Driving trust with AI Live from Boston Startup Week at Suffolk University with Parag Shah

At the heart of an industrial revolution is an innovation that changes everything. Building AI Boston sees artificial intelligence as a renaissance. From the heart of innovation and the mecha of tech learning, we bring you AI for real people, a conversation for everyone. Hello, welcome to Building AI Boston, where we are live from Startup Boston Week. We are with ProgShot, VP of data from CargooRooz. Yeah, thanks for having me. This is pretty fun to be recording a podcast live. I don't think I've done this before. Oh, well, I've done it before a lot, twice today. That's it. So you're a pro. I'm a total pro. I'm completely, completely down with this. But you and I met at, I think we met at the Imagination in Action over at MIT Media Lab, right? Yeah, we did. And actually, that conference, that Imagination in Action was so impactful to me. And Sarah Rich on my team who attended with me, that we came back and my data science leader was interested in attending as well. So he is in San Francisco right now. Oh, it's great. They went to the Stanford. Stanford, yeah, to attend the Imagination in Action out there. That's great. Yeah, that is so cool. Those events are incredible. And I remember, yeah, we started talking. I think we were getting food at the same time. So we met, I guess we were hungry at the same time. And we started talking about, you know, women and data and women in sort of like tech and all that stuff. And then I met Sarah and it was, yeah, it was cool. So. Yeah, it was a great opportunity to really network and talk to people who are in this space who are using data every day, using AI, trying to figure out the best use cases for it. And to understand really like, what comes next? Right. Exactly. And who knows, right? And the good news is that nobody knows. So we're all figuring it out together, right? That's right. We, some of us know a little bit more than others, but most of us really don't know anything. But before we talk about AI, which is super important, I want to talk about something else. That's also really important to me. And that is your building. Yes. Okay. So anyone in Boston, you know that they built more buildings over the top of the pike, right? So when you're going east, right? So when you're going into the city on the pike, you see these beautiful buildings. And one of them has a giant car gurus on the top. It's so cool. But that's not the part I want to talk about. I want to talk about your parking garage. Oh, yeah. Okay. Yeah, I do. I want to talk about your parking garage, not because your car gurus, like your car company, but because I parked there once when I got a spot hero, I've never seen such a beautiful clean garage in my entire life. What is up with that? I can just say that the team down there from VPN to E, they do a fantastic job. They are so personable. And honestly, they're members of the team and we treat them as such, but they just take care of everything so well. It was amazing. I was like, I don't think I've ever seen such a beautiful garage. And I was pretty impressed. And then it made me have a good feeling about your whole company. So just let them know they're crushing it. I will. I will. I'll let them know next time I see them, which will probably be tomorrow. Tomorrow. Very good. Well, yeah. But of course, you probably do more things than, you know, think about the parking garage. So if people don't know, everyone of course knows car gurus, but if you don't, like, give us just a little bit about what y'all do. And then we'll talk about how you're using AI. Yeah. Yeah. Sure. Yeah. Sure. We are the number one auto marketplace in the United States. So we connect consumers to dealers. And we help them realize that value. Great. Right. So you help people who are looking for a car, find a car. Find a car? Yeah. Within their budget or whatever they may be looking for. And we just, we try to be the middle person who connects people. Right. And when you think about sort of things that people look forward to. And maybe this is coming from my perspective. And I'm giving away sort of my personal preferences. But maybe buying a car is not the top of the list in terms of going to the market. And then the dealers running all around feeling like you're not sure if you're getting a good dealer if they're kind of pulling the wool over your eyes and things like that. So is that a fair comment to make that car group is helping solve that pain point? Yeah. We're helping. We're in there utilizing our deal ratings. And people can search based on that. And it makes life a lot easier when you're going through this search. And you have everything at your fingertips. It makes life so much easier when you're going through the car buying experience. Yeah. And you know, I mean, you want to get a good one. And I have to say like, I'm a huge Toyota person. My Toyota, I feel like I'm going to drive that until like, I'm a grandma. Yeah. Right. Yeah. I mean, people have the things that they love. Yeah. The vehicles