether or not buildings in the pictures had been damaged. So just presenting before and after images and letting trained satellite experts, some of the 6 million folks that Robert wants to hire at the UN to say, "Okay, we can see that there's damage in this assessment." And then using that data to train algorithms to monitor every single building in the entire country on a weekly basis. That information went directly to the Secretary-General of the United Nations and directly affected the UN's humanitarian response to the conflict. But then the story changed a little bit. That, that took about 6 weeks of consolidated work for us to set up, and then we deployed exactly the system. By the way, this is a picture of one of those, one of the pictures. Can you go back one, one slide, just really quickly? I, I don't know if you can see this. In this little red line here, you can see that the common reports that they had of damage, this is to water and sanitation and, and schools and hospitals, the human beings were finding six incidents of damage. The algorithm, once trained, was finding 53, so vastly more ability to process all of that imagery. This is exactly the kind of tool that Robert was talking about. Now, we took that tool, it took about six weeks for us to develop, and then we deployed it again. In this case, we deployed it in the earthquakes that happened in Syria and Turkey. You might remember those, and then we deployed it again and again. Eventually, we came to deploy it in Lahaina, as Will mentioned at the top of his remarks. In providing the information here, every time we launched this tool in response to a disruption to try to understand which buildings had been damaged, the amount of time it took to deploy dropped from weeks to days and from days to hours. Ultimately, here, it got directly in the action cycle of the frontline responders at the American Red Cross. Our colleagues at Red Cross said, "You know, normally, we have to process so much information that we couldn't possibly do this." Kasie Richards, who's the Senior Director for Situational Awareness at the Red Cross, said, "The volume of information that can be synthesized would take us days and days, and at that point, the disaster would be over." But she said, "In this model, it's allowed us to operate in a much more consolidated period of time." And as soon as this started to occur, we started to hear the phone ring from all kinds of actors who want to be able to take the information and deploy it as quickly as possible. Now, I wanna share... and there's said all those things. I didn't actually know that slide was still in the deck. Okay, last thing I want to tell you about is one other piece of the work that we're doing in humanitarian response, in response to disasters, that I think is gonna be transformational. And that's work that Planet and the Microsoft AI Good for Good team and a specialized public health organization called IHME. They're a world-class public health organization based at the University of Washington. We're working together right now to map populations around the world at the highest level of spatial precision and temporal precision. And why does that matter? It matters because knowing where people are is critical to saving lives. You've heard a lot of discussion today about where's the carbon, where's the water, what's happening on the landscape, where are the objects? Those are all really important questions, but ultimately, the denominator for many of these problems is, how do we help people deal with these problems? So what we're doing is we're providing satellite imagery, and Microsoft is providing the analytics, and IHME is providing the spatial demographics that allow us to produce the most up-to-date models of where human beings are living anywhere on the Earth. This is an incredibly important thing. It's less important here in San Francisco, which is continuously monitored and very information-dense. But if you look at the second and third and fourth most populated cities in, say, a country like Niger, it happens to be this country, Zinder, in Niger. They haven't been measured for decades, and they're growing at 5%, 10%, 15% a year in some cases. This is Niger the last time a census was conducted. This is the growth that's happened since, and what we're able to do is use these algorithms to produce spatial models of population that all of that red stuff, sorry, all that purple stuff, all those are indications of where human beings are living today. They weren't living there yesterday. So if you wanna deliver public health, if you wanna understand climate risk, if you wanna understand disease burdens, or you wanna understand where people might migrate to, it's a foundation for all of that kind of humanitarian action. So what we're doing now is taking those population models, and we're taking these other layers to begin to understand risk in ways that we fundamentally couldn't before. Now, these are all, as I say, previews of coming attractions. This is what is happening at the forefront of the application of these tools. It's a really wonderful time to be working as Robert said. In each of these and many other cases, Planet is developing the products and partnerships that will build new markets and bolster existing ones, all while fulfilling our mission as a publicly listed public benefit corporation, as an impact accountable organization, I like to say. I believe that we are proving that doing well and doing good are not at odds, but actually are fundamental strategic complements to winning massively in a future that is really worth living in. So with that, I'd like to say thank you for listening to me. Delighted to share all this with you, and would you please join me in welcoming up to the stage our Chief Financial Officer, Ashley Johnson. Thank you, Andrew. In case you're wondering why our employees love working here, everything that Andrew just described is what just excites us every day to get out of bed and keep working really hard at everything that we're doing. I know, Pavlovian, you're probably opening up your models right now, but actually I get to do something fun first, so you can hold on those. I'm actually going to introduce you to one of our customers. So Melanie Desjardins is the Director of the Northwest Territories Centre for Geomatics. Melanie has worked in public service since 2001, and has deep geomatic expertise through her works at Parks Canada, Aboriginal Affairs, and Northern Development of Canada, and the Government of Northwest Territories. So hopefully, Melanie is online. There we go. Melanie, welcome, and thank you so much for taking time out of your day to join us at our Investor Day. Thank you for having me. Great. So just to jump right into it, it'd be great if you could share a little bit of your background with our audience. Talk a little bit about your top priorities and how satellite data aligns with those priorities. Sure. Yeah. So I'll just start by acknowledging that I'm here on Chief Drygeese's territory, the land of the... Home of the Yellowknives Dene First Nation. So my background, as I said, I've had a long career in the public service, and I've done a variety of things with geomatics, mostly around conservation and now serving all the different departments within our territorial government. Great. In terms of the remote sensing data you use, can you talk a little about what you've used in the past and what's kind of brought you to Planet's data? Yeah. So in the Northwest Territories, we've been doing remote sensing for about 35 years, and we have typically done acquisitions on a very small basis because we have such a large territory. The territory is about 1.3 million sq km, and so, as