Thanks, Steve. Morning, everyone, and thank you all for joining us. A year ago, we told you that we would deploy 2,000 autonomous robots across the country by the end of 2025, that we would expand from a single city to a truly national footprint, and would prove that this technology works, not just in a lab or a closed campus, but on open sidewalks in dense cities, navigating the full complex of urban life. We did all of that and then some. Today, a fleet of 2,000 Serve Robotics Inc. robots have been activated across 20 distinct cities in six major metropolitan areas, from Los Angeles all the way to the Washington, D.C., corridor. We launched Atlanta, Dallas, Chicago, and Miami. We expanded aggressively in every existing market. We also added DoorDash alongside Uber Eats. This gives us access to over 80% of the U.S. food delivery market. We also completed four strategic acquisitions since early 2025, met or exceeded our revenue guidance every single quarter, and through all of it, we maintained a 99.8% delivery completion rate and a proud safety record. So let me say it again: 20 times the fleet, national scale, four acquisitions, and near-perfect reliability. And in Q4, we once again delivered revenue above guidance as we drove 400% year-over-year growth in the quarter. This is not incremental progress. This is a company that is defining a category in real time. But before I get into the quarter, let us look at the broader trends. We are living through one of the most consequential technology transitions of our lifetime. For the past few years, the world has marveled at what AI can do with words and images and code. The next frontier—the one that will reshape our physical world—is physical AI, machines that can see and think and act in real environments alongside people. As we try to anticipate what this future looks like, I find it really helpful to think about the evolution of computing so far. First, it was the personal computer. Then came the Internet. It connected information. It connected people. Next, we put computing and connectivity in every pocket and in every device. We connected the physical things too. As a result of all this, all of commerce and every industry is now digital and connected. Each leap along this path was worth trillions. Fast forward to today, AI has taken over our digital lives over the past few years, arguably becoming the fastest rising rung of this evolution of computing. Physical AI is the natural next phase that is right around the corner. It is the moment when this intelligence leaves the digital realm and enters the streets. And like computing and the Internet before it, the companies that build the platforms for physical AI will define this era. NVIDIA CEO Jensen Huang has called robotics and physical AI the next multitrillion-dollar industry. Every major AI company is racing to build models for the physical world. The investment thesis is pretty clear. The companies that build the platform and own the data will capture the value. And here is what is important. You cannot build physical AI from a research lab. You need robots in the real world gathering real data, encountering real edge cases, and at real scale. That is the flywheel, and it is exactly what Serve Robotics Inc. has built. Every transformative technology goes through the same arc. We are at a very familiar inflection point. Autonomous robots are here to fundamentally shift how we leverage technology in our lives. The question is no longer will this work, as we have seen by our progress last year. Now the question is, how fast can you scale? 2025 was the year we proved the technology. Looking ahead, 2026 is the year we compound the business model. Last quarter, I said that beyond 1,000 robots, the system tips. Scale changes everything. The economics improve. The partners lean in. Learning accelerates. At 2,000 robots, the system does not just tip. It compounds. We are now accelerating the flywheel. We discussed this concept last quarter when we described how more miles lead to more data, better models, and a more capable fleet. This is the flywheel that should be at the core of any physical AI company, and we have really organized our strategy around it. Every investment we make—every acquisition or deployment or partnership—they are all designed to strengthen a specific step of that flywheel and, as a result, make the whole system spin faster. So let me walk you through it: the four steps in the Serve Robotics Inc. flywheel. Step one is amassing data. Physical AI runs on large amounts of data. This is not just some data you scrape off the Internet. This is data collected in real environments. It is collected by robots at scale. Every mile our robots travel enriches our dataset. Every edge case, every construction zone or rush hour or unmarked crosswalk, they all sharpen our models. And this data is proprietary. You cannot just download it on the Internet or simulate it with the same depth and richness. You have to live on the sidewalks. And no one is better positioned for it than Serve Robotics Inc. What is new and exciting is that we are no longer just collecting data from a single environment. Today, our data spans multiple and distinct physical domains. On sidewalks, thousands of robots are mapping the world in 20 unique cities