that they love. And they, you know, sometimes they stick with it and sometimes they expand beyond it. And you never know. I mean, myself, I'm always looking for that 1968 Shelby GT. Oh, okay. 1968 Shelby GT. That sounds very cool. And also, I don't know what it is. But what is that? Is that just like a cool classic car? It is. It's a Ford Mustang. Oh, that I know. It was the car from gone in 60 seconds. Oh, yeah. The car from Bullet. Oh, yeah. So it's kind of been a dream car. When I was a kid, I moved, I graduated from Legos to model cars at one point. Yeah. And the first model car I built was a 1968 Shelby. And it was just since then, it's been ingrained in my DNA to try to find one eventually when I get to the point where I have the means to buy one. That's fun. And it's such an American thing that like our cars, like the cars that we think of throughout our life, how much they kind of like reflect a certain era or like they have a meaning. Yeah. Like for me, it was the VW bug, right? Oh, yeah. That was my family always had a VW bug. And of course, we called it Herbie here, right? Of course. But we drove all the way from Wisconsin to California in a little VW bug. And they didn't have air conditioning back then, right? So, but cars mean a lot. And so, okay. So, when you think about it, you're working with people who are trying to solve a really big problem, right? They need a car. They want what is going to work for them. And so, the amount of data and the inputs, right? When you think of millions of people, millions of our automobiles, like so that becomes a gigantic database, a data set, which is exactly what you do, right? You're the VP of data. So, talk to us about that. Like, what is someone in your role kind of thinking about when you're trying to make these matches for people? Yeah, I think one of the biggest things that I think about is really more so along the lines of empowerment and empowering people at the organization to understand that data, freely, and analyze that data freely, to truly democratize that data internally. Right. And how do we get that data from this raw point into a place where it's transformed and actionable and then democratized? And so, that's really what we focus on. That's the biggest thing I've focused on, probably at my last three or four roles, is finding ways to do that and to democratize that data. And it is often a heavy lift. You know, there's transformation that happens. There is enablement and empowerment that has to happen. And there's a lot that goes into that. And you have to make sure you have things like strong data governance policies and things like that. And the way I look at that is, I look at data governance. A lot of people look at it as like, oh, this is a roadblock. You're putting roadblocks in front of me. What I say to that is, I'm actually putting card rails there for you. So if you're an F1 driver and you want to drive really fast, you're more comfortable driving fast if card rails are in place, aren't you? Oh, yeah. If there's no card rails in place, you're going to be a little bit scared about that and it's a little bit less safe. And so that's what we want people to look at data governance as is card rails and not roadblocks. That makes so much sense because when you have that amount of sort of information that you can mind, it is so easy to do it wrong. Right. The wrong sort of signals from it or to have people not knowingly using it in ways that are just going to give bad information, right? So that's part of what you mean by the governance is not only protecting the data and making sure it's safe and secure, but making sure that it's being action in the best way. Understandable. I understand. I understand. I understand. That's it. That's making sure that you have something in some sort of searchable catalog that people can say, hey, I want to look up this term and how it's calculated. What does it mean? Putting that there, putting it at their fingertips so they know how to use the data. Right. And so when you say like, so you have this giant data set where you work now or where you've worked in the past. So when you say democratizing that, you mean so that other people that work in that company, whether they're marketing your sales or whatever, HR, anything that they can access sort of like information that they need for their own business processes. That's right. That's right. The way we look at it is you have all of these teams that are across the board that can use data and understand the data from their systems probably better than others. And so we want to make sure that they can properly analyze that data and utilize that data to make good decisions going forward. And anything we can do to help that and put that into their hands is win for us. Right. Exactly. Because then they can make decisions that really to KPIs or do things better that they can keep sort of all the things aligned for the business needs. That's right. And not have to, I'm in marketing so I'm very guilty of this myself calling IT all the time. Maybe. Like so people can kind