you can imagine, we don't necessarily have the budget to buy 10-cm imagery for vast areas of land. So it's typically been very piecemeal. And so working with Planet Labs and starting to use those subscription services, and especially the daily, almost daily images, has really been transformational for us. It's given us access to some situational awareness that we've never had before. So that's been kind of like our transition, and we use this for so many different applications, but a lot of infrastructure management, flood mapping, fire, many, many applications. That's great. Can you talk a little bit about what your work looked like before and how this has changed your ability to do those applications? Yeah. So, for instance, forest management in our organization, they do fire mapping, and they typically would monitor our wildfires. And not every wildfire is managed. It's a natural part of the ecosystem, but where we have values at risk on the land, that's where they would get involved and do a lot more fire management or fire operations. So typically, what they would do for that is send an airplane up in the air and look for hotspots. Well, that is very costly, and with the advancement of these almost daily images and having subscription services, they were able to save a lot of the funds that they would typically use to deploy airplanes and helicopters, and do that through the situational awareness tools that the daily images provide. Great. I know that we've worked with you in a number of different aspects. Can you talk a little bit about your journey in exploring other ways to use Planet's data? Some of the other ways that we use Planet data right now is, you know, anytime we do flood monitoring, we have nine flood-prone communities in the Northwest Territories. A lot of them are only accessible through aircraft, so it's really important to get a good sense of what the situation is with break-up, as our rivers start to melt. So that's been another way that we've been utilizing the Planet data, is to do that monitoring, and we appreciate the ability to do some trans-boundary monitoring as well, so that it's not just within our territory, so we can get some kind of advanced notice of when that's gonna happen. Other ways that we could use this data, and it's something that we're starting to investigate a lot more, is around infrastructure monitoring, our highways, bridges. Many of our communities, they're serviced only by one road in and out, and so the investments made in those infrastructure assets is significant. And so when we are faced with things like permafrost melt, and you have massive slump events that happen potentially along a highway, and risk cutting off your communities to supplies, fuel. So that's another way that we're planning on using more of the Planet data, is to do some of the situational awareness around that. That's great. And yet another example of the way that climate change is really creating economic challenges for communities. So when you think about other opportunities to work with Planet, what excites you and you know, what areas are you exploring of maybe doing more work with us? Well, that presentation that we just received was very eye-opening, and obviously, climate change is a massive issue for us in the Northwest Territories. The North, Canada's North, North across the globe, we're facing climate change at a rate much, much faster than any other parts of the world. And so for us, things like greenhouse gas emissions and monitoring that and quantifying that is something that is really of interest to us. The Carbon Mapper program that is in development right now with Planet Labs is something that we're monitoring closely. If we can somehow use this as a predictive way to monitor where there may be a mass slump event due to permafrost melt, this could be a way for us to kind of get there in advance and do any work that we might need to do to stabilize a highway along our coastlines. That's great. And are there any products that you're most excited about beyond. It sounds like you're looking into hyperspectral. Are there other things you saw today that you could imagine deploying within your teams? Honestly, one of the biggest challenges for us right now is getting people across our organization familiar and using the products every day. I think we're only scratching the surface right now. The examples I provided are examples that I'm most familiar with because of the people that we've worked with, but we know that there are a lot of other applications that we're not even tapping right now because the business really needs to kind of get up to speed with what's going on and the tools that are available. So I think once we start understanding those needs a bit better, we'll be able to really investigate a broader scope of what Planet Labs has to offer. That's great. Well, hopefully, your teams will have an opportunity to see some of the stuff that Kevin highlighted. And all of that is in service of enabling both experts to be able to do more with the data, as well as those that are less expert than you in working with geospatial data to actually find value. Thank you so much for joining us today. We really appreciate it. We appreciate the partnership. And thank you for being here. Thank you for having me. Appreciate it. Great. Okay, I'll put my CFO hat back on, and we can talk a bit about the numbers and how we're thinking about the road ahead of us from a financial lens. Will mentioned earlier, our fiscal 2024 has been challenging in many respects. We've had elongated sales cycles. We've had heightened solar activity, resulting in accelerated depreciation of our satellites. And of course, we started the year with a headwind to growth related to the loss or reduction of a couple of our larger commercial accounts. Nonetheless, our teams continue to execute, and we've set new records on revenue. We've sustained our gross margins in spite of the accelerated depreciation. We've readied new fleets for first launches while making progress towards EBITDA breakeven and maintaining a strong balance sheet. While the pacing of new business has lagged our, our expectations, some of our core metrics remain strong. So the contracts we sign with customers are, on average, quite sizable, with an approximate $200,000 average ACV across our customer contracts. And these tend to be longer-term, repeatable business, over 90% annual or multi-year contracts. And our direct margins to service these accounts remain very high, which provides us with critical leverage in our business model. Since going public over the last couple of years, we've accelerated our growth rate to a 29% annual compounded annual growth rate. This acceleration has been driven by the investments that we made across the products as well as our go-to-market. The large deal nature of our business can cause some variability in the timing of growth, and it can also create step function increases when we do sign large expansions with our existing customers. Our focus is on driving net dollar retention rate to ensure we continue to expand with our existing customers. A key ingredient for achieving those results is the strong customer satisfaction scores, which Kevin highlighted. Launching new solutions enable our sales reps to cross-sell incremental value in the form of other datasets and other analytics. These investments in our product around solutions for our core markets also enable us to expand our customer base, and we're focused on streamlining the sales motion to accelerate the time to close new business and leverage our platform for the fast onboarding and time to value for our customers, and to enable us to work with our partners to expand our reach