Brad Sorensen, CFA: Hello, everyone. I’m Brad Sorensen. I’m a Senior Analyst with Zacks Small Cap Research, and I’m thrilled to be joined by Jack from Ainos (NASDAQ: AIMD), which has a lot of exciting things going on with AI Nose, and that sounds like an interesting title. I was very intrigued by it when I saw it. Scent technology is kind of a new thing to me, but once I dug into it, it was extremely interesting, and it’s cutting-edge. It uses AI and a lot of other technology as well. I really want Jack to explain what’s going on. For investors who are new to Ainos, how would you describe the company to them, to new investors, and what problem is it trying to solve?
Jack Lu: Thanks, Brad. So, our company, Ainos, is digitizing smells and basically teaching AI systems how to understand them. Simply put, we’re building scent into a new data layer for AI. We call this Smell AI. The analogy I often use is video. Remember when cameras, connectivity, storage, and machine learning all came together, and visual information became machine-readable and useful as a skill for AIs. If you look at the last decade, global mobile video traffic grew more than fiftyfold — that’s five zero — in ten years. Video became much more than entertainment; it became an input for computer vision, robotics, industrial automation, and autonomous systems. So we think smell might be at the earliest stage of that same trajectory.
There’s a lot of valuable information in the air around us. It can signal a lot of things, such as contamination, process drift, equipment problems, or even safety risk. But most of that information today is still effectively analog. AI sees and hears these days, but it generally doesn’t understand what is happening chemically in the environment around it. So I’ll give you a real example. That’s about March this year. Newark Airport had a major incident. A burning smell led to the control tower being evacuated, and then all flights were temporarily grounded. It shows you that smell can be an early warning signal before you see it. But today, that’s still largely dependent on a person catching it or deciding what to do. So that’s a problem AINOS is trying to solve. Our product converts scent patterns into data, and then we call those data points Smell IDs. They allow systems to recognize patterns, compare them over time, and identify what may need attention.
Our technology was initially built for medical diagnostics, and then we later identified a much larger opportunity in industrial applications. Fast forward to today, right now, we are focusing on deploying Smell AI into semiconductor fabs, robotics, and even healthcare infrastructures. In these areas, small changes in the air chemistry can affect operations big time. Why now? Really, it’s because all the technology stacks are coming together. Hardware is becoming easier to deploy, AI is becoming more practical, and industrial customers are looking for new data sources that can help them improve automation and reliability. We like to say this is potentially the ChatGPT moment for Smell AI. That’s where we are.
BS: I think digging in a little deeper before we get into the commercial side of it, I think investors need to understand a little more. I think people are thinking there are already sensors — we have gas sensors and all that kind of technology. What differentiates this approach, the AI Nose, from those? How is it better? Why is it more useful?
JL: That’s a fair question. Gas sensors and electronic nose technologies have been around for a long time. What makes us special or different is how we are combining four things: ultra-sensitive sensing, small portable hardware, trainable AI, and shared data. First, on sensitivity. Our product is designed to detect changes in the air at parts-per-billion levels, the PPB level. In layman’s terms, it can pick up very small changes in the scent that people may not notice.
Number two, portability. Our device, the AI Nose module, is roughly the size of an iPhone Pro Max. That means our customers can place it where they need the visibility the most, visibility to the scent, I mean. It could be next to the equipment, along the production lines, in the clean room, or on a robot. If you imagine right now, one fixed gas monitor can only tell you what is happening in one spot. But with a network of our devices, you can do continuous sensing across your facilities.
Number three, trainability. Traditional gas sensing technologies are often designed to detect one specific gas or trigger at a fixed threshold. Our AI Nose is designed to learn what normal looks like in an environment, and then over time, you can better distinguish routine variations from meaningful change. So, you can tell what’s normal from what’s abnormal, and you can even set different types of abnormalities.
And then the last piece is the shared intelligence. Our product, the AI Nose hardware, collects the signal, and then our AI — it’s called the Smell Language Model, or SLM for short — interprets that signal, turns it into Smell IDs, which are structured data that can be stored, compared, and reused by machines and AIs. Now, because all the systems can be connected, the beauty is that if one system picks up new knowledge or a new scent, the other system can draw that same knowledge. The entire thing makes it easy to scale the knowledge across facilities or across multiple sites. We call that the Smell Intelligence Network.
So instead of an isolated sensor producing isolated readings, our customers, with our products, can see patterns across locations and analyze over different time horizons. That helps them narrow down the source of the problems and then assess whether it might be spreading before it becomes a bigger issue. The difference is not just a more sensitive sensor. Our product is a continuous, smart, trainable nose system that turns environmental signals into intelligence in our customers’ settings.
