The Exchanges

Q this world that I can start asking, um, when is my inventory going to be in? Is my inventory within the temperature range, like the milkshakes or the protein shakes? Is that in the, in the right range, uh, that I could, I could be in? I mean, Where, is that the type of questions that you think, or, or that currently are being asked by people using the platform?

A Absolutely. I, I think there's a macro level and there's kind of a micro level, uh, at the item level. So we'll, we, we could start with the, with the macro level. So this could be as easy as an example of a pallet that has meat and vegetables on it. And it starts as a source at a distribution center and it's packed in. Let's say one of the pallets is meat. One of them is vegetables. They're, they're refrigerated and frozen at different temperatures. As it goes through the transit on the, ah, on the back of the trailer, ends up at the dock door at the back of the store, you have a certain window that that pallet gets unloaded, it can dwell, and then it has to go directly to refrigeration or some level of a freezer. We calculate with our customers what that ideal dwell time is, and we send them proactive triggers via events to let them know if this dwells another five minutes, you can't sell it because it's out of compliance. So that's kind of the proactive way at the, at the macro level, but it can go one step further. It could go through the item level journey that let's say it is a package of meat And then it ends up in the front of the store. Some reason it was moved out of the freezer a couple times outside of that dock door. And it went through some variance changes that you can actually engage with that product and say, can I eat you? Is this safe to eat?

AI assessment note: “Absolutely. I, I think there's a macro level and there's kind of a micro level”

Q You can't really make mistakes here. But then again, something that we encounter often is that when you're dealing with AI systems, they are probabilistic. They are, you know, they're, they're not, you know, if A then B systems. They are systems that tend to freelance a little bit, for lack of a better term. So how can you then trust an AI system to act accurately in these environments?

A It's a great, great question. You have to start with the source of the data. You know, I've, I've been a victim of ChatGPT where I've asked it questions that I knew maybe 60% of the answer, and then I get an answer that's completely off base. And then you look at some of the sources that the data is being pulled from, you say, okay, this is not credible, or this was a opinion article and it pulled the information from there. So tying it back to the real world of, um, asset tracking and what that means from a data source is that you have to have a source of truth behind the data. And validity and trust that that data coming in is factual data, and then you can rely on the AI to do the, the legwork behind it. And, and for us, from a platform perspective, we really pride ourselves that the assets that we're tagging and the data that comes from it, we really synthesize that data in a right format so that when it is sitting in a platform and AI is now layered on top, Providing the insights that you're working off of really clean, decrypted, trusted data at the end of the day.

AI assessment note: “you have to have a source of truth behind the data”

Q Yeah. Okay. So, so obviously the question now is where's the AI in that? I mean, that's, that's data. But how does it go from data to something that's artificial intelligence related?

A Absolutely. So from a, from a platform, uh, level, We pride ourselves on being able to synthesize and decrypt the data as it comes into the platform, so we're a source of truth. All these capabilities that I went through, these are all different data signals, and AI is only as good as data that comes in, so you have some sense of truth that you're mining into and making logic out of. When we have data coming in, we have to unpack that data. So it might be repetitive data, It might be data in a sense of location, temperature, humidity, light. Um, there's variance as items move through supply chain. We take all that data, we ingest it, we make sense of it. Once we uncover it in the platform, then the AI component layers in. So then you can ask it questions, and then you'll know more around your assets and products. So this could be in a predictive format. You have assets that are reusable. They leave your facility. You can ask it, when is it going to return based on trends? And then you'll get the answers. You may ask it simple questions like, is my pallet within compliance? If your compliance levels are between 32 degrees Fahrenheit and 35 degrees Fahrenheit, and any kind of variance outside of that temperature doesn't allow you to sell the item that's located inside of it. We would be able to then tell you, yes, it had a two degree variance outside of the threshold. You can't m…

AI assessment note: “Once we uncover it in the platform, then the AI component layers in.”

Q And it works like a hundred percent of the time? 90?

A Absolutely. For, for us right now, we, we pride ourselves on the readability side, uh, first, so that you're getting the visibility, because if you can get the visibility, and we have a, basically a three-step process. We have a POC, we have a pilot, and we have a deployment. And what we tend to do is through these stages, when we come in with some level of infrastructure, Our intent, the way we qualify deals, is that that, that hardware stays on site. We don't rip and replace it. So the hardware that you use in a POC or pilot is only going to exponentially expand for the volume and the, the scale that that organization is going to go through. So while, while we go through the evaluation process and the use cases, we really pride ourselves that we're making the process really bulletproof, and it's something that has validity You are getting the scans, because if you are getting the scans in the read range, then you are getting the data that's consistent out of it, and then you have the assurance that the AI is going to do what it's supposed to do with, with factual data points.

AI assessment note: “we really pride ourselves that we're making the process really bulletproof”