more scalably. So while the timing of large deals can be challenging, and we've certainly felt that challenge this year, we do have a track record of consistently adding new customers and delivering growth. For many enterprise software companies, the definition of a large customer is a customer generating $100,000 or more of revenue. As you can see, we have a large and growing base of customers at this size, many of which are actually well into the six figures. We are also fortunate to have a strong and expanding base of large customers generating $1 million or more of revenue. As you heard earlier, we have a robust pipeline of large customer opportunities that we're actively pursuing, many of which are cross-sell or upsell opportunities with our existing customers. Importantly, the acquisition of Sinergise provides us with an e-commerce platform that enables customers to onboard more seamlessly and with minimal sales involvement, meaning we can leverage a more automated experience to offload the larger number of smaller opportunities that Kevin showed in the pipeline metrics and free up our account executives to pursue our pipeline of larger deal opportunities and really drive that focus. Sorry, the slide needs to be moved forward. We've got a repeat of... Can you move it? Sorry, move the prompt. I'll go by memory. No, keep going. Sorry. Net dollar retention rate. I actually know this slide very well and didn't even need the prompter. We focus religiously on this metric. We measure it a little differently, so we start each year at 100%, and that's important to understand, but this is the way we think about managing our business. We measure the book of business in hand by annual contract value of existing customers at the beginning of our fiscal year on February 1. As these contracts come up for renewal, if they expand the ACV of the contract, our net dollar retention rate expands. Obviously, any of those expansions are offset by contracts that renew at a lower value or that renew... fail to renew on time or at all. Our pace of expansion and Net Dollar Retention Rate has been slower than expected this year, as a few large government contracts either signed later than anticipated, or they renewed but aligned the start date for the renewal with a larger expansion timed for later in the year. We nonetheless have confidence in our current target of 115% Net Dollar Retention Rate for the year. Our Net Dollar Retention Rate in the first half of fiscal 2024 is actually better than what we saw in the first half of 2022, and we see a similar path to locking in our largest renewals and growing and expanding our existing customer revenue. So let's talk about how we use that for our guidance. Our guidance for revenue growth this year is approximately 15% at the midpoint. This reflects the challenging year-over-year compares that were caused by the expiration of a very large legacy contract that drove approximately $12 million of revenue last year, but less than $1 million in this fiscal year. It also reflects the timing of new business and expansions this year. If we adjusted solely for that one legacy contract, our business would have grown approximately 20%, which illustrates the path to 20% growth that we see in the business without needing to drive significant revenue from new business. But there are a number of areas where we're focused to improve our execution and enable us to secure not only this baseline growth rate, but also to drive growth on top. Did I mention we're very focused on net dollar retention rate? This is obviously a key lever for us, and we have the ability to drive net dollar retention rate higher, just like we did last year, through focused selling around the business. In those areas where we can drive the fastest time to value, we can work with those customers to expand our footprint, just like the conversation I just had with Northwest Territories and other customers examples you heard today. While 115% NDRR gives us a clear path to getting to that 20% growth, we aren't satisfied at these levels. We see a very large market opportunity in front of us. We have really valuable component to the problems and challenges that our customers and our governments are facing, and we need to have a focused execution in our, in our sales team and our go-to-market to ensure that we acquire these customers, we make them happy, and we deliver value on top so that 115% is, is a floor in terms of our net dollar retention rate. We also have multiple markets that represent very large SAMs for Planet, and we have proven solutions and lighthouse customers in these markets. So we're launching focused marketing campaigns and enhancing our sales processes to, to pursue those highest value opportunities with the highest probability of success. And with the high-quality pipeline we've continued to build throughout this year, we should have our reps focused on signing more business in the first half of the year. That means we're delivering as much revenue contribution as possible within the year and providing a clear line of sight to renew and expand in the subsequent year. And with the enhanced automation of our self-serve platform that we now have, our sales team's bandwidth should be clearly focused on those accounts that are six- and seven-figure opportunities, focusing them as early in the customer journey as possible to support this strategy of land and expand. And by focusing on our core verticals with targeted solutions, our sales team can deploy repeatable playbooks that support an accelerated time to close and higher win rates. Obviously, an important part of our growth strategy is the fact that we have a diversified mix of customers across geographies and across vertical markets. We're gonna continue to add feet on the street in those international markets, where we have seen consistent and repeatable success. As Kevin highlighted, we're developing solutions tailored to the greatest needs of those customers in specific vertical markets. We remain focused on building APIs and automations that allows us to not be overly concentrated in one particular market or one particular, particular geography, which should provide a solid buffer against any specific industry challenges that may arise. I'll shift gears to revenue recognition for a little bit, 'cause this can be a point of confusion. Our revenue recognition model sometimes causes variability in growth from period to period. You saw that last year, when we saw some accelerated usage across customers, versus this year, where we ran into some challenges of those contracts running out of usage and not having an opportunity for early renewal. To understand that, it's important to understand our revenue recognition model. So for one, our customers sign fixed-price contracts, typically for a period of one year or greater. They're paid typically upfront annually or quarterly, and we, in fact, saw one customer this year sign a large multiyear contract and paid that upfront, so we saw an increase in deferred revenue. But we also have customers, government customers in particular, that statutorily can't pay upfront, and so those customers tend to pay quarterly or in arrears. So deferred revenue isn't quite the signal for us on the business that you might see in other businesses, notably SaaS businesses, but it's something we're working towards. Revenue recognition is similar. There are aspects of our business that look just like it, what you would expect in a SaaS company, where customers are signing an annual contract. It's recognized on a ratable basis, so very predictable. And that's about 2/3 of our business, and the portion of our business that's recurring ACV. We also have about 8% of a contract