BS: Yeah, and that’s hugely important. It’s obvious that this is a big upgrade from what has been used in the past. But we are talking to investors, and they’re always going to want to know — that sounds great, but what has it done commercially so far? Where have you put this into practice? How are businesses reacting to this? Give some color on what businesses have shown interest in this and actually use this technology.
JL: Our first, most meaningful commercial attraction today is actually in the manufacturing sector, more specifically, chip manufacturing. There are two processes in chip manufacturing. There’s a front-end part and a back-end part. Generally, you can think of guys like Intel, Micron, TSMC — they are the front-end part. The back-end part is where they turn those chips into packages, into modules, and make sure they test them and everything works properly. Right now, we have traction on both ends.
On the back-end part, we already secured a three-year contract for about 1,400 of our systems with one of the biggest players in that field. The initial contract value is approximately $2.1 million. Now, because we are offering the product as a subscription-based service, that’s $2.1 million over the three-year horizon. This is the most important part — this is our first order. It moves us into commercial deployment. Our goal for this particular project is to start generating revenue from this program in the back half of this year, as the rollout progresses. And within this customer, there’s a broader expansion opportunity. Subject to further validation or commercial agreements, this could potentially scale up to a much larger number of systems across this customer’s facilities. So that’s on the back-end side, our first commercial traction.
On the front-end side, the chip fabs, we are now advancing validations in some wafer fabs. The focus is on monitoring air conditions in high-risk areas, identifying unusual changes early, and adding visibility around toxic or irritating gases. Outside of the chip manufacturing sector, we are also expanding into healthcare infrastructure and robotics. We have multiple programs going on in Taiwan right now.
In healthcare, we have some early-stage programs focused on hospital infrastructure safety, department environmental monitoring, and patient-level breath intelligence for emergency care. For robots, we are now working with robotics customers to integrate our AI Nose into their mobile robot and robot dog platforms. The goal is to give these robots another layer of awareness beyond vision, cameras, LiDAR, and other conventional sensors. The common thing among all of this is that we are aiming for applications where the platform can become part of a recurring workflow, rather than a one-and-done transaction.
BS: Right, that’s incredible. It sounds like you’re getting in on the ground floor of an industry, especially semiconductors and healthcare, both of which are exploding at present. How do you see expanding even further? And what should investors really watch for over the next quarters and year to see if you guys are executing on what you think you should be?
JL: Our Smell AI technology, powered by this platform, was originally developed for healthcare, but we always believed this same platform could extend across multiple industries, and as you can see, that’s what’s happening. The chip factories are our top priority at the moment because we’re already building commercial traction there. And then beyond the chip fabs, the logical expansions are healthcare infrastructure and robots, which we talked about.
In healthcare, we’re applying this platform across three pilot programs in Taiwan. One is hospital infrastructure — the product will go into power, electromechanical systems, gas infrastructure, and clinical laboratories in the hospitals, to help make sure the overall environment is safe, monitoring the environment. The second program is targeting the ER department, studying how AI Nose can serve as an early warning system for emergency department overcrowding and respiratory infection risk. Number three, there’s a research program on patient-level breath intelligence. What that means is the program will study how AI Nose can help support emergency care triage by identifying breath Smell ID patterns related to shortness of breath and related clinical conditions. These programs actually reflect where the AI Nose technology began. This technology was first developed to analyze human-emitted VOC patterns for healthcare applications as medical diagnostics.
Robots are another important area. We are helping them gain another layer of environmental awareness because a lot of these robots are going into factories for automation. As you can see, all our use cases are tied together. The key point is we’re not building a separate product for every industry. We are using the same core Smell AI platform across different applications.
For investors going forward, the main things to watch for include: number one, can we turn our semiconductor deployments into broader scale and convert that into revenue? As we said, there’s a program now that we are expecting to turn into revenue as soon as the back half of this year. Number two, whether our programs within healthcare and robotics move from early validations and pilots into commercial orders. And number three, can our base grow?
A lot of our Smell AI has a flywheel effect: the more it deploys, the more data we can use to train our SLM, the Smell ID gets better, and then that cycle repeats. So deployment is key to helping us strengthen that Smell ID dataset and improve our models. Longer term, we also see potential in consumer-facing applications that could become part of daily life, but that’s further out; we’ve done some early work there.
Overall, the strategy is focused on a few high-value markets where scent intelligence can solve real problems, create commercial value, and strengthen our data model over time. We picked all activities in Asia because it makes sense. Asia is the biggest cluster for chip manufacturing, automation, robotics, and healthcare.
BS: It’s an exciting future. This is an opportunity to get in on an AI company that is really just starting to expand and take off, and you can see that they have a market going forward. The potential is enormous here when you look at the number of applications. I think Jack’s just really scratching the surface of what this could ultimately do, and I think that’s already quite large. I’m really intrigued by this company, and I think it has a great future. Jack, I want to thank you for taking the time to explain the exciting technology you guys have and let investors understand the opportunity.
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