Business Combination Prospectus (Form 425)

Filed by Churchill Capital Corp XI pursuant to Rule 425

under the Securities Act of 1933, as amended,

and deemed filed pursuant to Rule 14a-12

under the Securities Exchange Act of 1934, as amended

Subject Company: Churchill Capital Corp XI (File No. 001-43020)

Set forth below is a transcript of the webcast held by Agility Robotics, Inc. ("Agility") and Churchill Capital Corp XI ("Churchill") on June 24, 2026, in which the proposed business combination between Agility and Churchill is discussed.

Agility and Churchill Capital Corp. XI Business Combination Call

June 24, 2026

Anthony Rozmus:

Good morning. My name is Anthony Rozmus. Thank you for joining us today. It is my pleasure to welcome you to the Agility Robotics and Churchill Capital Corp XI Business Combination webcast. The webcast will be available for replay at www.agilityrobotics.com/investors where you will also find a copy of the investor presentation.

Please note that this morning's webcast contains forward-looking statements regarding future events and future performance of Agility and Churchill Capital. These forward-looking statements are based upon information available to Agility and Churchill Capital today, and reflect the current views and expectations of the company's actual results could differ materially from those contemplated by these forward-looking statements, including but not limited to, the timing of development, milestones, potential future customers and revenues, competitive industry outlook, and the timing and completion of the business combination.

Please refer to the press release issued this morning and the presentation accompanying this webcast for information on the risk regarding these forward-looking statements that could cause actual results to differ materially.

This conference call is for informational purposes only and shall not constitute an offer to buy any securities or solicitation of any vote in any jurisdiction pursuant to the proposed business combination or otherwise, nor shall there be any sale of securities in any jurisdiction in which the offer, solicitation, or sale would be unlawful prior to the registration or qualification under the securities law of any jurisdiction.

I now invite Michael Klein, chairman and CEO of Churchill XI to share greater detail of the business combination. Michael, when you're ready, please begin.

Michael Klein:

Good morning and welcome to today's announcement of Churchill XI's exciting merger with Agility Robotics. We at Churchill are extremely enthusiastic about today's announcements. We believe that Agility represents one of the most compelling opportunities in the emerging field of physical AI and specifically in humanoid robotics.

Agility is the first humanoid robot employed and commercially operational in warehouse and industrial facilities. It is an absolute leader and that leadership is backed by substantial investors, customers, and strategic partners. These include some of the most important players in the ecosystem: NVIDIA, Amazon, SoftBank, Foxconn.

In addition to that, impressive group of investors that are leaders across the entire AI ecosystem, the company today has a strong base of existing customers and a growing customer footprint. Those customers today include Amazon, Toyota, GXO, Schaeffler, Mercado Libre, and others. And those are moving from what were initial applications and pilots to large scale orders and a substantial backlog.

We're excited about this opportunity also because we're partnering with one of the great management teams in the space. We've known Peggy Johnson, the CEO for many years, both from her background in leadership at Qualcomm and at Microsoft, but also from her turning around specific consumer technology companies into critical enterprise technology companies. That's what Agility is. It's an enterprise technology that is being launched and will be scaled at a rapid pace. In addition to Peggy, Jonathan Hurst is one of the pioneers in modern humanoid robotics, both from a academic platform and now from a commercial and operational platform.

Before I hand over to Peggy and the team, I just want to give a couple of moments on how Agility fits into the Churchill Capital platform. Many of you will know Churchill Capital, we've created several sizable and fast-growing public companies. Most importantly, we've generated substantial returns for our investors.

Of the $11 billion of initial capital we've raised, the value of that capital has achieved north of $40 billion of value or a three and a half to four times upside. It's important for us to partner with great companies, but also companies that are ready to succeed in the public markets, both because they have management teams that are focused on creating shareholder value, but also because the companies are at a point of scaling, not at a point of technology, question marks. The value of the transaction today is about two and a half billion dollars. We'll be raising over $600 million, which will all be primary and all be growth capital for the company. Included in that $600 million is more than $200 million that's been raised alongside Churchill Capital XI. This comes from Foxconn as a lead investor. It comes from inside investors and it comes from a wonderful group of institutional investors as well.

All employees, all of the Churchill family and all individual and initial investors are rolling their equity in and we have substantial long-term lockup commitments as part of our commitment to the growth of the value of this company. This approach is very consistent with our most successful partnerships as we've built a set of companies that are all a part of the AI enablement strategy.

Our first OKLO, an advanced nuclear energy company had the same structure. That company having gone public at $10 per share reached a peak of $193 per share. We've done the same thing with a quantum computing company, Infleqtion, which we recently took public. That company has gone public at $10 per share and has reached a peak of nearly $28 per share. Both of these companies have substantial partnerships with the US government, U.S. Department of Defense, which is another core part of the thesis for our investments.

We believe Agility combines category leading technology, meaningful commercial traction, a compelling market opportunity, and an exceptional management team. We're very proud to partner with Peggy, Jonathan, and the entire Agility team. And with that, let me introduce Peggy.

Peggy Johnson:

Hi, I'm Peggy Johnson, CEO of Agility Robotics. Thank you for your interest in Agility. Before we get started, I wanted to introduce you to our robot, Digit, so you can see for yourself what we're going to be talking about today.

This is Digit doing real work for real customers, in this case Schaeffler. It's picking up these dirty automotive parts, putting them in a washer, unloading them on the other side, putting it in a dryer, and then stacking it. It does this over and over all day long. This is exactly the type of work that is a great entry point for humanoids because it's difficult to find humans to fill these rules.