of come to actionable things themselves. Yeah, that's right. Because honestly, let's take marketing as an example. Yeah, perfect. You know marketing so much better than I do. How am I going to tell you what marketing data you should utilize and how you should utilize it and what other data you should combine with it. So if you come to me and tell me that, then I'm now informed and I can help make it so that you can self serve on that. That's great. And so it just makes the whole place more efficient. And then of course at the end of the day, what it is is to make the customer happy and get their problem solved quickly and easily. So when we think of like a search, right? So we're going and we're typing in a couple keywords or something or maybe there's a box and you check you want like a foreign card, an American card, you want a four-speed or an automatic. And so that's kind of the old fashioned way, right? But that's maybe what many of us think about. So I know you have implemented natural language search tools. That's right. So tell me more about that because that sounds like it's kind of a lot of fun to use. It is. So really cool. There's a AI search capability on cargers now that anybody can go to by typing in cargers.com slash discover. And when they go to that page, it brings up a natural language search box. So for an example, if I wanted to go there and I wanted to say, hey, I'm a dad, I have three kids, I take them to the sporting events all the time, I really don't want to drive a minivan. I know. And it'll come back with a bunch of responses and it'll show me. And I can then follow up. Because it recall, it remembers the context of my first question. Now I can follow up and I can say, okay, now I only want to look at three row SUVs. And then it'll filter down again. And then you can say, now I want to look at only Japanese SUVs. And I want them to be luxury. And then you go back and they can say, actually, now I want to look at German luxury SUVs. And you can get down to a multi-make model search very easily by using your own natural language and you're interacting with words as opposed to filters and keywords. That's great. So would it also help you find then where I could find that in the market? Or is it just point to me and say, this is a kind of car that's good for you? Is it like, and there's one over at Native XYZ? Yeah, yeah. So that's what they'll do. It'll return the listings for those vehicles in your local area or wherever you put in your zip code that you're looking and that stuff. So it'll actually returns results of cars that are listed on carguards.com that match your search criteria within that natural language search. Oh, that's awesome. So you can, that just cuts out so much of that. And some people I know love the car. So they love to go to every dealer and walk around. But for some of us, that sounds a lot more fun to me. Yeah, it's an online experience. And it's awesome. I use it all the time. Just honestly, just to play with it. Because I think it's such a cool experience to be able to do that. And when Chatchy PT was introduced, really, I'd say probably Chatchy PT 3, right? Back in 20, was it 22? Yeah, Chatchy feels like a million years ago, but not so long ago. But it was just a couple years ago. Back in the olden days. And back then, when it was released, we started to learn about prompting. And people started to use, well, not all people, but some people started to use it. And then that was an explosion for generative AI, which is what we're talking about here. And the larger umbrella of AI, and I think you were talking about this before, it also encompasses machine learning and data science. And things we've been doing for decades. Right, this is not new. That's right. Yeah, this is natural language, something that we, back in the day, used to call NLP, natural language processing. Oh, I never heard that one. NLP. Yeah, yeah. It's a bit smarter. Very common data science term back when we were doing that. And that was how you did things like sentiment analysis and understanding what people think of your company based on reviews or things like that that you have in your database. Right. Right. So it was this raw data, relatively unstructured, because it's just a blurb of something that somebody wrote and you want to understand what that means. And if you look back in the day, there was, I think it was nuance. They had their speech to text software. Really? Oh, yes, I remember that. I think it was called like dragon or something like that. Yeah, yeah, yeah, yeah. That was ahead of its time. I mean, that was very ahead of its time, but that was natural language search, right? Right. Or not natural language search, but natural language processing. Yeah, yeah. Which is what we'd get today, but it's so much faster. Yeah. Huge amounts of data that you're using to train these large language models. And now you've moved on to, you know, dare I say, NLP on performance enhancing drugs. Yeah, yeah. I haven't thought about dry in years. That was a funny slashback. I think a lot of attorneys liked