that can be recognized upfront. That's typically the archive revenue, and typically, that's when we see a significant download component that gets recognized upfront. The final piece is this 1/3 of the recurring ACV that's recognized on a usage basis. This is typically in contracts where a customer has limitations on the amount that they can download. So for customers that might potentially download large amounts of data that could cause egress charges to us that are meaningful, we do put limits on that, and then we have to recognize revenue as the customer downloads that data. Similarly, when customers buy tasking contracts, often there are quarterly or even monthly minimums for what they need to use, and that lends a little bit more ratability to the revenue recognition. But if there are no quarterly minimums or if they exceed those minimums, that'll cause variability quarter to quarter in terms of how we recognize revenue, because we'll recognize it as they use those contracts. So we're working in terms of how we're thinking about our contracts on ways that we can make the model more predictable, easier for our customers, because they also are challenged with understanding how much have they used and how much they have left. So Kevin talked about some stuff that your eyes might have glazed over a little bit on the admin side of what we do, but it's really important to our customers, and it's really important to us in terms of being predictable. It will also help us when we run into things, like I mentioned last year in Q2, where we saw a spike in usage, and our customers that weren't able to get ahead of that and secure budgets for early renewals ran into a gap where they could no longer access the data because they'd used up all of their contract. Frankly, that caused a gap for us on revenue, so it was frustrations on both sides. The good news is, when those do come up for renewals, we typically see that contract expand so that they don't run into that issue again. We wanna do what we can to avoid that situation going forward, and a lot of that is just the tooling, and how we work with our customers in contracting in the first place. Enough on rev rec. Gross margin. We often talk about the scalability of our one-to-many business model, and this is really unique in the earth observation industry because most of our predecessors in this space sell on a capacity basis. The scalability in our model means that our direct margins are very high. Costs are relatively fixed, cost of goods sold, and those include our cloud hosting costs, satellite depreciation, our mission operations teams, and our support personnel. Excluding depreciation, our non-GAAP gross margins have been greater than 65% over the last 14 quarters. As our revenue growth has accelerated, our non-GAAP gross margins, excluding depreciation and amortization, have actually been north of 70% for the last six quarters. These are higher than what you see in a lot of other satellite businesses, and this is attributable to the scalability of our one-to-many model. Our gross margins have also expanded consistently, even including depreciation and amortization, as you can see. Q2 was impacted, as I mentioned, by some accelerated depreciation due to the heightened solar activity, and yet we still sustained gross margins in spite of that impact. And again, since a lot of times people forget, I do wanna remind you, a lot of companies, other companies in our space, when they report gross margins, they exclude depreciation and amortization. We include it. So when you're doing your assessments at comparables, make sure you do it on an apples-to-apples basis. So just as having scalable gross margins are important to our path to EBITDA profitability, carefully planning and managing our investments in capital expenditures is critical in our path to free cash flow generation. We have reiterated our commitment to EBITDA profitability by the fourth quarter of next year, and that is an important first step in becoming cash flow positive. But the timing of that ultimate goal of cash flow positivity is dependent on our pace of growth CapEx, pace of investment in CapEx and growth CapEx in particular. So we have been investing this year, and we will continue to invest next year in the growth CapEx we need to support the build and launch of our Pelican fleet, in particular, which includes the tech demos that'll be launching in the coming quarters, as well as advanced procurements for the first blocks of satellites, that will enhance and replenish our high-resolution fleet and incremental ground stations that we need for enhanced latency. As we launch Tanager, additional Tanager satellites beyond the first R&D satellite, we'll see some incremental CapEx for that program as well, although to a smaller degree. But beyond next year, we have the ability to flex the build-out of these fleets to match customer demand, so we can move faster if we see incremental expansion of revenue beyond our baseline growth rates. We can also choose to move to a maintenance CapEx model more quickly and just align CapEx and the subsequent depreciation with the pace of top-line growth. It is this critical lever, enabled by our agile approach to space systems, that determines our path to cash flow breakeven, which we are determined to achieve while maintaining a strong balance sheet. We've said from the beginning, our data-as-a-service model is powerful in its scalability as a business model. We raised capital as we came public to enable investments across the business in new products, new fleets, and an expanded commercial footprint. We also invested in the back office to support the requirements of public company operations. The pace of our growth this year has provided us the opportunity to step back and more closely analyze which investments have had the greatest impact for our customers and our market capture, and to align our OpEx footprint accordingly. We're very clear on our target model, and while we will continue to lean into R&D investments that expand our technology moat and increase the accessibility and actionability of our data, especially through AI, we'll do so with an eye to scale and profitability. Similarly, a critical benefit of our recent Sinergise acquisition is the advancement of self-service capabilities for our customers. Between these kinds of platform enhancements and the power of our partner ecosystem, we have the ability to drive down our cost of market capture, also in service of overall profitability, while leaning into those areas most promising for growth. While we were in a full sprint to enable our operations to be public company-ready, with the time under our belt and first-year SOX behind us, we're now focused on how we make our operations more efficient, and able to support the growth in the business without needing to make a lot of investments in our back-office operations. Necessity is the mother of all invention, and drawing the line in the sand to say we're going to get to profitability by Q4 of next year means that we have to prioritize work around efficiency and optimization, and those types of investments and focus can often take a backseat when you're in a high-growth environment. To sum it all up, we are very clear on our priorities from a financial lens. We have the ability to sustain a base growth rate of 20% and higher on our top line if we keep our eyes on sustaining and improving critical metrics, such as on-time renewals and Net Dollar Retention Rate, pipeline close rates, and sales cycles. With focused execution, we should drive these metrics higher. We have a commitment to Adjusted EBITDA profitability by Q4 of next year, and the ability to do so is driven by our