I'm joined today by Agility's founder and Chief Robot Officer, Jonathan Hurst. He has spent decades in this field from earning his PhD at Carnegie Mellon to becoming a professor at Oregon State and establishing their robotics program before launching Agility.

Also with me today are: Daniel Diaz, our Chief Business Officer; Anna Lang, our Chief Legal Officer, who were together with me at Magic Leap and they carry deep experience commercializing early stage tech. Also, I have Jen Hunter, our CFO and COO, who brings a wealth of financial and operational expertise, including spending 10 years at Amazon and in their robotics division. I will ask the team to discuss specific aspects of their various roles in this presentation.

For some background on me, I'm an electrical engineer by training and I've always been drawn to the introduction of emerging tech into commercial markets. I spent 25 years at Qualcomm where I had essentially a front row seat to the rise of the mobile phone industry. I worked across the tech there that were very critical to the commercialization of mobile phones and from leading antenna systems to cell sites and finally to our scale product, semiconductors.

I was leading a division at Qualcomm when Satya Nadella gave me a call in 2014 just after he became CEO of Microsoft. He asked me to lead business development for them, supporting all of their hardware and software products, including Microsoft Surface, Xbox, HoloLens, and of course, Office 365 and Azure.

Both Qualcomm and Microsoft gave me the foundations required to navigate early tech into tangible business solutions for enterprises. So in 2020, I stepped in as the CEO at Magic Leap and leaning on these experiences, I refocused that company from consumer play towards an enterprise solution.

I've spent decades building an understanding of how enterprises adopt technology. And what I've learned is that companies don't buy tech, they buy solutions. You have to be solving a problem for them. And now at Agility, we're doing exactly that.

Today, we're at a fascinating intersection of humanoid robots and AI, and Agility alone is delivering true product market fit to companies adopting our robots. With all of the headlines around robots, what often gets missed is that AI alone isn't enough to scale them. Companies still have to solve real physical problems by making stable hardware. It's not enough just to do back flips. Companies don't buy that.

So early on, Jonathan and his team began testing a broad range of real world applications that led them to one of the strongest initial use cases for humanoids, which is simple material handling. This is manual labor that's repetitive, dirty, very injury-prone. So what drew me to Agility was I could see the team had found product market fit and they simply needed to scale.

Their robot was already capable of delivering customers an ROI and they were able to do it at a discount to human labor rates. The demand here is large and increasing. We have companies re-shoring production, older workers retiring, and younger generations just not opting for these types of menial jobs.

There are already more than a million unfilled jobs in this area right now in the US and by 2032, the TAM that our robot digit can address will be 1.25 trillion. To ready our business for scale, we make digit right here in the US. We're currently building our fifth generation robot and that means we've learned how to manufacture quite efficiently over the years. We've steadily taken cost out of the product, and today, we can produce a robot for $125,000. And with our next generation we have clear line of sight to a bill of materials of just $30,000. Volume will certainly help us to reach that bill of materials, but since we are vertically integrated and we control the whole stack, we have control of further engineering innovation that'll make up the majority of that cost reduction.

Today, Digit is fully autonomous, which means we operate our robots without the need for human intervention or what's called teleoperation, as you might have seen in the press. Still today, Digit remains the only humanoid capable of walking into commercial facilities and getting paid to work. At this point in time, we essentially own the addressable market. So, while material handling as an initial use case isn't super flashy, it does build a sustainable humanoid business because we're solving for real business needs.

Along with Schaeffler, Digit supports operations for companies like Amazon, GXO, and Toyota. We've also recently signed a number of new customers and have over 30 or so engaged in our sales pipeline. They all struggle with the same problem, humans don't want the jobs they need to be filled to maintain their production levels at these facilities. And customers are beginning to move from proof of concepts to deployments, with binding orders for our version five platform now totaling over $300 million. Along our journey, we've been backed by some world-class investors, Amazon, NVIDIA, SoftBank, Foxconn, Sony, Schaeffler, Abico, and many others who believe in the pragmatic route that agility is on. And our insiders are supporting us again, committing over $60 million to our pipe with Foxconn leading.

But success in humanoid robotics ultimately comes down to one thing, building a robot that can work safely alongside people. Right now, all humanoids must work inside safety cells cordoned off from humans. These are powerful mobile devices that can do serious harm. Later this year, when we launch our fifth generation Digit, we will be the world's first humanoid robot designed to operate in close proximity with people. We accomplished this through a highly complex AI-enabled human detection system designed to keep humanoids safe from robot harm.

We're also proud to be the very first company to partner with NVIDIA on its Halo humanoid platform to provide for safety and humanoids. Our solution also leverages NVIDIA's powerful IGX Thor chip, and our proprietary AI-based perception systems to deliver robots to the industry who can safely move through facilities full of people. This is actually a feat many in the robotics industry thought impossible, and our ability to achieve this unlocks significant new markets for us because Digit, and only Digit, can for the first time leave that safety work cell and walk amongst humans so they can do various jobs throughout facilities throughout the day, just like a human worker.

Also working with our customers, we learned of additional requirements they value, including the incorporation of fast charge batteries that allow a single robot to handle three shifts, cloud software to manage the fleets of robots that will be coming into these facilities, and powerful actuators capable of lifting up to 50 pounds over and over and over again. As we incorporate this functionality, our moat increases dramatically, because we're meeting the real-time operational needs of the industry.

Commercial deployments also have a powerful side benefit for Agility because we can obtain true operational data. This data is currency for building foundation models for robots. So, every deployment creates a flywheel of value for us. Data improves the AI model, which then accelerates the skills we can teach the robot, which then opens up new verticals for us.

