Hi everyone and welcome to the Jeffrey's Global Healthcare Conference. I'm Kellen Sanger, Sophia's Head of Strategy, and I'm joined here today by George Cardosa, our CFO, and we're excited to give an update on the company and share some recent news, including the press release that we put out this morning. So just quickly on the company, we were founded in 2011, growing up out of Switzerland. We currently have headquarters in Boston and a little bit outside of Geneva, and we're about 400 people globally and since the beginning we've had a deep focus on biology, AI and data science. Now as many of you know there's been broadly a proliferation of healthcare data being produced across the space, across different data modalities but also within and across institutions and our vision from day one has been to capture this data, apply artificial intelligence in order to help clinicians better diagnose and treat patients. And I think something really special about this page here is we recently were looking through old company documents. And this is one of the first visuals that we used to raise money back in 2011. And we've been talking about this same exact slide. It looks a little bit better now with better formatting, but it's been the same vision since the company was founded 15 years ago. And so before people recognized the benefits of artificial intelligence and machine learning, we've been pioneering this in our space specifically for precision medicine and I'll talk a little bit about what we've built and how we've been doing that. So in order to approach the problem, we have developed an AI platform for precision medicine acting as the intelligence layer to compute the world's healthcare data. We've been always focused on building in the cloud so that all people across the globe can adopt this. We've always been tech agnostic, so compatible with any types of technologies in terms of data production. And the platform was designed to compute data with AI embedded at almost every stage. So, you know, going from the bottom, just really robust and advanced data compute and manipulation and management functions. We have AI factories, which consists of highly proprietary algorithms and AI agents that help manipulate and improve and harmonize the data that we compute with applications specifically across oncology and rare disorders to help patients across a broad range of cancer types and also disease states. And then all of this wrapped together in what is kind of a beautiful platform interface that helps the clinician quickly diagnose and treat the patients that they're looking at. And the big kind of call out here is since inception, specifically in genomics where we started, We've analyzed over 2.5 million patients since we started. And I'll talk a little bit about how we did this. And in order to do so, I'm just going to zoom in briefly about how Sofita DDM is used every day, which is the platform that we've built. And if you think about the typical genomic workflow, a patient will typically go to one of our customers, which is the university hospitals or academic medical centers. A sample will be collected. There will be certain steps to enrich and enhance that sample with library prep and other sort of probes, et cetera. It'll be sequenced by a next-gen sequencer. And then what we do is we take the raw genomic information straight off the sequencer, upload that into our platform, apply our proprietary AI and machine learning techniques in order to provide insights, which help the clinician, increasingly pathologists and oncologists, diagnose and treat the patient. So you can see the business model is quite straightforward. Last year, we did 400,000 analysis, making us one of the largest players in this space in all of precision medicine in terms of the number of genomic patients which we tested. We charged between $100 and $500 per patient, and that just right there is 95 or 90-plus percent of our revenue. So the business model is very straightforward. Just to kind of emphasize the piece on our technology agnostic approach, working with all different sequencer companies, we have partnerships with Element, with Ultima, with MGI, we're compatible with all Illumina machines, and this has really been a core part of not only serving our customers, but also generating very diverse data from large amounts of geographies from across the globe, from an ethnic perspective, but also from a data production perspective, because as institutions are producing data, it looks very different based on the technology that was used to generate that, and so this has been core to our thesis and enabling us to produce high-quality algorithms on top of that data. In terms of a menu, so I'll focus on the check marks that you see here on the page across oncology and rare disorders. We focus on hereditary cancer, hemox, solid tumor, liquid biopsy, and rare disorder applications. I think broadly in this space, what's happening is obviously data is becoming more and more useful to treat patients and the clinical utility is being proven. And tests are