CollPlant Biotechnologies Ltd. (CLGN) Earnings Call Transcript & Summary

Now we'll begin with Eran Rotem, Deputy CEO and Chief Financial Officer, who will provide and a brief overview of the transaction. Then Dr. Ruti Ben Shlomi, Lightsolver's Co-Founder and CEO, will take you through the technology and the business. We will then address questions that were submitted in advance. [Operator Instructions] Before we begin, please note the disclaimer on this slide. Today's remarks include forward-looking statements, including statements about Lightsolver technology and product road map, commercialization plans, customer activity market opportunities, expected performance, future financial objectives. These statements reflect current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially. Please refer to conference's SEC filings for a discussion of these risks and for additional information regarding the transaction. We do not undertake to update forward-looking statements, expect as required by law. And now I will hand over to Eran. Eran, please begin.

Good morning, everyone. The recent acquisition of Lightsolver makes a pivotal moment for CollPlant, representing our strategic entry into the high-performance computing HPC sector, a significant new growth engine for the company. Our rationale is centered on 3 key pillars: First, the opportunity. We are entering a massive market projected to reach nearly $90 billion by 2034, with applications across critical high-value industries. Second, the technology. This is not a concept. Lightsolver has a proven working system that have already achieved a real-world validation with a global leader like Boeing. And third, the path to scale. This is where CollPlant creates decensive value. We provide a robust public company platform, the access to capital and the experienced leadership to accelerate Lightsolver's journey to commercialization. For our shareholders, this creates a direct path to participate in what we believe will become a disruptive new computing platform. Crucially, we also structured the deal itself to ensure that this participation is tied directly to execution and value creation. This leads me to the transaction structure, which was designed to be as strategic as the acquisition itself. Our core principle was to align all parties toward one goal, creating tangible value through execution. The deal is performance driven, while the upfront consideration was equity-based, the most significant component of the potential ownership is tied directly to the performance through milestone-based warrants. This structure is highly beneficial for our existing shareholders. It provides immediate participation in Lightsolver's potential while ensuring that future dilution is linked to the achievement of significant technology and commercial milestones. In short, as Lightsolver proves its value, it earns its equity. To power this next stage, CollPlant has also invested $5 million into Lightsolver to fuel its operational and development needs. Finally, as we advance this new strategic direction, we continue to actively evaluate alternatives for our biotech assets to maximize value for our shareholders from all parts of the business. With that, I will turn it over to Ruti, who will take you deeper into the Lightsolver technology itself. Ruti?