And finally, we're expanding our business beyond just humanoids. This includes licensing our proprietary actuator technology and our fleet intelligence software into the burgeoning robotics industry, which will provide new revenue streams for Agility. Given our first mover position here, this transaction provides us a significant advantage. We're seeking to enter the public markets to accelerate our customer deployments, produce Digit version five, and maintain our competitive lead. We've spent years solving for this very difficult physical challenge of humanoids, and the introduction of AI has supercharged our efforts.

To give you more depth on Agility's technical journey, I'd like now to turn it over to our Co-Founder and Chief Robot Officer, Jonathan Hurst.

Jonathan Hurst:

Thank you, Peggy. So I'm going to give a little bit about my background, and then go into the stages of our company as we've matured and progressed over the past 10 years, and then go into some more detail about the system, the hardware, the AI, etc. So in terms of my background, my career-long mission has been to build machines that can interact physically in the world more like humans and animals do, and less like traditional robots, less like robot arms and CNC machines and that kind of thing. And that is our whole goal here, is to figure out how to make machines that can go where people go and do useful work in human workspaces and do workflows that have been designed around people.

So for a long time, this was a basic science problem where we're applying learnings from biomechanics to robotics and trying to understand how it is that animals do what they do, understand it from a first principles point of view, a scientific point of view, and then reproduce that by designing a machine to capture those same physics. And early on, if we think about the areas of the company before the company started, this was a very academic pursuit. This was something that was best solved at the university. And so through my graduate work and as a professor at Oregon State, that was the work then. Now, we did solve some of the core challenges. We did solve some of our understandings of how physical interaction works, to the point where being able to have customer needs, being able to sell a product, and being able to do great engineering were really the blockers and things that really aren't done so much at a university, and that's when we founded Agility Robotics.

So when we first started, our very first product was Cassie, and I would call this our R&D era. Now maybe we'd solve some of the core science, but there's a lot of engineering to do in order to execute well and to implement this. And an example of this with Cassie is our special cycloidal actuators, which allowed this robot to have very, very good force control and be able to impact the ground repeatedly without any problem, something that previous actuators and previous machines could not do. So, we sold Cassie to some of the top universities in the world, like Berkeley, and University of Michigan, Caltech, Georgia Tech and others, because this was really the only machine on the market at the time that was physically capable of a biomechanically relevant movement and behaviors in the world.

Now, we always knew that we were on the mission towards robots doing useful work in the world, which means manipulators and perception. So launching off of the initial sales of Cassie, we designed and built Digit, a humanoid robot, and began what I would call the proof of concept era, where these first versions of Digit are our engineering team imagining, what does a robot need to be like for general purpose behaviors in the world? Now this allowed us to deploy, and we looked at literally hundreds of different use cases to try and find the best match between this technology, which we knew to be truly enabling, and something that actually solves problems for our customer. And this is when we started to realize that picking up bins and totes is an awfully good beachhead market. It's something that is somewhat structured but is still not automated, because the workflow is different in all of the different places where all the different types of bins and totes are used and picked up off of shelves, or autonomous mobile robots and put back on conveyor belts, or stacked many, many different workflows.

So as we really learned that, we were able to then develop our robot Digit v4 and be the first to commercialize the machine. In other words, Digit was able to do a useful case for GXO. They were our first deployment, commercial deployment of humanoid robot. And actually be able to have the robot in the workflow, not just part of the R&D, but in the workflow doing work, getting paid for that work.

Now, there's a few things that actually keep Digit from scaling up to thousands and thousands of robots, but the biggest one is safety. We learned in our commercial deployments that safety is the biggest blocker. That's one of the major things that Digit v5 achieves, among others, and that is our scaling moment. So once we deploy Digit v5, which is coming very soon in the coming months, this will be the scaling moment for humanoid robots. The first time a robot is able to step outside of a fenced area, step outside of a work cell, and just walk into unmodified human environments in warehouses and manufacturing environments and do so, following a regulatory environment and meeting the actual third party verified functional safety requirements. So, that is really quite exciting.

So I want to talk a little bit now, this is the visible part of the machine, of course, the humanoid robot, and that does have to be quite special, a lot needs to be done well and done right about that. But there's other pieces of this system that are not as visible but are equally big, basically. And I'll start with the embodied AI. Of course, having the robot learn the skills and the dexterity and the mobility and the decision making in order to operate in the world is critical. In addition, the robot coordination, the fleets of robots, and having 1,000 different robots operating in all coordinated and synchronized, working together, that is our fleet management system, Arc. Also a critical piece, also big enough to be a product in and of itself. And finally, the cooperative safety is something that is integrated from top to bottom in the entire robot. It touches every system of the machine. And so, I'll go into a little bit of detail on each of these things, starting with the hardware.

It's a very important point that Digit was not designed to be a humanoid. We never set out to build a machine that looks like a person. We've been really focused on first principles of at the beginning solving some of the science challenges. Then first principles of engineering implementation, because we're building machines out of motors and metal, we're not using muscle and bone. So the morphology, the shape of the machine may be quite different than what you'd see in an animal or a human, even though we want to achieve some of the same physics. And that has continued to evolve towards first principles of customer requirements and metrics about, what is it that we actually need to achieve?

So the vision is, and the hypothesis is, we'll get something that looks a little bit humanoid. The reason is because to work in human environments, we do need to operate in aisle ways and hallways and doorways and narrow spaces, while still being able to lift heavy things up high, like to the top of a two-meter shelf. And that means the robot needs to be balancing. It cannot be statically stable, the base would need to get much, much too large or it would get very tippy and tip over. So it needs to be actively balancing all the time, and legs are the most stable way to be dynamically stable and the best way to be balancing. Having a torso be upright is how the robot is able to lean and balance in the direction it needs to go while fitting in all the computing and battery and everything else. Also allows us to mount the arms and the sensors and things up high in order to see what's going on in the human environment.