scalable business model, a focus on efficiency, and an aligned investment strategy behind the key areas of growth. We have a critical lever for our path to cash flow breakeven, which is enabled by our agile space systems. We'll lean into CapEx growth if the revenue acceleration supports it, and we will accelerate our path to maintenance CapEx following the initial build-out to preserve cash and align the pace of our CapEx investments to the pace of market capture and revenue growth. Ultimately, our goal is to build a high-margin, cash-generative business. We have a balance sheet with a strong cash balance and no debt to enable us to get to that cash flow profitability without needing to raise additional capital. We carefully manage dilution, and we're striving to build a long-term sustainable business and deliver shareholder value. So with that, I'd like to invite the executive team to come back to the stage, and we will open the floor to Q&A. All right. So, mic's on? Okay, yeah. Yeah, Q&A. First, hand up. Jason. Take it away. Great, thank you. It's on? Yep, great. Thank you. Two topics I wanted to cover, so I'll go first to Robert and Ashley, and then Andrew and Kevin, if that's okay. First, I'd love to get an update on the EOCL program and just getting an understanding of the option years and whether there's been any change that you've seen there. And then, Robert, talk to us a little bit about what you're seeing inside federal government and where the priorities are going to be made, if indeed we, you know, end up in sequester at the end of the year, God forbid, because the last time that we faced sequester, we saw a 50% reduction in the EnhancedView program. So that's topic one. And then Kevin and Andrew, I am really intrigued by the CU and the ARM, and the potential that that has, because I think a lot of the customer adoption that we're likely to see for you all will be driven by regulation. 100%. You mentioned 1 million potential customers, so that, Kevin, hopefully gets you excited. A $5 billion addressable market to, Compliance. Address all of this, the compliance of it. Yeah. I'm kind of curious, you know, how do you identify those 1 million customers to go after? And that $5 billion number, how much of it do you think is gonna get spent on geospatial data? Okay, I, I'll start. So we just rounded the first year, obviously, on the EOCL. For those who aren't familiar, it's a large government program called the Electro-Optical Commercial Layer. And so the first time the options kind of come up for either renewal or expansion would be come June of next year. Good news is we have a great relationship with that customer. The program is going well. We talked about even with NRO, we expanded with them this year in terms of them exploring some of the work around hyperspectral data. And as you know, the way the contract is structured, it locks in availability of funds, but also gives flexibility for them to explore different ways to work with the partners in the program. So nothing beyond that to report at this stage, but feel very good about the way the partnership's progressing. So I'll let Robert take part two. One of the first rules of being an analyst is knowing your limitations. So let me just start there, right? I'm not gonna try to predict the resolution, what's gonna happen mid-November, what's gonna happen. But I appreciate the overall intent of your question, right? Macroly, where is the government heading, and how do you, how, how do we see that shaping up? I see the company's offerings being well timed from the following assumptions I'm gonna make about future government purchases, and that has to do with efficiencies and services. And if I was back in government and was faced with tight budgets or a challenging out-year projection or whatnot, I would be looking for opportunities where I could kind of protect myself and say, "Look, I need the following outcomes or outputs." One example I would highlight is NGA has the Economic Indicators Monitoring contract that Planet has contributed to. That's gonna run out over the next few months, and then will be recompeted under a new contract called Luno. And again, we'll see what Congress applies to that program, but I'm optimistic that programs like that will be, if you will, what you can rely upon, even in uncertain times. So sum is, I remain optimistic because I think we'll be quite competitive in those types of contracts. And I'll speak briefly to the CAP program, which is sort of today's example of the regulatory program that you're talking about, and I'll let Andrew speak to EUDR. But CAP, I mentioned, is this $50 billion European program where they're paying out agricultural subsidies to farmers who are following sustainable best practices. And for these kinds of programs, you need satellite data because you have to do this at scale. You also need AI and machine learning because you need answers. You need to know when someone, you know, implements cover cropping, they're tilling their fields, things like that. You can tell these things from satellites with a little bit of AI, and we're seeing good traction there. We've already got a number of deals that we have in place with countries in Europe around these programs, and we have more opportunity ahead. So you've got that program today. I think you've got a lot of interest from other countries in the world around what Europe is doing in this space, and so we see opportunities more broadly. Then you have EUDR coming up, which I'll let you talk about. Yeah, it's a great question on EUDR. The reality is that the whole supply and value chain is gonna have to be informed. And so that, you know, the—right now there's lots and lots of discussion among all the different participants in the chain, but that—those participants are not just the, say, the growers of commodity or the consolidators of a commodity. They're not just the importers or the CPG companies that eventually... But But all, all of them are having conversations about this right now, for sure. But they're also the back-end, you know, enterprise supply chain management firms, the big database firms. Like, because those things have to pass through the system in such a way that if someone is audited for their compliance with this particular regulation, that they can readily show. So there's a lot of work to do, but the market's really coming together, I think, very quickly, for sure. And in terms of how we source them, I think we're looking for the places where we can inject this kind of - exactly these kinds of insights. Like, you need Field Boundaries because you have to be able to say, "This is where I'm growing," and you need to understand forestry. So it's gonna exist at the intersection of some of the tools you saw today. I'll stop there. Yeah. Thanks. I'm gonna ask a big picture question. It's meant to be respectful- For sure. - but I'm gonna try and tie it all together from an investor standpoint.... you know, with the upcoming launches at Vandenberg, super exciting. I know a lot of work's been done there. Kevin, I really appreciate the resets and tuning adjustments in a product sense. But again, this is all against the backdrop where we've had two revenue resets lower through the course of the year. And so what I'm trying to understand is, every time I attend one of these, I come super excited. You're doing really great stuff. But where is the mismatch, if you will, on what I'll say, price to value? And on all the customer examples, I never come away with an understanding of how the procurement process happens- Mm. how customers consume your product. Mm-hmm. The contracting is fixed price. So if they're consuming it, loving it, but they've got a budget challenge, how