Thank you, Eran, and thanks, everyone, for joining us today. I'm Ruti Ben Shlomi, Co-Founder and CEO of Lightsolver. My background is in quantum physics. For nearly 20 years, I worked with cold atoms and trapped ion systems, 2 of the leading physical platform being developed for Quantum computing today. I also spent a few years at Intel as a process engineer working at the cutting edge of semiconductor manufacturing and helping build advanced silicon [indiscernible]. So I had the opportunity to experience 2 very different frontiers of computing, silicon and quantum. I thought there are exceptional capabilities, but also their fundamental challenges. Computing is increasingly becoming an energy problem. Billions are being invested in better chip, quantum computers, nuclear power, even data centers and space. Today, the world is investing enormous resources in 1 question. How do we dramatically -- how do we get dramatically more computing power without consuming dramatically more energy? So our question was, what if we could compute with light itself. So together with Han, Chief Scientist and Co-Founder, we invented a fundamentally different computing paradigm in which the core computation happens internally in the optical domain. Instead of using billions of electronic transistors, it uses the physics of interacting lasers to perform computation directly with light. And because these lasers interact simultaneously, the system can explore real physical effect. That is the basic idea behind Lightsolver, and that's how our journey began. So I'm happy to introduce you the LPU laser processing units. The LPU gives us massive parallelism, up to 1 million variables per core with optical dynamics operating on a few nanosecond time scale. Because the computation of state remains inside the optical system, we minimize the constant movement of data between compute and memory with an equivalent internal bandwidth of approximately 200 terabytes per second. The result is the potential to compute targeted workload up to 10,000x faster while operating at room temperature in rec scale system and consuming less than 1 kilowatt. In simple terms, we use light to achieve more computation faster with dramatically less energy and infrastructure. So now let's switch gears and let me show you how it works. Let's start with an example of a computational problem, which is called image generation. So we know how to translate the mathematical problem into a set of parameters that our optical system can work with. These parameters are then embedded into the laser array. In fact, we are programming the physical system to represent the problem we wanted to solve. The computation is performed directly by the physics after interacting lasers. We start with a small amount of random buttons, the trend back and forth inside the cavity, which means essentially 2 mills. Now the photons are being amplified while simultaneously interacting with one another, and the system evolves until it reaches a stable state, what we call the steady-state solution. Once the system reached that steady state, the camera takes a snapshot of the laser value. Those values are then translated back into a digital solution that can be used by the customers' existing software environment. So the loop is very simple. We translate the problem into light. We let the physics perform the computation and translate the result back into digital form. From the user's perspective, you don't need to know anything about lasers. You walk through the software interface, and we handle the translation into and out of the optical system. And this is what excited us from the beginning. Rather than calculating every step, we set up the physical condition and let the system settle naturally into the answer. So we started with a scientific breakthrough back in 2022. We demonstrated for the first time that interacting laser could solve their computational problem using only the physics of flight. Now 1 year later in 2023, we show that the approach could sell to larger and more complex problem. Since then, we have moved from those early scientific demonstration to a working prototype in the lab and from there towards the next generation of the LPU. The progression is important. This is no longer just a physics experience. We are taking a scientific breakthrough and churning it in the product that can ultimately be deployed commercially. So where are we focusing first? Today, we are focused on 3 broad areas: optimization and large-scale simulation. We are also exploring capabilities in AI and physical AI. These are the areas where we currently see the strongest combination of technical fit, customer need and commercial opportunity. Very different real-world problems can ultimately reduce to similar types of computation that we can map on to the LPU. In optimization, that can mean finding the best solution among a very large number of possibilities and constraints in areas such as finance, manufacturing and logistics. In simulation, the range of applications is very broad, designing an aircraft focusing the weather modeling heat flow in a semiconductor. Physical AI is a particularly exciting area for us as robots, autonomous vehicles and drones became more capable, they need to model, predict and respond the physical world around them, creating a very demanding set of compute problems. This workload address a very large market. The overall HPC market is projected to reach approximately $89 billion by 2034. Within that, even if you take only differential equation workloads, which are the equation used to model physical system, it represents more than 60% of the HPC workloads. This translates into serviceable market of approximately $53 billion by 2034. And importantly, these numbers don't even include AI. AI and physical AI represent an additional opportunity on top of what you see here. So we have already run POCs across many of the industries we just discussed. Working on real customers' problems will conventional computing has become a bottleneck. What you see here are just a few examples. Across these projects, we've demonstrated significant improvement in speed, accuracy and solution quality across very different types of workloads. And now we are starting to run that technical validation into paid customer programs. Several customers are already moving from POC into paid pilots and analysts, and we expect more to follow. This is an important step for us. These paid program allows us to go deeper with our customers, welcome real-world application and ultimately move those relationship towards model commercial deployments. Now we can share the names of most of our customers we work with, but Boeing is one we can talk about publicly. Boeing asked us to work on corrosion because it is a major economic and operational problem in aerospace and defense. Corrosion related costs run into tens of billions of dollars. An industry study suggests that a meaningful share on the order of tens of percent can potentially be avoided through better corrosion management. It affects maintenance, health craft availability, material selection, structural weight, fuel consumption and ultimately, CO2 emission. The challenge is that high fidelity corrosion simulation is extremely computational intensive as the physical structure gets larger as you move to final resolution or from 2D to 3D, and as you add chemistry, material behavior, temperature and other physical effect. The number of variables grows very quickly. This is exactly the kind of problems, the LPU -- where the LPU architecture can have an advantage because its compute time is relatively insensitive to increasing problem size. As the model gets larger, conventional compute become much more demanding while the LPU scales much more efficiently. And now we work with Boeing that translate into up to 10,000x faster while maintaining the required engineering accuracy. The practical goal is to make much larger and more realistic erosion simulations. So engineers can better understand how the gradation develops across an aircraft structure and how it may affect the structure of all time. So in this -- and this is exactly the kind of problems Lightsolver was built for, a computational bottleneck where faster simulation can translate into better engineering decision and significant economic impact. Boeing has signed a multiyear NRA agreement with us to continue developing this work and move it towards a real engineering application. This is the road map for taking Lightsolver from the technology and customer validation you've seen today to commercial scale. There are 2 trucks progressing together. On the product side, we are moving from the current lab platform towards a product-ready 100,000 variables laser system, followed by a standard commercial LPU at approximately 1 million variables. From there, the focus shifts to production scaling, orchestration of multiple LPUs enterprise, clustering and mass deployment. So in parallel, our customer programs are helping us build around real-world applications. Paid NRs and technical PLCs allow us to work closely with customers on difficult problems and develop the software and application capabilities around those workloads. The next steps are deeper integration into simulation tools and over time, border access through cloud partnerships. The key point is that product readiness and customer validation are advancing together. That is how we move from successful individual programs towards a reputable commercial platform at scale. Now the category we belong to is photonic computing, using light to perform computation. A lot of activity in Photonics today is focused on moving data faster between electronic processors. There are also companies using photonics inside the processor itself. But those systems typically still combine optical computation with electronics, somewhere in the compute group. Our approach is different because the coal iterative computation remains optical and we keep the loop closed in the optical domain rather than repeatedly moving back and forth into the electronics. Quantum is a different comparison. The LPU is not a quantum computer. Some of the computational methods we use particularly in optimization are inspired by the approaches developed in quantum computing, which is why we sometimes describe aspects of the approach as Quantum inspired. But the machine itself uses classical lights, operate on temperature and does not require cubic or the specialized operating environment of many quantum systems. And importantly, our road map is to bring the LPO commercial availability in 2028. So we think of Lightsolver as a photonic computing system that combined combines in all optical compute group with a practical path to deployment in high-performance computing. One important thing to understand about Lightsolver is that we are now building an optical component. We are building the old computing system. The all optical core is central, but that alone is not enough. We have developed the know-how to control and orchestrate the optical system and the software that takes customers mathematical problem and translate it into something that laser can compute. On top of it, we are building the algorithm and application libraries for areas such as optimization and simulation. And through our customer programs, we are accumulating the know-how to apply the LPU to real problems across different industries. We have developed both the hardware and software ourselves, supported by proprietary IP and patent across both. So Eran and I both trained at the Whitman Institute. Eran specializes in lasers, optics and photonic computing. And his research helped from the scientific basics for the architecture, we are commercializing today. Our team includes PHDs in physics, mathematics, optics, software and engineering. That scientific debt has enabled us to take the technology from the lab into a working computing system while also publishing the underlying method in scientific venues. So we have also received a significant grant sport including from the Israel Innovation Authority and the European Innovation Council. And Gartner has named Lightsolver as a sample vendor in tonic computing in its hype cycle for computing technologies and data centers infrastructure. These are important external recognition points showing that the technology has attracted attention and support well beyond the company itself. What's new now is the combination with CollPlant. Lightsolver brings the technology, the scientific and engineering leadership, a growing commercial business and strong customer relationship, while CollPlant brings additional financial, legal, belnance and public company expertise. We are very happy to be here, and I want to thank ran and the CollPlant team for the very warm welcome. We believe this combination gives us a stronger platform as we move toward productization and commercial scale. With that, I will hand it back to Eran.