Having two arms is important so that we can lift large things. A single arm can only lift small things, the manipulator starts to get really big, but two arms allow you to pick up large things such as the bins and the boxes. It also allows you to reposition objects in your hand, so it really simplifies in hand manipulation that you can just swap between hands. And then having that mounted up high also helps us with inertial actuation balancing, it helps us with catching the robot when it falls, getting back up after it has fallen on the ground.

And then finally, when you've got two legs and two arms and an upright torso, it's a real opportunity here to be able to communicate on human terms. You have some body language, you also have expectations of where a face would be. And that human aware design is really important and having some indicators about what the robot is able to do, what is it focusing on? You can do that in a way that is intuitive to people around and you don't have to train people. This is a human-centric robot. It's something where the robot is operating on human terms, human environment.

This performance is really driven broadly by a handful of really critical systems on the robot. So I mentioned the actuators. Cycloidal actuators are practically indestructible. They've shown no signs of wear even in the 10 years that we've been operating them on our robots with repeated impacts on the ground and they give us exceptional force control inherently because the transmissions are 95-96% efficient. The properties of these beat anything that exists on the market today for the specific use cases and purposes of our robot for this use case, for this machine.

Also, our end-effectors are modular. So we get to design the manipulators that can lift 50 lbs bins, which nothing off of the shelf can do. So it is hard to do that. But then we can also swap out for a variety of different end-effectors for different use cases and work towards increasing generality there with manipulators. Then broadly, the sensor architecture, our super-fast charge battery, and the system-wide safety piece. So all of that is hardware top to bottom, which really has to be done well.

Now, to talk about AI just a little bit. We think of AI as a big tent of computational tools, not just one black box. And at the top, you think of something like the semantic AI, which is what people are mostly familiar with working with Google's Gemini or Anthropic's Claude or any of these chatbots where it's essentially solving perception for us. The robots can see what's in the environment, identify what's there, and then identify basically what would you do with those kinds of things.

Now, the reason that this is so powerful in part is because the world has had the entire internet to train from. And so we have all of the images, all of the different language and words and all of the videos in order to then generate patterns and understand how to interact with that. That does not exist for controlling a robot, that source of data. There is no list or database of the right answers for what torques to command the individual motors given sensor inputs on our piece of hardware.

That's something that has to be created, has to be discovered. That's the physical AI actually being able to control a robot. And the semantic AI cannot do that. So agility owns the physical AI and we are creating that that is more specific to our robot. The semantic AI is much more broadly general. It's something that is essentially a utility or a commodity at this point.

Everybody has access to Google's Gemini and it is a rising tide. It's lifting all boats, very powerful tool that enables our robots to operate in more general environments. Then the physical AI though, think of it as two different pieces on top of very capable hardware. The first is the set of skills that the robot will need to have. How do you grasp this? How do you pick up that? How do you walk over here? And we train our robot there with forms of learning from demonstration.

Now, there's many sources of data that are useful, including teleoperation or motion capture or even animation that we can create on the computer telling the robot what we want it to do. That's a very good starting point. Now, importantly, this is very different from the type of training that's used for a large language model. We don't need tens of thousands of hours of teleoperation data and don't seek that. We philosophically don't believe that's the strongest approach to do this. We think of this learning from demonstration more as a modest amount of data that's a really good starting point to show the robot what to do.

What's very important then is for the robot to essentially practice. And this is reinforcement learning and simulation allowing the robot to really get good at that task that's going to be a little bit different than the initial example because remember, this robot is not exactly like a person. So looking at the example of a person doing it, the robot's going to do it a little bit differently and has to figure out how it's going to do it. It's just that having that hint allows it to search a much narrower space to get really good at that particular behavior.

So that's the coordination piece. Those pipelines are continuing to mature. That's how we control our robots to do walking and running and grasping and any of the things that the robot does. Give it some hints through whatever data that we want and then practice in simulation. And over time, this is going to be reinforcement learning in real life on fleets of robots.

Now next, I'll talk a little bit about Arc, our fleet management software. This connects Digit to all of the customer operations. This is how not only we are gathering all of the data from the robot, I mentioned reinforcement learning in real life, all of the data that will be collected comes through our Arc fleet management system. Also, when we have hundreds and thousands of robots out there and they're doing different tasks in different places in a warehouse or a manufacturing plant on a given day, Arc is what coordinates all of those machines.

It's also coordinating with all of the other automation that exists, all of the other AMRs, autonomous mobile robots, the robot arms that it may be picking things off of or interacting with and engaging with the warehouse management systems of these plants that Digit works in.

The other thing that it gives us is a health of every robot. We know exactly what's going on. If there's any error flags, if a robot is confused, all of that comes back to our main operation center and all of it is done through Agility Arc. Now, because Arc engages with warehouse management systems and all of the other forms of automation, we expect that that's going to be a very useful product in and of itself, whether or not Digit is part of that. So that's something that is also part of our business.

Now, finally, I'm going to land on safety because as I mentioned, safety is the big blocker and the major enabler now that we have taken those first steps to really solve it. At the beginning, Digit is a new class of machine. I mentioned that it is dynamically stable and balancing, which means that it can fall and that's a new type of risk that's not envisioned in any of the regulatory standards that exist.