becoming more sophisticated in turn as new targeted therapies are being discovered. And so we sit right here and we're always kind of focused on serving our customers and performing the analysis that offer the highest amount of utility to them. Now where the vision and the mission gets very interesting is if you think about us computing genomic data and increasingly multimodal data across all the patients in our network In doing so, we're streaming a huge amount of incredible real-world, real-time data from over 1,000 institutions in 75 different countries, live and in real time. And so at any given moment, I know in March, we analyze over 40,000 genomic patients on our platform. Not only is that data being shared and streamed through our network and our algorithms and AI are learning from the data that's being shared, but also the intelligence from our users who are using the platform to treat patients is being contributed as well. So we currently have customers that include 15 of the top 20 global cancer centers, including folks like MD Anderson and Mayo Clinic. We work with MSK, Gustav Rossi, Jameli, a lot of the kind of premier institutions. And as they're looking at a patient case, they're thinking about how would I treat this patient? What do I need to know from this patient? And they're inputting their intelligence or their knowledge into the platform. And so if you were to think about what this means for each one of our users as, you know, say you're using the platform to identify or analyze a patient anywhere in the world, anywhere from, you know, South Asia to India to Latin America to certain parts of the U.S. where a patient might not have access to the best in class care, through our platform, the clinician is able to access the knowledge and collective intelligence from everyone who is using the platform globally. So you can see how would this patient who has this specific biomarker or genetic mutation with these clinical attributes and this maybe imaging data, how would a similar patient across all of the 2.5 million in our network react and respond to certain type of therapies? And how can I optimize the survival of that patient? And so these are the types of intelligence and decision support that we're providing to our customers, which, as you can tell, is not only gets smarter, but is increasingly valuable. And so this is what we call the network of flexor, the flywheel that we're enabling broadly across the globe. And so just thinking about why customers choose, Sophia, and a bit about our value prof, if you're thinking about the different kind of players in the space and why Simone would choose to in-house testing, the analytical performance for us is absolutely table stakes, and this is where we excel. We're really like the Ferrari of data analytics in what we do. We have obviously invested significant amount of effort and focus into developing AI and machine learning algorithms, which take any information from across the globe and any patient to identify highly accurate insights. But this is really where we kind of blow away customers. In addition, we're faster in terms of turnaround time, getting that result back to the patient and the treating oncologist faster. If you think, you know, we had a patient in Abu Dhabi who is sending a liquid biopsy sample all the way to San Francisco to be analyzed, right, by another provider. By bringing that testing in-house, you're able to move the turnaround times from weeks to hours in some cases. In addition, the ability to save cost. Instead of bringing in an entire tech platform, coding your own, you know, bringing in a server, coding all of the work that we've put in, we're able to offer an enterprise solution there, and in addition to unlocking revenue in some cases, if someone's standing up testing for the first time or moving volumes into their institution as opposed to sending them to someone else, they're getting reimbursed, and they're realizing the profit per patient as opposed to someone else outside of their system. And then I think increasingly and the most importantly is the retaining control of your data. I think about most hospitals and other academic medical center strategy around precision medicine in is to obviously keep the data and use that data for discoveries or also to partner with BioPharm to create new diseases. And so within our platform, or create new cures for diseases. And so within our platform, we enable users to quickly and easily manipulate and play and experiment with that data so that they can make discoveries of their own. Now, if we look how this is evolving, I'm about to turn it over to George to talk more about the financials and the growth story here. But you can see across those application areas, which I mentioned, which, again, are becoming more and more sophisticated over time, which benefits us as the cost of sequencing comes down and new things are discovered for clinical utility, the growth has been impressive and recurring. And George is going to talk more about that from a volume and from a data perspective.