Thank you, Ruti. We are thrilled to join forces with you, [indiscernible] and the extraordinary team at Lightsolver. When you combine breakthrough deep tech with the operational platform to scale it, the investment opportunity becomes clear. It rest on 5 key pillars first. We are providing the direct exposure to a high -- to a huge growth market. as conventional computing hits a wall. Our all optical computing approach offers a fundamental solution addressing the thermal and power limits of silicon. Second, this technology is validated beyond the lab. We have active paid pilot programs with till 1 global leaders like Boeing, proving the technology's value on real-world problems today. Third, we are targeting massive workloads. Our systems are tailored to solve critical optimization and simulation bottlenecks across a market ejected to reach nearly $89 billion by 2034. Fourth, this is all driven by elite execution and leadership. We have combined pioneering physics with the Whitman Institute with Season NASDAQ executives who know how to build and scale public companies. And finally, significant value creation milestones are still ahead. productization, customer conversion and commercial scale provides multiple opportunities to create value as like solver executes its road map. In closing, we have a validated technology in a massive market and the right team to execute. This is why we are so excited about the future and why we believe the most significant value creation is still ahead.

[Operator Instructions] First question, what is the business model? Hardware sales, recurring software revenue, cloud use or some combination?

So basically, LPU is a full stack system. So the commercial model is expected to include both the computing platform and proprietary software and application capabilities around it. Our initial focus will be to deploy in HPC centers, data centers, entries and with broader access, cloud service over time. The precise commercial packaging and pricing will evolve as we move from early programs and POCs into a product.