So we have had to create a best practices process for how a dynamically stable robot like Digit would operate safely in a human environment. And at the beginning, before we've generated that, Digit has to exist inside of a work cell as does any other dynamically stable robot, any other humanoid would need to operate with a work cell around it. This is because companies need to have all of their automation insured. The insurance companies have to have metrics and an understanding of exactly how much risk they're underwriting. And that's typically because third parties are validating that these machines, these pieces of automation are meeting various pieces of regulatory compliance.

Now, we have had to work to generate that regulatory environment as we create the engineering best practices for the machine in order to have those third parties then validate that we've done the formal hazard and risk assessment and we've understood all the possible risks and all of the ways in which this robot might be able to cause injury and we've mitigated those risks and found a strategy that's going to enable the robot to operate in human spaces.

So in order to get out of the work cell, which is kind of the first place where Digit v4 and other dynamically stable robots have to operate, Digit v5 is able to perceive and identify human beings operating in its vicinity and differentiate between people and something like a statue of that same person. It has to be very good at identifying people. And then, the robot can identify different behaviors as a person starts to approach. It can stop moving, set its payload down on the ground and even sit down on the ground and turn off its power before a person would be able to physically touch the robot.

Now we've been able to do this. This is a holistic bottom to top design exercise that includes ... touches basically every feature of the robot. Digit v5 will be able to operate in cooperative safety mode, be able to deploy tens of thousands of robots that can operate in human environments and human spaces adjacent to people to be able to collect and build the data in order to be able to actually achieve the long-term goal of this collaborative safety.

Now that, along with the continued improvement on manipulation and all of the skills that the robot will grow over time, that's how we actually achieve this dream that humans have had forever of having robot helpers in our homes and part of everyday life. It starts with the first use case, which we are doing right now and this is picking up bins and totes and something that's the classic 3Ds of robotics, the dull, dirty, dangerous kinds of tasks that have been hard to automate to date.

And that's why people still do these things, but Digit can walk into these environments unmodified and start to do these initial tasks, then work up towards each picking and say kitting and moving individual pieces and using totes in addition and then continue to get better at the manipulation, do cardboard boxes, get better at safety, start working into retail like working in a grocery store and stocking shelves and working in the backroom unpacking things, working into construction and working in machine tenting. That'll come early and then gradually work our way out towards robots that can help us at our homes.

And this is very exciting. We believe this is the fastest path to get to this big dream of robots that help us in everyday life and we're getting started right now.

Peggy Johnson:

Thank you, Jonathan. As you've seen, there is a tremendous amount of innovation behind Digit v5 and the broader agility platform. Just as importantly, that technology is translating into real-world customer adoption and value. To discuss our commercial momentum and customer engagement, I'd like to invite our Chief Business Officer to speak, Daniel Diez.

Daniel Diez:

Thank you, Peggy. Well, as you've heard, Digit is really designed to work where people work and where labor is needed most. We've chosen three beachhead markets in manufacturing, distribution and logistics and each one of them was chosen for a specific purpose. These markets share some very common traits in that they have large labor pools. They spend lots of money on labor and they have a persistent need to hire more and more people and it is getting more and more challenging for them to do so.

They have very typical workflows that are physical and repetitive, and these can be very difficult to automate and difficult to recruit for. They have very structured workflows. They look very similar, which allows us to repeat tasks both within a customer's environment, but also repeat them in other customers and other verticals. And they have existing infrastructure that has been designed for people to work in and these robots are designed to work where people work. And so it is an incredible opportunity in the near term for us to deploy humanoids to take on these tasks that have been difficult to recruit for.

Digit's been proving itself every day in real world operations. Our deployments have been demonstrating that Agility can perform useful work every day and deliver ROI in these very intense environments. Agility has had deployments with customers like Schaeffler, GXO, Amazon, Toyota, Mercado Libre, and a number of other customers in the automotive sector, in general manufacturing, warehousing, logistics. Digit has been hard at work. And at Schaeffler and GXO, we've been deployed for years demonstrating accuracy topping out at 98% and moving over 125,000 totes in live operations.

These aren't demos or an R&D lab. These are physical environments. And in these environments, digital offers measurable ROI. Customers can choose to deploy digit in one of two ways with us and if they choose our robot as a service offering, they pay a price that's based on a discount to their fully burdened human labor rate. And what that means is the customer experiences ROI on day one of operation because we are simply cheaper than the human labor that they could deploy if they could find it. And if the customer chooses to purchase the robot, Digit v5 will deliver a breakeven ROI in only 1.1 years.

Either option has the potential to deliver significant savings compared to their existing human labor rates.

The way we're deploying Digit these days is with our customer acceleration program, which is designed to deliver operational deployments for customers in as little as four months. It's designed for customers that can demonstrate a real business challenge, specifically labor shortages and the ability or desire to scale humanoid deployments in the near term.

The program takes our customers through a proof of tech, an onsite proof of concept and a three-month pilot RaaS deployment. And the customer then continues to deploy this robot in the robot as a service engagement as they prepare to scale with the v5 robot as it becomes available at the end of this year.

Digit v5 has been designed to expand the use cases that we've established as our beachhead market and greatly increase the TAM available to Agility, and it does that through the addition of dexterous object manipulation. This brings use cases like machine tending, item manipulation, sorting, decanting, even carton manipulation into our set of offerings. And demand for each of these new skills has been driven and validated by existing customer needs and data gathered from our pipeline of prospective customers. And as we continue to deploy, we'll continue to gather critical intelligence that will drive scale within existing and new customer sites, accelerate the deployment of new workflows and skills, and ultimately win new customers based on our proven track record of results.

