Tech Talks Daily
00:01:28 · Mike Link, Neil C. Hughes
[00:01:28] S1: Yeah. My name is Mike Link. I'm the COO of KamiVision, and we operate a AI vision business, which means AI and cameras. We started probably ten years ago with home security using AI to do, you know, people detection, dog detection, package detection, and that thing grew pretty quickly and then allowed us to build other things. One of which what we'll talk about today is the elder care business where we can detect falls and let families know, staff know when somebody's on the ground in a room, which is pretty cool. And then the other thing which we're building is enterprise security. So, you know, in retail stores, in commercial real estate, using existing cameras to layer AI over to do either security or intelligence, maybe in, like, a grocery store, seeing where people spend the most time, or maybe in a a warehouse to do detections of breaking and entering or maybe inventory management or possibly even, like, warehouse shelving deteriorating and breaking. So, yeah, we we we kinda do it all. [00:02:38] S0: Incredibly cool. And I've gotta ask because I always love diving into the origin story of my guests. You talk about home security, package detection, and retail store security. What was the fur... What was it that first showed you that Eldercare needed a different approach to monitoring safety and and also supporting independence? There's gotta be a story there. Was there a moment that inspired that?
00:03:02 · Mike Link
[00:03:02] S1: Not necessarily. So I never intended to get into this. I actually studied neuroscience and wanted to become a neurologist or, like, a surgeon or something, and ended up in India for a while studying alternative medicine called Ayurveda. And when I came back to The States, I'd always done sales and I'd always had businesses. And I needed some money, so I ended up consulting a different company that was doing AI in the elder care space. And that's how I first learned about the challenge with elder care and that, you know, people fall and nobody finds out about it till, like, three, four hours later often even in a facility. And it was an interesting problem. And then, you know, we had some success there, and then Kami had hit me up, and we're doing something similar. And I thought that it was pretty cool that they were able to do similar technology or build similar technology, but for a fraction of the the price and and modify the technology very quickly because we didn't have any VC or any, you know, outside investor to answer to because the home security business had done so well. We had... I wouldn't say, like, infinite money, but, you know, basically millions of dollars that we can invest and then not have to answer to a VC, which is pretty unique. A lot of the companies in the space, you know, the way that technology typically gets built... And and now it's a little different with this whole AI credits thing, but, you know, a VC or an outside investor will come in, give some money unless the founder has some cash. So in this case, the founder had some cash and was able to build the technology super quickly. And then because nobody else was, you know, knocking on the door for the check, we could have a much lower price than some of our competitors. So the challenge was that, you know, the technology in elder care is very expensive because of the reasons I I just said. And the other challenge was that it was hard to install and actually took a lot of capital from a lot of the communities and facilities upfront to actually install the technology. The other thing was that in home, there was nothing that existed for consumer. So, you know, even though there was a lot of these companies selling into communities and facilities where, like, mom might go to a nursing home, there wasn't any technology for somebody at home.
00:05:27 · Mike Link, Neil C. Hughes
I think the big shift was people wanting to not go into a facility because it's not the nicest thing when you have to leave home and, you know, move all your things, and then you're basically in, like, an adult day care. Mhmm. [00:05:41] S0: Yeah. And and anybody that's got an elderly parent, grandparent, or family member, they will know about fall detection systems, things that could be worn on the wrist or or around the neck. But for... From what your experience here, where do those current traditional fall detection systems, where do they tend to fail in homes and residential care settings?
00:05:41 · Neil C. Hughes, Mike Link
[00:05:41] S0: Yeah. And and anybody that's got an elderly parent, grandparent, or family member, they will know about fall detection systems, things that could be worn on the wrist or or around the neck. But for... From what your experience here, where do those current traditional fall detection systems, where do they tend to fail in homes and residential care settings? [00:06:02] S1: Well, those are definitely good options, but the problem is is if somebody doesn't remember to use them or if they need to be charged, then they're kind of useless. And the idea is that, you know, somebody who's older doesn't have to remember to press something or ask for help in an emergency because, you know, I don't know if you have a mom or dad or had grandparents who maybe needed extra help. But Yeah. I know for sure my grandmother would not remember. You know? In an emergency situation, she's not gonna think, oh, let me press my button for help. You know? She's gonna freak out and be panicking and then be stuck. So, yeah, there's nothing wrong with them. I think it's a good... They were a good start, but I think now the technology has evolved to a point where it's requiring less human assistance from the person in need. And that's the idea is that, you know, the person who needs extra care doesn't need to say anything, doesn't need to do any... They could just exist, which prevents a lot of, like, user error. And then we can see based on how they live, what kind of help they need.
00:06:02 · Mike Link, Neil C. Hughes
[00:06:02] S1: Well, those are definitely good options, but the problem is is if somebody doesn't remember to use them or if they need to be charged, then they're kind of useless. And the idea is that, you know, somebody who's older doesn't have to remember to press something or ask for help in an emergency because, you know, I don't know if you have a mom or dad or had grandparents who maybe needed extra help. But Yeah. I know for sure my grandmother would not remember. You know? In an emergency situation, she's not gonna think, oh, let me press my button for help. You know? She's gonna freak out and be panicking and then be stuck. So, yeah, there's nothing wrong with them. I think it's a good... They were a good start, but I think now the technology has evolved to a point where it's requiring less human assistance from the person in need. And that's the idea is that, you know, the person who needs extra care doesn't need to say anything, doesn't need to do any... They could just exist, which prevents a lot of, like, user error. And then we can see based on how they live, what kind of help they need. [00:07:03] S0: And, of course, KamiVision cites 99.5% accuracy, which is incredibly impressive. But how is that measured, and what should buyers ask before trusting any accuracy claim in a a real care environment?
00:07:03 · Neil C. Hughes, Mike Link
[00:07:03] S0: And, of course, KamiVision cites 99.5% accuracy, which is incredibly impressive. But how is that measured, and what should buyers ask before trusting any accuracy claim in a a real care environment? [00:07:17] S1: Yeah. That's a good question. So the 99% comes from the three layer detection system. So we have an edge chip on the device that detects the fall itself. So there's AI on the chip in the sensor. So that's the first layer. The second layer is after it is detected on the device itself, it goes to the cloud. We have an AI model in the cloud that'll filter out false positives. And then we have a third layer, which is actually a human layer, and it's... The the the clip of a fall is blurred and encrypted. And a human can then see just the blurred clip, no personal identifiers or anything like this, then say yes or no. This is or isn't a fall. And because the cloud filter and the edge chip get rid of a lot of the false positives, by the time it gets to the human, there's only a handful left, and then they could say, okay. You know, this is or isn't a a real notification. By the time it gets to the user, it's 99.5% accurate. [00:08:22] S0: And as it's... We're talking about cloud and connections and things, and a lot of elderly people won't have an Internet connection. Is it SIM related? How's that... How is that connection managed?