Yeah. I mean, here you see we talk about 2.5 million analysis and you see the remarkable growth the company's had. But what's also on the right side what's also significant too is you know the if you went back to the NGS market years ago you know people were doing 30 gene panels and 50 gene panels then they went to 300 and 500 and today our enhanced exome product is 6,800 genes so there's been a real challenge in terms of the hospitals if you would trying to keep up with that data explosion so again larger and larger panels more sequencing happen more happening so Sophia has a great advantage really as a tech company because our process through our Uniflow is extremely efficient in terms of processing data and we can do it at much lower cost than what a hospital or a reference lab could. And that really is a significant competitive advantage that we have. So if you look at the financials, clearly you see the success of the company. You know, our analysis volume has continued to trend up. We actually announced in our press release, our March volume actually topped $40,000 a month for the first time. So you can see how many patients we're touching through this global network. We're actually operating in 75 different countries right now. So it's really, I don't think you're going to meet a more global company than us. We added our first clients in Vietnam. So, you know, Jurgi's vision was, you know, to democratize, you know, precision medicine around the world. And the company really is living that vision and that dream. Again, if you look at revenue growth, you know, 2024, our revenue growth did slow a bit. Our pharma business has slowed down a bit. You've seen our farmer business recover a bit in 2025, and it came up, and now we're looking, we guided to 92 to 94 million in 2026, and we're certainly looking to beat that. Again, we talk about accelerating revenue growth, and this is important too because, again, we've been building sort of quarter by quarter. We've continued to put up good results. Our first quarter growth was 22%, and certainly our expectations are to continue to drive that number up. We're aiming for 25% to 30% growth. We believe the market opportunity is there, and this is a very driven company. So what is the growth algorithm of Sophia? So if you look at, you know, I mentioned biopharma, and I'll start there certainly. This is a very interesting space. We've actually signed a couple of new large deals with AstraZeneca. We actually signed one of the largest deals in the history of the company. And I think biopharma increasingly is turning to us, and we had a full plate at ASCO in terms of meetings. One of the reasons they want to work with us, though, is the 75 countries, is the data set. A lot of the competitive labs in the United States, they give you good data on the United States. They don't even give you Canada. You know, we literally have a network that is truly global, including Canada. So we can give you a lot of information, a lot of data, and really drive things that other laboratories can't bring to the table. So our pharma plate has been very full. Our team is doing very well. And we talked about in 2025, our pharma was a drag on our growth. It's been an accelerator in 2026, and we think it's going to be a hyper-accelerator in 2027 and 2028. We talk about our Land and Expand program. Land is your classic new logo, so we are always looking to bring new hospitals on and sign new hospitals. We've been very successful, added on 124 new clients, so again, people are seeing the value of the platform and coming on board. Expand is the other important part of that. We always want to get into a hospital. You may get in for solid tumor. then you want to migrate over and okay who's doing your heme who's doing your hereditary who's doing liquid biopsy so expand is an important part of this as well and we have a significant opportunity there with all the clients we even brought in last year you know you always want to prove yourself over six to twelve months but once you prove yourself and they like your platform then you want to make sure that you're selling the second and the third application and just organic i mean the the ngs market is growing i mean you've seen some of this desi bio who are very smart people forecasted 7% CAGR growth from 2025 to 2026. Thank you, Steph. And honestly, more sequencers keep coming onto the market. That's very important as well. So you're seeing this growth in sequencing, and Roche has announced that they're coming in, so come on into the pool, more sequencer, more boxes. What does that all mean? More NGS data that's going to come off that needs to be analyzed and needs to be processed. And Sophia's really in a great spot there, just in terms of the overall market growth. And we also talk about our recurring growth. One of the things I think that we're really proud of in this company is when people adopt the SOFIA platform, they stay with it. The pathologists, the people actually see the value in the platform on making difficult calls on difficult cases, the ease of use, the cost of it being fairly competitive. And you see our churn rate last year was less than 1%. That really, in any business, that's really world class in terms of when people are on this platform, they don't want to leave it. So I think that's a real tribute to us, and it's helped drive our growth, because when we bring customers in the front door, we're not losing any out the back door, and that's a really important part of this. And again, you just see that our current customers, we're rather proud, seven of the top ten cancer institutes globally are using this platform. So, I mean, people see the results that we achieve on the really challenging cases, and that's why these top, top academic leaders in the field want to use the SOFIA DDM platform. We talk about our land and expand engine, and I also want to talk about kind of why this company has been so successful. And I'll kind of use 2017 just as an example here. What started with a group of customers that in the first year were doing 15,000 analysis, and you would expect, you know, the next year that number would go up because you have the full year benefit. So it did. It went up to 31,000. But you've seen over the years that customers have stayed with the platform. They've added other applications on. And what started as 15,000 analysis now has gone up 5x to 77,000 analysis. And we have a couple of wonky years in there for COVID. But really, if you look at this model, we've been able to bring customers on. We've been able to keep them, retain them, and then see an uptick in their work. And that really is why this company has been able to achieve the growth it's achieved and get up to 2.5 million analysis. So I'll talk about our growth drivers for 2026. And this is important in terms of our top areas. This company started as a spinoff out of the EPFL in Lausanne, so really it tackled Europe, as you would expect a startup to do. It attacked the European market first and built a very strong footprint in Europe. It's come to the United States a little bit later, but the U.S. market now really is firing on all cylinders. We saw fantastic bookings last year in the United States. We've seen some big names added, and certainly one of our key focuses