do we fix that? So what can be done on price to value- Mm-hmm. And on the customer engagement, budgeting, procurement process, so they can consume as much as you want to give them? Yeah. Well, a great set of questions. Maybe I can just kick off, and maybe you can add a little bit on that consumption piece. Like, we obviously have this big opportunity. I think it's mainly about timing, right? As we execute towards it. There is a bit of a mismatch and, you know, we're not happy, of course, and satisfied with resetting guidance that you mentioned. As I mentioned, we have this huge pipeline of opportunities and our job to bloody execute against them, not just, you know, to put them through the pipeline, but to actually turn them into real contracts, right? As you can see, I think we're getting more focused on that. And to your point about procurement mechanisms, we are learning this space. You know, we said, for example, at the end of the Q1, in our Q1 earnings, that sales cycles were elongating. Deal sizes were reducing a little bit, okay? We were affected by the economy, and then we were able to now see, looking more closely at data, the second one of those recovered back, and the first one of those is mainly dominated by the switch of business from commercial to more civil government. They have longer sales cycles. That's just the nature of that business. But as we understand that, we can get more effective. There are efficiency improvements in how we go towards that, right? I'm glad you come into these events enthusiastic. I hope you leave enthusiastic as well. But, you know, I'm sorry you haven't had a sense for that specific value proposition that they're getting out of it. But, you know, we're trying to give... We're trying through these customer examples to touch on that. I don't know if you wanna sort of try and summarize that a little. Yeah. I'd say a couple of things. I mean, one, before Planet, there was no daily scan of the Earth. So as we go into these customers with a lot of these use cases, it's not like they're replacing their previous daily scan of the Earth with ours, which is a bit better. It's replacing old manual processes, or it's a new capability that they never thought they could do scalably, that now they can. Yeah. But it takes a certain amount of expertise, right? And that's. So that is one of the reasons that we're so focused on moving up the data pyramid. Because if you come in with a lot of raw data and the customer doesn't have the expertise, then you're talking about, you know, integrating, you're bringing in other partners to help them, or they're writing code. Whereas if they can just plug in a data feed of a geophysical variable that they care about, forest carbon, soil water content, or they can use AI and just, you know, get a stream of detections of the thing that they care about, suddenly that's a much easier proposition. So as we go up the data pyramid, as we have more powerful products, a lot of these things get easier. It's also one of the reasons that this shift from trying to solve all the problems and trying to go after a lot of different kinds of deals, to focusing on civil gov, defense and intelligence, and agriculture, is gonna help us streamline, you know. We have a lot of expertise in those fields. We know where the big deals are, and rather than sort of a new motion for different verticals, we're gonna focus on letting our partners drive a lot of that, and we'll stick to our core, where we can focus on bigger deals. So, Kevin, just again, this notion of price to value, like, that's what I'm—I think we're trying to understand is, you know, Robert, yeah, I'm sure, you know, finding where the Chinese satellite was launched was hugely valuable, but there's one buyer, okay? What do they pay for that, right? In other words, on the, you know, carbon at the tree level, I mean, that's unbelievable. But do you price per variable, or is this a all you can drink- Mm-hmm. You know, fixed price? I mean, that-- I think that's the price to value algo- Yeah. Yeah. that, you know, trying to understand the intrinsic value of clearly you're building something, a proprietary database of some incredible value, but are you really getting that price to value? Yeah, yeah. Yeah. -through the contracting process? Yeah. Well, most of these products are ratably priced based on, you know, sq km area of your concern. But it gets back to a lot of what you said. There's one customer for that synthetic example. I'd argue there are a lot of customers- Yeah Because you've got, first of all, every MOD in the world cares about solving these problems. But before you have solutions like that, you have the raw data itself, and the answers they want are in there. But if it's too hard for them to get at those answers, and I'm using, you know, MODs as one customer, but you could apply this to lots of other customers. If it's too hard to get at those answers, then either the cost that they need to, the, sort of the effective cost of the solution increases, 'cause they need to bring engineering teams in or look for other partners, or they, it's just too much work, and they don't do it, and they don't see the value. When you can bring these higher level tools to solve the problem, AI and Planetary Variables and other things, all of a sudden, you decrease the cost of that solution, and you decrease the time to get to that solution, and that's why we're so optimistic about these, these products that we're bringing out. Just to add one more thing onto that, it's rarely a budgetary question, it's more the technical solution question, right? Because we're unique, if it can solve the problem, we're the only game in town, and especially with the government, it's not driven by the budgets, it's driven by policy objectives. But then the technical piece is where the rubber hits the road on how quickly we can actually get them to that value as opposed to theoretical value, right? Right, we better carry on. Yes, you got a question online? Yeah. So we have a question from online. And for our online audience, just a reminder, if you'd like to ask a question, please raise your hand using the Raise Hand function in Zoom, and we'll get to your question. The first question comes from Gaby Knafelman of Morgan Stanley. Gaby, please unmute your mic and ask your question. Hey, guys, it's actually Kristine Liwag dialing in here from Morgan Stanley. So thanks for taking my question, and I wish I was there in person. It was really helpful to hear from your customers and how they use Planet. The use case is very compelling. But if we look out at the continued maturity of AI technology, one can imagine the example where a customer is looking at soil moisture data using Planet images to be linked to a satellite weather data and create some sort of predictive analysis and pricing of insurance claims, and have something more proactive versus something backward or current-looking view. So first, like, how far away do you think we are in terms of fully integrated approach to use multiple data systems like that? Second, you know, how integral is Planet's capabilities in that environment, where you would be only one of many potential data points, especially if more satellites are being launched? Third. Sorry, there's a lot of questions. To keep your relevance, do you have to be more of an AI company than an imaging company? So those are the three I have. Okay. Well, thank you for the series of questions. We'll try and get to. Just first point, as we were talking about, towards your point about predictive analytics, I think things are going in that sort of direction. But we can already get to some benefit of prediction, even with some of the Planetary Variables we