Coming up next is Ainos, ticker AIMD on the Nasdaq. Presenting today, Jack Lu, Head of Corporate Development. There you are, Jack. How are you?
Good day. Doing well, [Craig]. Good to see you again.
Good to see you again. I suppose you'll be using your presentation today.
Yes, I hope.
If so, let's go ahead and display it while I get some preliminaries out of the way. Once again, we love your questions. Submit them by pressing the Q&A button at the bottom of your Zoom window and typing into the text box. We'll keep your lines muted throughout the presentation. Here is the safe harbor statement. This segment may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements pertaining to future financial and/or operating results, along with other statements about the future expectations, beliefs, goals, plans, or prospects expressed by management, constitute forward-looking statements. Any statements that are not historical facts should also be considered forward-looking statements, and o f course, forward-looking statements involve risks and uncertainties. Jack, please go right ahead.
All right. Good morning, everyone. My name is Jack Lu. I'm Head of Corporate Development at Ainos, stock symbol AIMD. Thank you for joining us today. We, Ainos, we are building what we believe is a important missing layer of the physical AI, that is the sense of smell. Today's AI can read, can see, can hear, and can reason, but AI still struggles to understand the chemical information that surrounds us every day. Our mission is pretty simple. We're teaching AI to smell by digitizing scent and then transforming scent into machine-readable data. Today, I'll explain why we believe smell can become a new AI data layer, how our AI Nose system works, and how we are commercializing the platform across semiconductor manufacturing, healthcare infrastructure, and other real-world environments. All right, let's dive right into it. One minute. Okay. This is our own version of disclosure statement.
Just going to leave here for a while. With that covered, let's start with a simple question. Why does smell matters for AI? Every major AI breakthrough started with a new data layer. Large language models needed text, computer vision needed images, voice AI needed audio. Once AI could understand these formats, entirely new industries emerged. But there's always one major source of information remains largely inaccessible to AI today, t hat is smell. We often explain the opportunity using the computer vision analogy. If you recall, camera existed for decades before computer vision emerged, and that breakthrough really happened when the images get digitized.
Once the images become data, AI could learn from them, and that shift created all kinds of new applications that are so embedded into our everyday lives today, ranging from having the webinar like we're doing right now, watching YouTube videos, watching NBA finals on your phones, self-driving cars, to medical imagings. We believe scent may be approaching a similar moment. The world constantly generates scent informations. Machines, so far, simply don't know how to interpret it yet. Equipment failures, gas leaks, overheating cables, environmental changes, health conditions, m any of these signals appear first in the air. So, the question becomes: h ow do you turn scent into data? And that's exactly what AI Nose, our product, is designed to do. Our product converts scent into machine-readable intelligence in four main steps: o ur hardware captures the scent signals, w e convert those scent signals into Smell ID data, o ur AI model, which we call the Smell Language Model, learns from that data, and then we deploy that intelligence into real-world environments.
The important part isn't just the hardware. The key point is the infrastructure. Just as AI requires computing infrastructure and data infrastructure, we believe Smell AI will require scent infrastructure. Ainos serves as the foundation of that infrastructure. We transform real-world scent signals into machine-readable data that AI system can understand and use. That brings us to the business model behind the platform. This slide really is the heart of our business model. Every deployment generates Smell ID data, and that data improves our Smell Language Model. A better model creates more customer value. More value drives more deployment, and then the cycle repeats and our revenue grows. Unlike text or images, scent data cannot simply be scrapped from the internet.
Someone has to collect it in the real world. That's why we believe early deployment creates an important advantage to us. Over time, our Smell ID data set can become a very meaningful competitive moat, and our customer stickiness will build. The technology matters, but the data moat matters even more. The next question becomes: where do we deploy first? This is the AI Nose system. The device combines multi-sensor detections, cloud connectivity, AI analytics in a very compact, deployment-ready device. We use sensor chips from leading companies, so making them important ecosystem partners of ours. We focus on integration, calibration, signal processing, scent digitization, and AI interpretations. The system is engineered for fully automated, clean, controlled, continuous sensing. We optimize for factors such as temperature, humidity, airflow, environmental stability, which are critical for reliable operations in the industrial environments.
The platform is also designed to be very sensitive. In many applications, we can detect compounds down to parts per billion levels, enabling early detection of very subtle environmental changes that could signal equipment problems, contamination risk, or other operational anomalies. The device is very small, roughly size of a smartphone, easy to deploy. Our goal is that our customers can place multiple AI Nose systems throughout their facility, creating a so-called smell map that helps them to detect and locate potential problems earlier. Unlike the traditional sensing systems, our product is trainable so that our customers can continuously teach the platform to learn new scent patterns and environmental conditions. In short, the AI Nose connects the physical environments to our Smell AI infrastructure. Building the technology is only part of the story. The next question is where we apply it.