in 2026 is to continue to grow the United States market. We believe it should be as big as Europe. Today it's quite smaller than Europe, but we believe this is a huge growth potential for us. Liquid biopsy, Kellen gave the example of what we saw in Abu Dhabi and clients praising the MSK access test. This is a liquid biopsy test. And again, in the United States, liquid biopsy testing is there's a fairly good market penetration in Europe that it's not near where it is in the United States. So the idea that you can go over to a hospital in France or in Abu Dhabi or in Australia and say, we can give you, by working in partnership with SOFIA, we can help you bring up this MSK access test. It's a world-class liquid biopsy test developed right here in Manhattan. And you will be able to do liquid biopsy testing in your building, in your institution, for your network. That's really compelling, and it's opened a lot of doors for us. And I think people are recognizing the value of, again, doing their own liquid biopsy testing, both for faster patient care. Again, a lot of these countries, our team was recently in Japan and they told them how unhappy they were with specimens having to fly to California. That really wasn't something that they wanted and how focused they were on being able to do these samples and test them in Japan. So you can see where the decentralized model really is going to fit globally on the liquid biopsy front. And I mentioned biopharma. Again, we have a tremendous network. We have tremendous data and continue to build this, and biopharma now increasingly is coming to us. We've done a lot of projects we've announced with AstraZeneca, certainly one of the leaders in the oncology space, and I think that's actually motivated a lot of other pharmas to say if AstraZeneca is working with these guys, then they're probably worth giving a call to. And we did a press release today that came out at 8 a.m. We want to talk a little bit about. It's our strategic collaboration with our friends at the MS Memorial Sloan Kettering Cancer Center. So this again, MSK obviously in New York City here, everybody knows them, one of the leading cancer institutions, if not the leading cancer institution in the world, sees over 100,000 patients annually, a phenomenal data set in terms of what they have genomically. They were really looking for a partner, and Sophia Genetics, in terms of if you want a partner to help you build a decentralized lab and help you build a hub, Sophia Genetics really, I think, is the right partner to work with, certainly. And I think we just announced this memo of understanding to really work together on bringing new innovations to the market, being able to service the patients in this geography with world-class testing, and to continue to develop. So, again, it's exciting for us in terms of what we believe we can bring to the table. And, you know, they wanted a partner that was deep in terms of AI and being able to analyze this data. So, again, when you look at the tremendous capabilities that SOFIA has in its network, it's really very flattering that they picked us to be their partner. So, again, we're going to continue to work with them. You know, what is this going to be? It's going to be an incubation center. So, again, we've rolled out globally the access and the impact test. Certainly you're going to see other, you know, other tests developed by the two partners. Again, we've got a great R&D team. They've got a great R&D team. So, again, working together, the goal is to continue to develop new tests, new and exciting tests for the market. And SOFIA is going to have the ability to have an infrastructure basically to work with. And that's important, too, in terms of evidence generation, in terms of working with pharma, to actually have somewhere where we can do the testing, work in collaboration with MSK, and launch this innovation hub. So very, very excited about this, expanding our partnership with MSK. We've had a phenomenal relationship with MSK, and I think it just continues to grow. And this is an example, again, of us working even more deeply together to continue to build a laboratory and to continue to work. And again, even at Memorial Sloan Kettering, you know, there potentially is work that's going, you know, outside of network. And they want to be able to build and fight for that work and be able to truly be a global center of excellence where the work is coming into them. So I think a lot of hospitals, you know, look at work and they see it go to the reference labs, and that's why they want to bring up tests like MSK Access. That's why they're excited about working with SOFIA in terms of a decentralized model so that they can start doing more of their NGS testing. And that's historically what the model's been in medicine, even in the United States, is, you know, after a few years go by, the testing becomes a little bit more standard and it moves into these institutions. And I think you're going to continue to see that on the NGS front. So we're certainly very excited on that front. And in terms of Sophia's, you know, our overall structure, you know, again, we've done over 2,000 publications. We're actually up to over 1,000 customers right now. You know, and again, the AI, it's funny, you go to J.P. Morgan and some of these conferences, and people are talking about using AI, and it is remarkable when you see some of Jurgi's early presentations and how visionary he was to really see AI and the cloud. I mean, he was doing things that were very scary to people a decade ago, and now we just accept it as reality that his vision came through. But again, building this collective intelligence, building this network where everybody who contributes to it actually benefits from it. And you can really see the flywheel effect now, having done 2.5 million genomic analysis. That really is tremendous and why that's created what it's created here. The company itself continues to grow. I want to highlight, too, our 75% gross margin. Again, we're a software platform, so we have a very scalable business model. And as we continue to grow, that scalability comes through. And we are working. We guided this year to $92 million to $94 million, continue to grow 20% north. And we've said that we are going to continue to move toward being profitable, and we expect to be profitable by the end of 2027. So the company is a remarkable company. I think if you ever have the chance to come to Lausanne, you would see the passion people have to bring the vision. We always kind of view ourselves as the people really trying to democratize precision medicine. And being able to have a partner somewhere like Vietnam or Chile or places where maybe getting molecular scientists could be a challenge, we are the partner to help these people stand up this testing and help them treat their patients and save lives. So there's a tremendous amount of pride inside the company for who we are, what we do. and the belief is there that the decentralized model is the right model. I think pharma is even seeing the light increasingly why they want to work with us because they see the benefits of going to this decentralized model and we believe there's an incredibly bright future at SOFIA. So with that, you don't need to fully go through the...

