 From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE, covering when IoT met AI, the intelligence of things. Brought to you by Western Digital. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel at a small event talking about data and really IoT and the intersection of all those things. And we're excited to have a little startup boutique and one of the startups is great enough to take the time to sit down on this. It's Sean Moore, he's the founder and CEO of the recently renamed TrueFace.ai. Sean, welcome. Thank you for having me. So you've got a really cool company, TrueFace.ai, looked at the site, you got facial recognition software. So that's cool, but I think what's really more interesting is you're really doing facial recognition as a service. Yes. And you have a freemium model so I can go in, connect to your API and basically integrate your facial recognition software into whatever application that I build. Right, so when we were thinking about what we wanted to do in terms of pricing structure, we wanted to focus on the developer community. So we wanted tinkers, people who just liked to play with technology to help us improve it and then go after kind of the bigger clients. And so we'll be hosting hackathons. We just actually had one this past weekend in San Francisco, had great feedback. We were on product hunt. We're really trying to get a base of almost outsourced engineers to help us improve this technology. And so we have to offer it to them for free and so we can see what they build from there. Right, but you don't have an open source component yet so you haven't gone that route. Not quite yet. Okay. We're thinking about that though. And still really young company, AngelFundit, haven't taken an institutional round yet. Right, yeah, we've been around since 2013, end of 2013, early 2014, and we were building smart home hardware. So we had built the technology originally to be a smart doorbell that used facial recognition and customized the smart home. From there, the trajectory went, we realized our clients were using it more for security purposes and access control, not necessarily personalization. We made a quick pivot to a commercial access control company and continued to learn about how people were using facial recognition in practice and could it be a commercial technology that people were comfortable with? Right. And throughout that thought process and going through and testing a bunch of other facial recognition technologies, we realized we could actually build our own platform and reach a larger audience with it and essentially be the core technology of a lot cooler and more innovative products. Right, and not get into the hardware business of doorbells. Yeah, the hardware business is tough. That's a tough one. We went through a manufacturing round and I'm glad we don't have to do that. So what are some of the cool ways that people are using facial recognition that maybe we would have never thought about? Sure, so for face matching, so that the API is four components. It's face matching, face detection, face identification, and what we call spoof detection. Face matching is what it sounds like, one-to-one matching. Face detection is just detecting that someone is in the frame. Face identification is your one-to-x, your one-to-a-database of people. And the spoof detection is, if someone holds up a picture of me or of you and tries to get in, we'll identify that as an attack attempt. And that's kind of where we differentiate our technology for most, is not a lot of technology out there can do that piece. And so we've packaged that all up into essentially the API for all these developers to use and some of the different ideas that people have come up with for us have been for banking login. So for ATMs, you walk up to an ATM, you put your card in instead of a pin, so to prevent against fraud, it actually scans your face and does a one-to-one match for chip industries. So for things like cruise ships, when people get off and then come back on, instead of having them show ID, it's a quick facial recognition scan. So we're seeing a lot of different ideas. One of the more funny ones is based off of a company out of LA that's doing probation monitoring for drunk drivers. And so we've built out technology that's drunk or not drunk. Drunk or not drunk. Right, so we can actually measure based on historical data if your face appears to be drunk. And so the possibilities are truly endless. And that's why I said we went after the development community first because they're coming to us with these creative ideas. Now it's interesting that drunk versus not drunk, not to make fun of drunk driving is not a funny subject. But obviously you've got an algorithm that determines, I suppose there's anchor points on the eyes and the nose and certain biometric features. But drunk is you're looking for much softer, more subtle clues I would imagine because the fundamental structure of your face hasn't changed. It doesn't change, right. So it's a lot of training data. So it's a lot of training data. A lot of training data, well. We don't want to go down that path. It's a lot of research on our team's part. Well, and then the other thing too is the picture, is the fraud attempt. You must be looking around and shadowing and really more kind of 3D types of things to look over something as simple as just holding up a 2D picture. Right, so a lot of the technology that's tried to do it, that's tried to protect against picture attacks has done so with extra hardware, extra sensors. We're actually all cloud based right now. So it is in our software. And that is what is special to us is that picture attack detection. But we've got a very, very intelligent way to do it. Everything's powered by deep learning. And so we're constantly understanding the surroundings, the context. Right, right. And making an analysis of that. So I'm curious from the data side, obviously you're pulling in kind of your anchor data and then for doing comparisons, but are you constantly updating that data? I mean, what's kind of your data flow look like in terms of your algorithms? Are you constantly training them and adjusting those algorithms? And how does that work kind of based on real time data versus kind of your historical data? So we have to continue to innovate and that is how we do it, is we continue to train. Every single time someone shows up, we train their profile once more. And so if you decide to grow a beard, you're not going to grow a beard one day, right? It's going to take you a week, two weeks, we're learning throughout those two weeks. And so it's just a way for us to continue to get more data for us, but also to ensure that we are identifying you properly. Right, right. Do you use any external databases that you pull in as some type of adding more detail or kind of other public sources or is this all your own? It's all our own right now. And I'm curious too on the kind of opening it up to the developer community, how has that kind of shaped your product roadmap and your product development? We've got to be very, very conscious of not getting sidetracked because we get to hear cool ideas about what we could do, but we've got our core focus of building this API for more people to use. So we continue to reach out to them and ask for help and if they find a flaw, if they find something cool that we want to continue to improve, we'll keep working on that. So I think it's more of a, we're finding the developer community really likes to tinker into play and because they're doing it out of passion, it helps us drive our product. Right, right. Okay, cool. So priorities for the rest of the year, kind of what's at the top of the list? We'll be doing a bigger rollout with a couple partners later on this year and those will be kind of our flagship partners. But again, like I said, we want to continue to support those development communities so we'll be hosting a lot of hackathons and just really pushing the name out there. So we launched our product yesterday. We're on the leaderboard all day and that helped generate some awareness but we're going to continue to have to get the brand out there as it's now one day old. Right, right. Well, good. I look forward to it. It was chewy before and now it's trueface.a. So we look forward to keeping an eye on your progress and congratulations on where you've gotten to date. Thank you very much. I appreciate it. Absolutely. All right, Sean Moore, it's trueface.ai. Look at the camera smile. They'll know it's you. You're watching Jeff Frick. Here's theCUBE in downtown San Jose at the when IoT met AI, the intelligence of things. Thanks for watching. We'll be right back with a short break.