across the country. Every new neighborhood brings new edge cases and new pedestrian and traffic dynamics, new weather patterns. All of that enriches the models network-wide. In hospitals, our recent acquisition Diligent Robotics has a fleet of nearly 100 robots called Moxie, and they are navigating some of the most challenging indoor environments in robotics. These are multilevel facilities with tight corridors, constant foot traffic, high-pressure operations. Moxie robots have completed over 1,000,000 deliveries across more than 25 hospitals, and counting. So, sidewalks and hospitals and beyond—multiple domains, with wide-ranging geographies, all feeding a single robotics and autonomy platform. There is no one who is doing all this and realizing the value of the combination of indoor and outdoor data collection from commercial-scale fleets. The second step of the Serve Robotics Inc. flywheel is the models. Data is a raw material, but step two is where we take everything our robots are seeing and experiencing, and we turn it into better AI models. This is where another recent acquisition comes into focus. VYU Robotics brought us a specialized team that builds end-to-end models for physical AI. We are building systems that empower us to train across all our operating domains, indoors and outdoors, so that what a robot learns in Los Angeles would help a robot in Dallas, or what a Moxie robot learns navigating a hospital corridor could improve a Serve Robotics Inc. robot that is navigating an obstructed city sidewalk. That kind of cross-domain learning is really significant, and it is a compounding advantage that will widen every quarter. Also, our acquisition of Phantom Auto brought us one of the most capable robot connectivity stacks with extremely low latency. This enables us to operate at a large scale and across a significant geographic region because we can reliably assist robots remotely in real time. What is underappreciated here is that every time a remote supervisor assists a robot anywhere in the country, we generate high-quality training data. Our operations, which are empowered by this connectivity stack we acquired, are a conduit to collecting more data and more edge cases, and it is paired with a considerable training dataset, all collected at a faster rate than ever, feeding right back into our models. I should also mention the talented team of engineers that make all of this possible. When you have one of the largest autonomous robot fleets, plus data from multiple physical domains, and the infrastructure to turn all of this into deployed AI, that is where the best people want to work. Retention across our team has been really strong because people love building on real robots in the real world with significant, unique data. The flywheel attracts talent, and talent accelerates the flywheel. The third step of the Serve Robotics Inc. flywheel—after you gather the data and develop the models—is to deploy those models into the real world. Better models only matter if you can actually get them onto live robots. That is pushing all that improved autonomy out to the fleet that is in the real world where the edge cases live. This is where our fleet scale and our partnerships become a strategic asset. Uber Eats and DoorDash combined serve over 80% of the U.S. food delivery market. We are now a multiplatform fleet. We see robots finishing a DoorDash delivery, then picking up an Uber Eats order on the way back. That kind of interoperability drives utilization, and, of course, utilization is the key to both our economics and our data collection. Our merchant network has expanded to over 4,500 available restaurants and retail partners today. Just this morning, we announced a new partnership with White Castle, one of America’s most iconic restaurant brands. And our geographic pipeline also continues to develop. We are in active discussions with city officials across the country, from New York to Boston to San Jose—and even internationally, Vancouver and Toronto and Sydney and Melbourne. As we evaluate all this new wave of market launches, each market will represent a natural extension to our existing footprint, and we are really excited to share more about our plans throughout 2026 as these initiatives progress. And this is the critical point. Every deployment, across every domain, into every new city, generates new, unique data that feeds directly back into step one. And the cycle continues. Finally, the fourth step of the Serve Robotics Inc. flywheel is monetization. This is the step that makes the whole flywheel self-sustaining. When you monetize your fleets, you fund the next turn of the cycle and make the flywheel accelerate much faster. The companies that figure this out early, and can get paid to collect their proprietary data, have a real advantage over those who have to pay for their data. Tesla is the obvious example. They collect massive amounts of road data to train their models by simply selling cars to consumers. One way we are really advancing our monetization is by increasing our revenue sources rapidly. Delivery fees are, of course, our core business. It is continuing to accelerate as we scale geographies. But branding and advertising saw a 50% increase in Q4 year over year. With 2,000 robots moving through