Peggy Johnson:

Thank you, Daniel. We're proud to be delivering useful work and measurable ROI for our customers today.

The next question is, how does Agility capture that value and scale the business over time? To discuss our operating model and financial strategy, I'd like to turn it over to Jen Hunter, Agility's Chief Operating Officer and Chief Financial Officer.

Jen Hunter:

Thank you, Peggy.

One of the advantages of our business model is that we can meet customers where they are in their automation journey. Agility offers two primary adoption models. The first is Robotics-as-a-Service, or RaaS, where customers pay a recurring subscription fee and Agility retains ownership of the robot. The second is a traditional ownership, or CapEx model, where the customer purchases the robot and then subscribes to software and support services. Today, many customers are demonstrating an affinity for the RaaS model, as it reduces upfront capital requirements, simplifies deployment, and allows customers to adopt humanoid technology with minimal friction.

For organizations that are still early in their adoption journey, this can be a very compelling way to begin scaling deployments. That preference is already evident in our customer pipeline. To date, we have secured more than $300 million of contracts with Digit v5 orders, the vast majority of which are structured under our RaaS model.

Importantly, RaaS is also highly attractive to Agility. It creates a recurring revenue stream, a growing installation base, and strong customer retention. Based on the assumptions shown here, a single Digit deployment can generate approximately $500,000 of cumulative revenue over a five-year useful life. Over time, we expect customers to increasingly transition towards ownership as humanoids become more established as a part of their operations and capital planning processes, and viewed really just as they see any other piece of automation within their facility.

The ownership model provides immediate deployment revenue for Agility while still generating recurring software and service revenue over time. Under this model, a Digit deployment can generate approximately $400,000 of cumulative revenue over the five-year assumed life of the asset. Ultimately, as a business, we aim to drive a benefit to our customers and providing flexibility in how they adopt Digit while Agility generates attractive lifetime economics under either model.

This slide illustrates how revenue and economics evolve under each adoption model. The primary difference between the two models is the timing of the revenue realization. Under the RaaS model, revenue is recognized over the useful life of the robot, creating a highly recurring revenue stream with relatively even revenue realization over time. Under the ownership model, a larger portion of revenue is recognized upfront through the sale of the robot with recurring revenue on software and services generated thereafter. Under the RaaS model, we're achieving payback in less than one year, even using the conservative assumptions reflected on this slide.

As we continue to optimize the bill of material, improve manufacturing efficiency and scale production, we expect that payback period to improve even further. It is also important to note that the assumptions shown here affect a relatively conservative pricing framework. As customers increasingly recognize the value that Digit creates, we believe there will be opportunities to further optimize pricing over time.

From a margin perspective, both models already support attractive economics today and we see a clear path towards product margins in excess of 70% as the business scales. These improvements are driven by lower cost of product, manufacturing scale, supply chain optimization, and the increasing combination of both software and service revenue. Both business models support compelling unit economics, rapid capital recovery, and long-term margins that we believe will compare favorably with even the best businesses in industrial technology.

Next, I would like to shift towards our bill of material and cost structure. One of the questions we receive most often is how quickly humanoid economics improve as production scales. The attractive unit economics we discussed on the previous slide, including payback periods of less than one year, and a path towards product margins exceeding 70% over time are driven by our ability to reduce the cost of Digit as production volumes increase. Importantly, this is not something that we're forecasting for the first time. It is something we've already demonstrated as we're underway in building our fifth generation of the robot. Through engineering improvements, supplier engagement, manufacturing experience, and increasing scale, we successfully reduced the bill of materials for Digit v4 to approximately $125,000 per unit today. We are now applying those same learnings to Digit v5.

Importantly, Digit v5 is not a concept or a design on paper. We have already built and produced v5 units and our early production experience is validating the cost reduction roadmap shown on this slide. As we move from early production towards larger scale manufacturing, we expect v5 to follow a similar cost curve to what we successfully achieved with v4. Experience in design, manufacturing, and scaling is what sets us up for success to deliver meaningful cost reductions through engineering execution and manufacturing scale. Based on the work completed to date and the opportunities that still lie ahead of us, we believe we have a clear path toward a bill of material which costs approximately $30,000 per Digit over time.

The attractive economics we've discussed are only meaningful if you have the infrastructure required to scale production and support customers. We believe that this is one of Agility's most important competitive advantages. Over the past decade, we have invested in building not only a robot, but the broader platform that is required to deploy humanoids at commercial scale. That includes RoboFab, our purpose-built manufacturing facility, Agility Arc, our software platform for deployment, fleet management, and orchestration of the automation across our customer facilities, a predominantly domestic supply chain, and ownership of many of the highest value components and subsystems within Digit.

When taken together, these capabilities provide us with greater control over quality, cost, production, and the customer experience. Many companies in the industry are still focused on demonstrating what humanoids can do. We have spent years building the infrastructure required to deploy and support them at scale. We believe that foundation will become increasingly valuable as the customer adoption begins and accelerates.

A great example of the infrastructure is RoboFab. RoboFab is the world's first purpose-built humanoid manufacturing facility and represents a significant milestone for both Agility and the broader robotics industry. Located in Salem, Oregon, RoboFab was stood up in 2024 and designed by our own team specifically for the production of humanoid robots. Our facility utilizes a modular manufacturing architecture that allows us to efficiently increase production as demand grows, with the ability to support production of up to 10,000 robots annually with low capital investment. Importantly, RoboFab is not a future capability. It exists today, and we are already producing Digit robots.