it because they have so much, you know, they're so much they'd have to be writing all the time. They were also a Massachusetts company. That's right. Yeah, they were in Burlington. Of course, they're a Massachusetts. I mean, we are the cutting edge of technology in Massachusetts and not just technology, but medicine and education. It's a great state to be in. Okay. And I just mentioned one of our other things we talked about that we're both fascinated in about is education, right? So we think about, so not to like do a sharp right turn here, but when you think about the youngins of today, you know, college isn't, we have 45 or whatever it is, colleges and Boston, like it's insane, right? Yeah. And it's just the quality of talent here is amazing. And we know retaining that talent in our ecosystem is a huge initiative, not always easy given the cost of living and things like that. But, you know, I'm imagining Cargoo is as, you know, hot companies. I'm sure you have a lot of really, a lot of these great students, you know, these really smart, bright kids coming along and wanting to work with you. And, you know, some of the best things in your career. I mean, we have a co-op program and, you know, these young folks come in and they work with us and they learn and it's amazing to see the kind of talent that's out there. I mean, I see young kids who are doing things and being able to perform at a level that I can't, I can't even conceive myself performing at that level when I was their age. Right. Right. From the engineering perspective, right? That's right. Right. So, so we have, and you know, no shade to any of these schools. I mean, they're all amazing, but I was just walking around in my teeth at a day at an event and like I've, I've never, I've walked in to the cafeteria because I was getting a water or whatever. And I just looked around and said, literally, this is the pinnacle of the world. Like the students that are in this, just sitting here having lunch, like this is the cream of the crop. Right here. But we also know that just because you're amazing engineering doesn't mean your NASA is really great at everything else. So tell me a little bit about that because I know you have an interest in this kind of whole person. Yeah. No, it's a great way to put it. Yeah. I mean, it is the whole person. It's understanding that there are two different sides to your, your everyday work, right? There's your, there's the side that's personal and almost like the liberal arts thinking side and the technology thinking side. And to be able to balance those two things is more of a, I would say it's a, it's essentially something that we're falling behind on because we live in such a digital age and we have for so long that there are a lot of students who are coming through who have lived online their entire lives. Right. And it makes it a little bit of a, a little jarring when it comes to relationship building in real, in the real world in case to face. COVID messed up a lot. Absolutely. I have two teenagers and you know, there was some skip development there. Yeah. I mean, when I was a kid, I always hear about people talking about this and how Gen X were a feral generation because we used to do that. That's why we're the best. We're the best generation. No, best, everyone. We, you know, we, we were out of the house when we were in school at like 8 a.m. Yep. And your home when the street lights come on. Yep. And you drink from random people's hoses if you get thirsty. That's right. And you know, there's no bottled water. There was no bottled water. The only bottled water had fizz in it. That's right. That's right. And back then we had things like clearly Canadian stuff like that. I remember these drinks that I had as a kid. Yeah. But I would leave the house with my friends and be out all day and all night riding my bike 20, 30 miles throughout the day. And there was, there was nothing to it. But that was a part of my childhood along with my education. Right. Was building those relationships, those strong relationships, which I retained today in a lot of cases. Right. And there were a lot of buddies that I had from when I was a kid that we still go on an annual golf trip every year. That was awesome. Yeah. So it's, you know, but that translates to you. Do you drink clearly Canadian on the trip or? I try to stay away from sugar when I can and they don't make clearly Canadian zero as far as I know. Okay. Oh well. Someday. Yeah. But it's great stuff. I mean, that was the idea of building these relationships. It translates into business. Because a lot of times you have to have a trade-off conversation with someone or you have to have a tough conversation with someone. And you don't really know how to handle that because you've never really had a tough conversation with somebody in their face-to-face manner. Right. And so it goes a long way to be able to say, oh, I can now do some of this relationship building. I can, I have some of this liberal arts thinking as opposed to a very strong technical base. And you can also at the same time, folks