high-density neighborhoods, we have effectively built a neighborhood-level media network on wheels. Advertisers’ response has been exceptional, and we are building a robust bookings pipeline. Over time, we believe advertising and branding can represent as much as 50% of our fleet revenues. Think about what that means. It monetizes miles that are already being driven, at nearly zero marginal cost. Also, data and platform revenues are emerging. In 2026, we plan to further invest in our data and platform capabilities to strengthen the foundation of our robotics solution offering. By offering the platform that powers our deployed robots to external partners and other robot operators, we expect this new revenue base to mature and become a meaningful, high-margin contributor. Also, going forward, healthcare revenue from Diligent Robotics will be another meaningful contributor: nearly 100 Moxie robots across over 25 hospital facilities, with each facility generating over $200,000 in annual revenue. This is already a fully functional business unit that is generating both meaningful data and meaningful revenues. Here is what ties everything together. Every dollar of revenue funds more robots, which leads to more data, which helps us create better models, which leads to even more deployments and more revenues. And the cycle repeats. The monetization does not just sustain the flywheel. It accelerates it. I think that our acquisition strategy also deserves a moment of its own. We have completed four acquisitions in the last twelve months. Every acquisition we have made maps directly to a step or two of the Serve Robotics Inc. flywheel. Phantom Auto strengthens our data collection and our deployment scale as well. VYU Robotics strengthens our model creation. Diligent Robotics further strengthens our data gathering by introducing a new operating domain and also boosts our monetization through recurring revenues with compelling economics. And last but not least, Veebo strengthens our delivery robot monetization by boosting our partnerships with restaurants and major QSRs. This is all deliberate. It is a flywheel-driven strategy. Each deal is designed to make the flywheel stronger. Now let me bring this back to our 2025 progress, and specifically, our Q4 results. In Q4, we exceeded our revenue guidance once again. Total revenue for the fourth quarter was $900,000, representing nearly 400% growth year over year, and also meaningful sequential acceleration. Full-year 2025 revenue came in above our $2,500,000 guidance at $2,700,000. We completed the deployment of our 2,000th robot in mid-December, on time and on plan. Q4 alone, we deployed nearly 1,000 robots. That is in a single quarter. That is more than many robotics companies’ entire fleet size. Delivery volume grew 53% quarter over quarter in Q4, and roughly 270% for the full year versus 2024. This is the compounding effect of fleet at scale. Also, it is the geographic expansion and the deepening platform partnerships, all of which are working in concert as we start to see the benefits. And we expect this growth to continue as we deploy new robots and also optimize their operations and utilization. Our merchant base has also expanded to over 4,500 restaurants and retail partners today. This is a more than 10x increase from roughly 400 a year ago. We now reach over 1,700,000 households in our metro areas. This covers a population of over 3,750,000 people. And we did all of this while maintaining our 99.8% delivery reliability and also our strong safety record. This is the part that I am most proud of. Scaling fast is hard, but scaling fast while maintaining quality and safety is what really separates us. Okay. I want to close with where all of this leads to. A year ago, we had roughly 100 robots. Today, we have 2,000. The path is clear from here to 10,000 robots and well beyond. This would be across more cities, more verticals, even internationally. We have the engineering and operations roadmap and also a track record of execution. The hardest part—building the platform and proving the technology, earning the trust of our partners and cities and consumers—these are all tailwinds now. What excites me most is that each additional robot we deploy makes the entire system more valuable. The data gets richer, the models get sharper, the economics improve, the partnerships deepen. This is the nature of a platform business with a flywheel at the core. We are just entering that phase where the compounding effect and the acceleration of the flywheel become visible. With the Diligent Robotics acquisition, we have extended this platform beyond the sidewalk and into hospitals. That is not a one-off. It is a signal of where things are heading. The robotics platform we are building will be general enough to operate wherever very, very intelligent machines are needed to move safely among people, and mature enough to deliver real commercial value right away. We are not building a delivery company. We are building the operating layer for how robots integrate into our lives. That is the long game, and we are playing it from a position of strength. 2025 was the year of proof. 2026 is the year of compounding returns. I have never been more energized about what is ahead. And with that, let me hand it over back to Brian.