have. So, for example, in a lot of the disaster response use cases we have seen, including Lahaina, that I mentioned in my presentation, there's the quick response afterwards. But then there's the realization by that state government that had we had this data before, we could have seen that there was invasive species, that they were too close to power lines, that there was very dry soil, and that could have been an preventative work, like the work that the CAL FIRE representative was talking about, could be done to get ahead of that. So preventative work there can be done through some of these signals. In fact, the soil moisture content one is one which is starting to be used for predictive work, where people look for areas where it's getting too dry, and in which case, it might lead to a drought and a crop failure, or when it's too wet and when the soil is saturated with water, it means the next time it rains, it's gonna flood. And so you can have a little bit of a flood warning or a flood high-risk area map from this sort of work. So that, yeah, it is getting there, but I would say that there's a huge amount of power just in being able to analyze and understand the data, even in the present, let alone the prediction. Anything to add? Yeah, I was just gonna say, I really like that question. I thought it was great. I think one of the marks of success of a technology is that it vanishes into the background over time. Like, if you go back 15 years, when I was joining Twitter as an engineer, we were talking a lot about big data and what kind of databases are you using to do this, that, and the other thing, and now you don't talk about that. It's, it's de rigueur, it's expected, and a lot of database companies have made a lot of money in the meantime. They're there in the background, powering everything, but you don't treat it the same way you used to. I would love to see a world, and I think we are moving to a world, where this kind of data that we offer becomes part of solving these problems. You just say: Of course, we're using satellite data. That's exactly the world we're driving towards. It's one of the reasons that the Sinergise acquisition is so strategic for us, because it does make it easier for companies to bring different data sets together and run different analyses on them. We may just be one component, like, I think that's a great thing. We don't solve every single problem with our satellites, but our satellite data is a key component of so many problems, like you're seeing. So I love that future world. It's absolutely where this is going. And you had a question around, like: Do we need to become an AI company? The way we think about it is, again, we're not gonna solve every problem, but where we see opportunities, where we can help customers and where we can kind of bring the industry forward, we're gonna do that, and we're gonna leverage partners in every place we possibly can, because there are a whole lot of smart people outside these doors, and we wanna take advantage of that as well to bring the future forward. Yeah, I think that was really well said. Next question? ... We have a question online from Edison Yu of Deutsche Bank. Edison, can you please unmute your mic and ask your question? Hey, thank you for the question. First, on the financial side, I realize you're probably not giving guidance for 2025, but could you maybe directionally give us some type of bridge in terms of what are the main factors? Obviously, you had some headwinds this year, the [audio distortion] goes to go away. Any type of even qualitative bridge to see how 2025 might look a lot better than 2024? Yes. So I provided a framework in my prepared remarks. The way we look at it is, first of all, if we sustained 115% NDRR, and the pace of new business mirrored what we did this year, the path to 20% growth is very, very clear from that. Because we don't have the headwind that we had coming into this year, of the very large contract that came to an end. The levers to obviously drive that better, and, and we certainly have aspirations to do so, are to take that net dollar retention rate higher by expanding with our customers, and, and we've got very large expansion opportunities in the pipeline that we're actively working. There are opportunities also to see that sales motion around solutions enable us to expand within current markets, where we've got some great lighthouse customers, some of which you heard from today, that should enable us to go account-based marketing, reach out to those customers, and sell those solutions in a much more compressed timeline. So it's about obviously continuing to accelerate close rates. It's about bringing those close rates earlier in the year to drive more revenue in the year, and it's about taking that Net Dollar Retention Rate up that can see 20% growth go higher, and that's obviously where we're aiming internally. Great. Fundamentally, this should be a high-growth business, and that's where we're aiming. And I wanted to come back on- Yeah. -EUDR. Yeah. Is there a way to sort of translate that into an opportunity for Planet? I realize those are very big numbers, but how does that sort of manifest, you know, once it flows through? Is it, you know, one big contract? Is it- Mm. Many, many small contracts? What, what, what would be a range of configuration that could be? Is there any way we can maybe translate that more? Yeah. Okay. I mean, I think it's not one opportunity, it's a million. But, I mean, maybe, Kevin, you can comment on how would we go after this opportunity from a go-to-market standpoint. Well, I mean, as the regulation comes together, it's gonna be something that every company that—not just companies based in Europe, but every company that does business in Europe—is going to need to satisfy. Yeah. You know, we'll, like I said today, we're gonna start not by going after a million things at once, but start with the bigger opportunities around our core verticals, companies that we know, that we have relationships with, where we can look to close larger deals, and then we'll expand out from there. Other questions? Oh, were you done? Was there something else you wanted to say? I just wanted to say, is this something that's, you know, tens of millions, hundreds of millions? Is there any rough kind of ballpark? Well, Andrew talked about the $5 billion-ish cost for compliance with this. I mean, we don't know exactly the fraction that would be satellite data, but I mean, there's not many ways to do this. I mean, there's certainly it has to involve satellite data, so I would imagine it would be a meaningful proportion of that, but I don't know if we've got any estimates of that. Yeah, just to sort of give you a sense of the state of play, this is a really significant additional new thing that all of these actors are thinking about. There's gonna be real competition in the compliance space. Some of that competition is gonna come from specialty firms that want to establish, you know, solutions that either deliver the certifications for compliance on their own. Some of them are gonna come from big systems integrators that actually build those big governmental systems. Some of it's gonna come from companies themselves, especially the large players. So right now, between the standards makers, the market actors, the infrastructure providers, the whole market is now figuring out where that's gonna live. We're having conversations, as you might expect, with all of that space all the time. Yeah. ... because it's still not clear, but we do know that, you know, everyone's gonna have to have a solution in the next 18 months, and so there's a real driver. And if I can add to that, I mean, I think our data can underpin- All of