One of the unique aspects of RoboFab is the close integration between our engineering and manufacturing teams. Our engineers work directly alongside the production teams on the factory floor, creating a rapid feedback loop between design, manufacturing, and deployment. This allows us to identify opportunities, implement improvements, and iterate significantly faster than in a traditional manufacturing model. As we discussed earlier, one of the keys to improving economics is manufacturing experience and production learnings. RoboFab provides us with that environment where we can continuously improve quality, reduce our costs, and accelerate production as volume increase. We believe manufacturing capability will become increasingly important as a differentiator as the industry transitions from pilot programs to scaled commercial deployments. Taken together, our technology platform, customer traction, compelling unit economics, and manufacturing infrastructure position Agility to lead the next phase of humanoid adoption.

With that, I'll turn it back to Peggy for some closing remarks.

Peggy Johnson:

Thank you, Jen.

As we conclude today, I would like to leave you with a few thoughts about why we are so excited about the future of Agility. Over the course of today's presentation, you heard about the tremendous market opportunity in front of us, our differentiated technology platform, our commitment to safety, our growing customer base, and the strong economics that underpin our business model.

Most importantly, you heard that this is no longer a vision of the future. Humanoid robots are already performing useful work today. Customers are already deploying Digit in real-world environments, and we are already seeing the commercial demand that we believe will drive the next phase of growth for our company and our industry. We believe Agility is uniquely positioned to lead that transition. We have spent more than a decade developing the tech, building the team, establishing customer relationships, and creating the infrastructure required to scale.

Today, we believe we have assembled one of the most complete and integrated platforms in the industry, spanning robotics, physical AI, software, safety systems, manufacturing, and deployment. That brings us to why we're going public now. The humanoid robotics industry is reaching an important inflection point. Customers are moving beyond pilots and proof of concepts towards scale deployments. Advances in our AI systems are expanding what robots can do and demand for automation continues to grow as businesses address persistent labor shortages and increasing productivity requirements. We believe now is the right time to accelerate.

We are thrilled with the strong investor support we have received so far with more than $200 million of capital committed to the transaction from new and existing investors. In total, we expect to raise more than $620 million of gross proceeds. Agility tends to use this capital to fulfill existing customer orders, expand commercial deployments, scale production of Digit v5, and continue investing in our integrated platform, spanning robotics, physical AI, software safety systems, and manufacturing.

We're excited to welcome Churchill as our partner. Michael and the Churchill team have a proven track record of supporting innovation technology companies as they enter the public markets, and we look forward to working together in the years ahead. On behalf of Jonathan, Jen, Daniel, and the entire Agility team, thank you for joining us today and for your interest in our company. We are incredibly excited about the journey ahead and look forward to meeting many of you in person in the coming weeks. Thank you.

***

Additional Information About the Proposed Transaction and Where to Find It

The proposed transaction will be submitted to shareholders of Churchill for their consideration. Churchill intends to file a registration statement on Form S-4 (the "Registration Statement") with the U.S. Securities and Exchange Commission (the "SEC"), which will include preliminary and definitive proxy statements to be distributed to Churchill's shareholders in connection with Churchill's solicitation of proxies for the vote by Churchill's shareholders in connection with the proposed transaction and other matters to be described in the Registration Statement, as well as the prospectus relating to the offer of the securities to be issued to Agility stockholders in connection with the completion of the proposed transaction. After the Registration Statement has been filed and declared effective, a definitive proxy statement/prospectus/consent solicitation statement and other relevant documents will be mailed to Agility stockholders and Churchill shareholders as of the record date established for voting on the proposed transaction. Before making any voting or investment decision, Churchill and Agility shareholders and other interested persons are advised to read, once available, the preliminary proxy statement/prospectus and any amendments thereto and, once available, the definitive proxy statement/prospectus, as well as other documents filed with the SEC by Churchill in connection with the proposed transaction, as these documents will contain important information about Churchill, Agility and the proposed transaction. Shareholders may obtain a copy of the preliminary or definitive proxy statement/prospectus, once available, as well as other documents filed by Churchill with the SEC, without charge, at the SEC's website located at www.sec.gov or by directing a written request to Churchill Capital Corp XI, 640 Fifth Avenue, 14th Floor, New York, NY 10019

Forward-Looking Statements

This communication includes "forward-looking statements" within the meaning of the federal securities laws. Forward-looking statements may be identified by the use of words such as "estimate," "plan," "project," "forecast," "intend," "will," "expect," "anticipate," "believe," "seek," "target," "continue," "could," "may," "might," "possible," "potential," "predict," "should," "would" or similar expressions that predict or indicate future events or trends or that are not statements of historical matters, but the absence of these words does not mean that a statement is not forward-looking. We have based these forward-looking statements on current expectations and projections about future events.

These statements include: statements relating to, without limitation: our ability to consummate the Merger and PIPE Investment and the satisfaction or waiver of the closing conditions set forth in the Merger Agreement and Subscription Agreement; the occurrence of any other event, change or other circumstances that could give rise to the termination of the Merger Agreement or Subscription Agreements; projections of market opportunity and market share; estimates of customer adoption rates and usage patterns; projections regarding Agility's future development plans; the timing and success of Agility's future development plans; the ability of Agility to implement its strategic initiatives and continue to innovate its existing products and services; the potential for share price appreciation; the expected timing of announcement and close of the potential transaction; Agility's economic opportunity and total addressable market; the expected amount of gross transaction proceeds and the planned pre-money valuation of Agility; expectations regarding Agility's ability to attract, retain and expand its customer base; Agility's deployment of proceeds from capital raising transactions; Agility's expectations concerning relationships with strategic partners, suppliers, regulatory bodies and other third parties; Agility's ability to maintain, protect and enhance its intellectual property; future ventures or investments in companies, products, services or technologies; development of favorable regulations affecting Agility's markets; the potential benefits of the proposed transaction and expectations related to its terms and timing; and the potential for the combined company to increase in value.