Roughly 13 years ago, our team began developing AI Nose for healthcare and medical device applications. That's one of the most demanding environments for sensing technologies, where really accuracy, reliability, validations are all key. That journey helped us build the foundation of our Smell AI platform, including the ID data sets, the Smell Language Model, and the expertise required to digitize scent in the real world. Today, we are applying that same platform across semiconductor manufacturing, healthcare infrastructure, and other robotics and other industrial environments. Different industries, but same one Smell AI platform. Healthcare gave us the foundation, then commercialization gave us the directions, and that led us to semiconductor manufacturing, our first major industrial market. Where do we start? We start where scent already matters. That's manufacturing, and we start within chip factories. Chip factories use hundreds of specialty chemicals and gases every day.
Tiny little changes in the environment can create big operational consequences. The customers, these fab companies, chip makers, they already invest heavily into monitoring and process control, but we always believe scent intelligence can become another valuable layer of their environmental information. Chip manufacturing also operates 24 hours a day, so that creates an environment for continuous real-world deployment. From our perspective, it's a great place to begin commercialization, and that's exactly where we secured our first commercial order. This is where the story moves from lab to executions. Our first customer is one of the world's leading semiconductor packaging and testing companies. Together, we are now establishing a multi-phase deployment roadmap. The initial phase includes roughly 1,400 AI Nose systems under a three-year subscription structures. Over time, the roadmap would potentially lead to scale significantly beyond initial deployment as we continue to execute.
This is a recurring deployment model, so every deployment unit generates revenues. At the same time, every deployment expands our footprint. That's why we view this deployment as a very important milestone. This first commercial contract validates the opportunities. Our next step is ecosystem expansion. We are first building our partner networks in Asia because Asia accounts for roughly 70%, that is seven zero percent of global semiconductor manufacturing capacity. Our ecosystem partners each play a unique role. Some help us expand customer base and market reach. Topco here is a good example. They expand our commercial reach through its customer relationship and industry network. Together, we are exploring opportunities across multiple industrial environments, including the front-end semiconductor fabs. These projects will help us grow our revenues and expand the network, and o ur ecosystem doesn't really stop here.
Since last year, we have continuously expanded strategic partnerships across industries. This is one of our other partners, Trusval. They help us reach more customers. They strengthen our access within the front-end fab infrastructure environments, and the partnership expands our presence in the customers. Their expertise in infrastructures, they are basically the backbone of these semi-fabs, so t heir expertise help us accelerate validation and deployment efforts. Together with our partners, we demonstrate how we are building an ecosystem rather than pursuing isolated pilots. Healthcare build the foundation, the chip fabs commercializing the products, and then the next step is the network and use cases. Another use case is healthcare infrastructure, and healthcare infrastructure represents another important deployment environment. Strategically, this slide is about something bigger. It's about building a Smell Intelligence Network.
Every new environment expand the range of real-world scent patterns that our platforms can understand. Chip fabs contribute one type of environmental intelligence. Healthcare contributes another. They all share similar things. They are all very sensitive to small changes in the environment that could be crucial to their operations. Over time, we believe these connected deployment networks can form a growing Smell Intelligence Network, we call that the smell map, across industries. We can now deploy through programs. Within this healthcare infrastructure environment, we're now deploying through programs with two leading Taiwanese medical centers. They will help us support hospital infrastructures, critical facility operations. The programs are expected to generate more than 2,500 hours of environmental scent data so that we can continue to improve our models. At the same time, this platform collects no images, audios, or personal information.
Again, different environment, one platform, expanded networks, and that leads us to the final investment thesis. Let me leave you with four key points. Number one, we believe smell represents a major missing data layer for physical AI. Number two, we spend more than 13 years building the technologies, the Smell IDs, and the expertise needed to digitize scent. Number three, this year, 2026, marks the beginning of commercialization through chip manufacturing deployments and the growing ecosystem of partners, and also our latest new deployments within healthcare infrastructure. Lastly, we are building more than a product. We are actually building an AI infrastructure based on Smell AI, and we are expanding that Smell Intelligence Network across real-world environment.
As the deployment grows, our revenue will grow, the work network would expand, the platform will become more valuable, and we believe that combination will position Ainos at the forefront of a new category of AI perception. Our goal is very simple. We want to build the smell infrastructure for physical AI. We discussed the opportunity, the platforms, and our strategy. To bring it all together, I'd like to leave you a short video on our mission for Smell AI and the future of physical AI. I want to make sure everybody can see my screen, so I need to.
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Stop for a while.
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Yeah, Jack, we are.
Yep.
We are not seeing the video.
Yes. I need to re.
We're hearing it.
I need to reshare my screens.
Okay.
Let me see.
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[Presentation]
Okay. That wraps up my presentation. Thank you for your time today. We appreciate your interest in the company. We look forward to continue to update you our progress. If you're interested, follow our social media. Thank you.
Appreciate it, Jack. Jack, a lot of investors, of course, would be familiar with AI as text, sound, images, things like that. Nose and smell may be a little bit different for them. What is that aha moment that they have that really hooks them?
Yeah. That's a great question. The aha moment usually comes when people realize this, that some real-life events happened in the air before they become visible. I'll give you a classic example. I think that was about March. There was an incident at Newark Airport where the control towers, they needed temporary evacuations because the personnel reported that there was a burning smell within that tower. That smell was really the first indication that something went wrong. What went wrong? Their cables overheated and started to smell weird. This kind of events are something that most people can relate to. A gas leak, an overheating cable, a process issue in the factory often creates a smell signal before you can see it or before machines can see it.