Maybe if there's any questions from the audience.

Yes.

I was wondering if you could comment on pricing pressure in a space, like the complexity of the analysis that you conduct continues to go up. Does that mean that this offset the pricing pressure that you see in terms of price per gene, or can you comment on this?

Yeah, I think if you look at a test like MSK Access, I think the clients recognize that they could try for three years to bring up a liquid biopsy test, and they might not even be successful at the end of that. So they see the value that we're bringing to the table, when we help them bring up a liquid biopsy test. So that test is like over 2x what are our average prices. So again, I think our job is to bring these high-value-added tests that people see, okay, this is really bringing significant value to the table, and people are willing to pay a premium for that. Now, the reimbursement is very good for liquid biopsy, obviously, and they're going to get $2,000, $3,000 for that. So for them to pay us 10% to 15% out of that still leaves very good economics on their side.

Yeah, but also more broadly, as sequencing costs continue to decline and there's more competitors in the space and more diversity from sequencers, this is really good for Sophia, right? Because it opens up more room for where the value is moving across the geomic workflow, which is to the data compute and analysis. And I think, as we mentioned earlier, as tests become more sophisticated and new things are discovered, like, you know, we had liquid biopsy, but now MRD and methylation and transcriptomics in some of the new areas, I think there's reason to believe that the value will continue moving to the data compute, where obviously we're well positioned to work across all those sequencers and for increasingly complex indication, to answer your question, carry a higher ASP, especially on the data compute.

And you don't find that this increased complexity scares people to actually bring that in-house, that they're actually okay with that?

I think we always believe that there will be a world in which some tests will be run in central models or in some institutions, right, especially for more sophisticated tests. But over time, those will become more established in terms of the clinical utility, and then you'll see them move to a more decentralized model. So we're looking forward to perpetuating that wave of innovation.

I mean, the complexity, in a way, plays in our favor, too, because would you rather climb the mountain by yourself or with a Sherpa that's done it 50 times? So let us be the Sherpa. Let us help you. And honestly, I think through that complexity, they love us as truly their partner in standing up NGS or bringing up a new application.

If my math is right, so 391,000 tests and $77 million last year comes to about $197 or something like that, a test. Can you talk to kind of that ASP and where that came from, where that's going, what kind of control you have over that?

Yeah, I mean, it's a similar answer to before, right? As testing becomes more sophisticated, as new therapies are discovered, which require more complex and sophisticated precision medicine applications, we will continue following the industry in terms of innovation. and with sophisticated testing and more complex biomarkers but also insights to be generated off those tests, they typically carry a higher ASP. In addition, the data compute is increasing. You saw the nice slide in our presentation. We're moving into petabytes of data computed instead of terabytes. This is something that I think a lot of the frontier AI companies have done a great job of educating the market on, on the difficulty to compute large amounts of data, right? Data centers are in all the headlines right now and as a company that has been you know solely focused on optimizing data compute with partners like nvidia and microsoft in order to improve how we analyze genomic information and doing so in an efficient way you know we're perfectly positioned to to capture the additional data and if anything we benefit uh in every company and every year since the the company was founded we've had asp appreciation so we continue to hopefully see this trend and more value moving to the analytics piece.

Hi there. Thank you very much for the presentation. I have a quick question. I mean, earlier in the presentation, you mentioned that you partnered with some of the companies that are in the sequencing space. Yes. Would you say you're generally agnostic in terms of the sequencing, the data that you collect, or do you only work with these four?

We've press-released our partnerships with Element and with Ultima. Obviously, Illumina has the biggest footprint, so naturally we work with Illumina. MGI, Complete Genomics in the United States, has very good machines, and we work with them. Roche is coming into the pool, and I think as soon as our data people and bioinformaticians see their output, the intention is to work with Roche as well. So we're very happy with more sequencing, and yes, our goal is to be agnostic. It's important, too, that because you're seeing certain sequences can work better on certain applications. You know, like Ultima perhaps on MRD or Element for certain applications and Illumina for others. The great part of the Sophia platform is you can unite all those on the back end. So your people on the back end aren't jumping from, okay, I use Illumina for this, then Element I got to jump over and use a different product. And then, okay, now Ultima's got another one. You actually can unite all that on the back end with Sophia. So we're actually very, you know, that's one of the reasons why it's important for us to be agnostic and to work with everybody.

And many of our customers do have that set up where they have an aluminum machine, a complete machine, they have an element machine, and this will only increase. And so we're able to consolidate all those technologies into one interface for the clinician and to be able to analyze the patient. Great. Thank you very much.