These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on as, a guarantee, an assurance, a prediction or a definitive statement of fact or probability. Actual events and circumstances are difficult or impossible to predict and will differ from assumptions, many of which are beyond the control of Agility and Churchill.

These forward-looking statements are subject to known and unknown risks, uncertainties and assumptions that may cause Churchill's actual results, levels of activity, performance or achievements to be materially different from any future results, levels of activity, performance or achievements expressed or implied by such statements. Such risks and uncertainties include: that Agility is pursuing an emerging technology, faces significant technical challenges and may not achieve commercialization or market acceptance; Agility's historical net losses and limited operating history; Agility's expectations regarding future financial performance, capital requirements and unit economics; Agility's use and reporting of business and operational metrics; Agility's competitive landscape; Agility's dependence on members of its senior management and its ability to attract and retain qualified personnel; the potential need for additional future financing; Agility's ability to manage growth and expand its operations; potential future acquisitions or investments in companies, products, services or technologies; Agility's reliance on strategic partners and other third parties; Agility's ability to maintain, protect and defend its intellectual property rights; risks associated with privacy, data protection or cybersecurity incidents and related regulations; the use, rate of adoption and regulation of artificial intelligence and machine learning; uncertainty or changes with respect to laws and regulations; uncertainty or changes with respect to taxes, trade conditions and the macroeconomic environment; the combined company's ability to maintain internal control over financial reporting and operate a public company; the risk that the proposed transaction may not be completed in a timely manner or at all, which may adversely affect the price of Churchill's securities; the failure by the parties to satisfy the conditions to consummation of the proposed transaction, including the approval of Churchill's shareholders; the possibility that required regulatory approvals for the proposed transaction are delayed or are not obtained, which could adversely affect the combined company or the expected benefits of the proposed transaction; the risk that shareholders of Churchill could elect to have their shares redeemed, leaving the combined company with insufficient cash to execute its business plans; the level of redemptions of Churchill's public shareholders; the ability of Agility to grow and manage growth, maintain relationships with customers and retain its management and key employees; costs related to the proposed transaction; the occurrence of any event, change or other circumstance that could give rise to the termination of the business combination agreement; the outcome of any legal proceedings or government investigations that may be commenced against Agility or Churchill; failure to realize the anticipated benefits of the proposed transaction; Agility's estimates of expenses and profitability; the evolution of the markets in which Agility competes; the ability of Churchill or the combined company to issue equity or equity-linked securities in connection with the proposed transaction or in the future; and other factors described in Churchill's filings with the SEC. Additional information concerning these and other factors that may impact such forward-looking statements can be found in filings and potential filings by Agility, Churchill or the combined company resulting from the proposed transaction with the SEC, including under the heading "Risk Factors." If any of these risks materialize or assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. In addition, these statements reflect the expectations, plans and forecasts of Agility's and Churchill's management as of the date of this communication; subsequent events and developments may cause their assessments to change. While Agility and Churchill may elect to update these forward-looking statements at some point in the future, they specifically disclaim any obligation to do so. Accordingly, undue reliance should not be placed upon these statements.

In addition, statements that "we believe" and similar statements reflect Churchill's beliefs and opinions on the relevant subject. These statements are based upon information available to us as of the date of this communication, and while we believe such information forms a reasonable basis for such statements, such information may be limited or incomplete, and Churchill's statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are inherently uncertain and investors are cautioned not to unduly rely upon these statements.

An investment in Churchill is not an investment in any of Churchill's founders' or sponsors' past investments, companies or affiliated funds.

The historical results of those investments are not indicative of future performance of Churchill, which may differ materially from the performance of Churchill's founders' or sponsors' past investments.

Participants in the Solicitation

Churchill, Agility and certain of their respective directors, executive officers and other members of management and employees may, under SEC rules, be deemed to be participants in the solicitation of proxies from Churchill's shareholders in connection with the proposed transaction. Information regarding the persons who may, under SEC rules, be deemed participants in the solicitation of Churchill's shareholders in connection with the proposed transaction will be set forth in proxy statement/prospectus statement when it is filed by Churchill with the SEC. You can find more information about Churchill's directors and executive officers in Churchill's final prospectus related to its initial public offering filed with the SEC on December 16, 2025. Additional information regarding the participants in the proxy solicitation and a description of their direct and indirect interests will be included in the proxy statement/prospectus statement when it becomes available. Shareholders, potential investors and other interested persons should read the proxy statement/prospectus statement carefully when it becomes available before making any voting or investment decisions. You may obtain free copies of these documents from the sources described above.

No Offer or Solicitation

This communication does not constitute an offer to sell or the solicitation of an offer to buy any securities, or a solicitation of any vote or approval, nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. This communication is not, and under no circumstances is to be construed as, a prospectus, an advertisement or a public offering of the securities described herein in the United States or any other jurisdiction. No offer of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended, or exemptions therefrom. INVESTMENT IN ANY SECURITIES DESCRIBED HEREIN HAS NOT BEEN APPROVED BY THE SEC OR ANY OTHER REGULATORY AUTHORITY NOR HAS ANY AUTHORITY PASSED UPON OR ENDORSED THE MERITS OF THE OFFERING OR THE ACCURACY OR ADEQUACY OF THE INFORMATION CONTAINED HEREIN. ANY REPRESENTATION TO THE CONTRARY IS A CRIMINAL OFFENSE.

Page 16 of 16