Once our customers understand that these signals can be digitized into data, they really start viewing smell as another important source of intelligence that they can deploy with AI so that they can better understand the physical world.
Excellent, Jack. Thank you very much. That sums up a lot of the kind of questions we've been getting about that. Just to remind everyone, if you want to submit your question, press the Q&A button at the bottom of your Zoom window. A text box will appear, and you can then submit your question as we can only take your written-in questions today. Audience, we ask that you not use the raise hand button. Another question we've got here, Jack. One of the more interesting aspects of your story is the concept of a Smell ID and a Smell Language Model. Over time, do you see the greatest value residing in the hardware, the software, or the proprietary scent data being accumulated across deployments?
Yeah, I think all three layers matter. If you think about our AI Nose platform, there are three layers. The hardware layer, that's the white box I showed you, the AI layer, that's the Smell Language Model, and the data layer that's being collected in the real world, and you can't really get that from the internet. Over the long run, I think the data layer becomes increasingly important. The hardware really is the gateway for the signals to enter the digital world. The software creates the intelligence. You can download video, images, text data from the internet, but you can't scrape scent data. You just got to smell it from the real world. That's why we're focused on building that Smell ID data sets through deployments.
Just like a lot of the LLM these days, the algorithm is the backbone, but the real value is the data that feed into it and the outputted intelligence, and I think we're on the similar trajectory.
Jack, you've chosen semiconductor manufacturing as an early commercial focus. What makes that industry particularly well-suited to demonstrate the value of AI-powered scent intelligence?
Yeah. For us, to build our platforms, we need a lot of data that gets churned out 24/7 so that we can optimize it and create that data flywheel. Chip factory is actually one with the ideal environment for that because they are highly chemical- intensive, and small changes can have meaningful operational consequences. These chip makers, they already invest heavily in the monitoring and processing. They want to control risk before it escalates because they simply cannot afford downtime. We believe scent intelligence can become another valuable layer for them because they already collected all kinds of data, vision data, pressure data, even vibration data. A lot of these fabs are now fully automated, so having that extra layer of information can help them discover the unknowns. They are 24/7 environments, so they are really a perfect place for our product.
Right now, we are working with the biggest back-end part of the supply chains. Through our partnerships, we are now expanding into the front-end part of that chip supply chain, which is one of the highest valuable part.
Jack, are you finding that the core technology transfers well across industries? Or does each new vertical create its own unique opportunity to expand the platform?
As you can see from my earlier slides, the platform really transfers well. Right now, what we are deploying in the chip factories and in the healthcare, in the hospitals, are actually the same product. The changes in the application and the type of environmental intelligence, that's the change, but the concept remains the same. For us, expanding deployment help us expand what the platform can understand. Just like ChatGPT, you start with a simple chat box, you feed it with all kinds of information, and then become domain experts, and then you can do all this agentic work as they are doing today.
Jack, how do you envision olfactory technologies, olfactory intelligence, sorry, changing the capability of autonomous robots over time?
We, the humans, we don't rely on visions alone, right? Eventually, we think robots probably won't either. I think smell can become a very important sensor input that helps robots to better understand their surroundings, detecting things that vision cannot do. Just like people can smell smoke or gas before they see the source, robots may eventually use scent intelligence to detect environmental changes. Actually, right now, we already have some projects with some of the robotic customers in Asia to add that extra sense of smell onto their robotic platforms.
Jack, your healthcare heritage, your very ticker is AIMD. What advantages do they provide in developing a platform capable of addressing such a broad set of markets?
Yeah. Medical is really our root. That's where everything started for us. We spent about roughly 13 years developing and refining AI Nose technology in a very defined environment, because accuracy, reliability, and validation matters for medical devices. That really just leveled up our entire platforms. Now, that foundation is now helping us to expand into chip factories, healthcare infrastructures, and other applications because the concept is the same. They demand accurate, reliable, scalable sensing based on smell.
Final question, Jack. We've got about 30 seconds for you to sneak this one in. How do you think about the eventual size of the smell data economy if digitized scent becomes as commonplace as image and voice data is now?
Yeah. I think it's very early to put an exact number on that opportunity, but conceptually, I would say computer vision 30 years ago versus now, it gets a lot bigger. We've seen this movie happen before. I just saw a data, right? Video now accounts for roughly 75% of the global mobile data traffic. People always take photos, videos. What's changed is they become machine-readable data, and you can send them around and process them. People have always smelled things too. If scent can become a data, it could enable entirely new category of AI applications, like what we are driving today. Our focus today is to build the infrastructure and data sets that could help to make that future possible.
I would say we're at that beginning of that inflection point, and what you're seeing with computer vision now, that's where we eventually will become with smell.
Jack Lu, thank you very much. It's Ainos , Inc, AIMD on the Nasdaq. If you'd like more information about Ainos, write us at aimd@redchip.com or call us at 1-800-RED-CHIP. Jack, great to see you again. Thanks for the excellent presentation.
Thank you. Bye-bye.