it. A.ll aspects of that. 100%. And, and- Yeah. And that's the role that we aim to play, just like we're talking about the carbon markets. There's many folks trying to put together carbon market solutions. That's right. ... but the idea is that we have a standard data set that underpins all of them. Right. And it's worth noting, we're using EUDR here, which is, you know, crisp, and the guidelines, sorry, the regulations actually make explicit reference to geospatial data. But the Common Agricultural Policy is also enormous. The mandated disclosure for climate regul... You know, mandated climate and sustainability requirements, which are European today, and I'm obviously can't predict with certainty, but the likelihood of knock-on effects of those being carried to other markets, in the U.S. and other places, is not zero. So it's There are all these additional spaces, and each one of them represents a huge potential market- Right ... for Planet's data. Thanks a lot. Yes, at the front here. ... You talked a lot about the importance and opportunity in hyperspectral with Tanager. Yeah. Can you discuss the deltas in the business model for Planet as it relates to—I know that there's a JV involved, and you have access to the same data. Can you kind of walk us through at a high level if there are any differences in the business model? Thanks. Yeah, and maybe, Ashley, you can talk a little bit about this. I mean, basically, through this partnership, we will be publishing some of the data as a public good. But the underlying data, and faster data, we will be able to sell, as part of this partnership. It's really a pretty good public-private partnership that enables the funding of this to be done up front as we explore the market. But, you know, we've talked about selling to the companies that are trying to comply, as well as the regulators that are trying to enforce the regulations as we go forward. Methane has been identified as one of the major sources of driving climate change and a major forcing. We don't know exactly where the sources are. So that's one reason. But we've spoken about the fact that there's agriculture, defense, and intelligence. Biodiversity is another use case that I think is really interesting, that we're gonna... Just like carbon, we're gonna need to get our hands around biodiversity, where it's at, and companies are already starting to get their Nature Zero solution, you know, Nature Zero goals, just like they're getting their carbon neutral goals. And that's gonna require more detailed understanding of biodiversity around the planet. Where is the nature? How well is it doing over time? So in time, I hope, like the carbon thing, we'll be doing that sort of for biodiversity. I think there's huge opportunities there, but it's extremely early. It's why we have this early access program. It's why we have early contact like that with NRO. But, you know, just to be clear, we are creating another market here. You know, Kevin was just talking about how, in general, we are market making, and that's the time limit, in a sense. But this is an even more new system. We see massive potential there, but it's gonna take time. Ashley, anything else you want to add to this sort of structure? No, I mean, I would just highlight that this is an example of where, you know, doing well and doing good actually worked really well together economically. We had a partner who really wanted to see this capability come to light after we'd done a study and shown that this, you know, massively powerful sensor that NASA JPL had built could be put in a satellite and enable the data to be produced more scalably than in airplane flyovers. So once it was determined that that was of interest, but because we still had this uncertainty around the market, there was funding that was given to us and to NASA JPL, over $40 million to Planet, that shows as a contra R&D expense on our financials, that essentially incentivized us to do this program and to see this capability come to fruition while we also assess how big is this market. So it was great to hear somebody from the defense and intelligence community talk about how excited they are around this, as well as somebody from civil government is thinking about that from managing, you know, broad-scale. Yeah. ... climate challenges that they're having, in addition to the really source of the funding was around understanding point source methane gas emissions. Yeah. So these are really positive signals, but as I said, as we think about CapEx in the next phase, we'll get through effectively what's being funded, and then as the market evolves, we can launch more to produce more data if we see that pull from the market. So again, it's a kudos to the team that's on our first floor that enables us to be agile in how we think about CapEx. But also a real credit to the teams that established these partnerships. Actually, Robbie was a key person in the partnership with Carbon Mapper, to really think about how can we make this work for what they wanted to do from a pub... a policy, public policy perspective, and what we need to do from a, you know, being a for-profit business. Yeah. So, and hopefully, we can see more of these types of capabilities. Yeah. There's somebody else? Yeah. Chris has his hand up. Oh, Chris? Yeah, go. No, we just have time for one more. Great. Oh, you don't have one more. We have time. Okay, any other questions on the floor? Yeah. Kevin, you mentioned with the Sinergise acquisition and the e-commerce kind of portion of the platform you're building, and I think you threw out a number of $50,000 as the kind of sub that. Is that just sort of a finger in the wind of, like, baseline, where you think anything that or below will kind of come in there, or... 'Cause that seems pretty high to me. It's not that far off from kind of what you're calling a larger customer. So how just kind of maybe walk us through that number and just where, from a budgetary perspective, customers would designate a spend of that kind of size without with just via credit card, or how that kind of how you see that playing out. Yeah. So if you go and check out the website today, there's a $50,000 package. There are also a handful of packages at lower price points, going down fairly low. So there will be a range of, you know, interests, and we wanna kind of map to those. Just to say, though, the self-serve option for Sinergise, it's great. I'm really happy that there are easy ways for somebody to come off the street and, you know, grab some geospatial data and, you know, solve a problem. It's not our focus, though. We're not putting a huge amount behind, you know, doing a lot of marketing to drive self-serve business today. It's really an outlet for all of the commercial deals that I was pointing to in the pipeline that are on the smaller side. We want to be able to funnel them there, so they can get up and running without being, you know, sort of an opportunity cost for our sales team. So today, a lot of that work is so that our sales team can focus on the bigger deals in our core markets. So with that, we'll wrap it up. Thanks very, very much, everyone, for joining us here. Thanks for the Q&A, and thanks for the sessions. We will now turn it to a lab tour for those that are here in person. There's a quick lunch first, and then we'll go on the lab tour. I highly recommend it if you have time. And thanks for all of our virtual viewers joining us online today. I hope this was useful. Once again, we're really excited. We feel very honed on the opportunity, and how to execute against the opportunity that we see in front of us. And we definitely see AI as an accelerant, which is exciting for us, and thanks to you all for joining. Thank you.