who are going through liberal arts schools, can be utilizing some of the technology thinking that is happening at some of these technology schools like an MIT. So if you look at say, I don't know, MIT versus Tufts, right? That's a, I think that's a good example. But you know, you have people who are thinking differently. But still solving problems. Maybe MIT and Emerson. Emerson's a good one. That's much better. Let's do that. Okay. We love you, Emerson. Yeah. Yeah. Yeah. I think it's a good example of what you're doing in one way and then another, in another way. Yeah. And imagine if you combine those two together. Yeah, that's where the magic happens. That's where the magic happens. Because those are the, those are the skills that you can bring to an engineer that can help them solve business problems, right? Thinking about the business and how I can solve these problems and just be a problem-solver. When I broke into tech and I started as a developer, I had headphones on. I had my head down. Yeah. My fingers on the keyboard for eight hours a day. Right. And then I really looked up from that keyboard was when I was eating lunch or going to the bathroom. Yeah. Now we all have those like tech necks, right? Yeah. Our backs are on her. Yeah. That's it. But yeah. And I mean, that's, and it's your right. I mean, it's brilliant. And we kind of romanticize too, that like brilliant engineer. And of course, super important, you know, the unicorns and whatever, who went on to build these crazy things. But there is something left behind. And it would be the ultimate irony. And I think kind of an amazingly beautiful one. I think it's a bit of a helped bring back the importance of liberal arts education. I think so. I think it is. Yeah. I think there's there's stuff happening right now today between, you know, at the another example is Holy Cross and WPI. Okay. Talk about that. And it's just something that I was another person that I met at the imagination and action conference. Thank you John. Thank you John. Thank you John. And so, um, so Shawna and I started talking about this and she had, um, you know, this effort to kind of bring that liberal arts thinking to WPI and vice versa to Holy Cross. And they're kind of working through what this looks like right now as we speak. I'm kind of plugging it here. I wasn't really prepared to plug it for Shawna, but I think it's pretty cool to see somebody taking that, um, putting in that effort to kind of connect these students and say, we want to bring liberal arts thinking to you at WPI and at WPI. We want to bring some, I mean, we want to bring some to WPI and some of that technical thinking from WPI over to Holy Cross. It's like a cultural exchange. It is. It is. It's a mindset exchange program, right? Because you think about one thing like you, when you think about it, like the highly technical schools, like they cheat you the engineering mindset, right? And what is engineering mindset for those of us who are engineers is that just what does that mean to you? To me, what it means is is when you write, um, if you're building something, you think about how it scales, right? You think about how do you productionize this? You can't just throw something together really quick and dirty and say, okay, now I want to million people to hit this and be able to be a stable class. It'll collapse. Right? So you need to think about that from a platform perspective. You need to start to think about how to think scale. Right. Right. And those are some of the things that you learn in technical schools, right? But less so in liberal arts schools. Oh, yeah. Right. Like you don't scale a poem. That's right. I mean, you hope a lot of people read your poem, right? Not to simplify things, but that's interesting. But you think about, you know, and then I'm just going to contradict myself because if you have a beautiful piece of art, like millions and millions of millions of people are enjoying it in the Louvre, or whatever. Right. Right. Or whatever. But it's completely different, right? Because to enjoy a piece of art, you just have to show up. Right. But the Louvre has security in place. Yeah. They have cameras everywhere. They have tech everywhere. They have the infrastructure built to be able to accommodate all the people that want to see that beautiful painting. That's great. You have to make sure that you have a technical infrastructure that can support what you're trying to do as a technology company. Right. Exactly. And so to bring it back home to Boston, right? So that kind of feels like that kind of mind-meled is a really Boston thing, right? It is because there's such a diverse set of universities here in the Boston area. Right. Like in Massachusetts, it's ultra diverse. You have where we are right now. Where it's Suffolk Law School. Right. Right. Right. Right. So that's one kind of school. And really actually being here makes me think about the impact on AI on the legal space. Oh, yeah. And when you think about the fact that people start to do research a lot faster. Oh, yeah. And you still need to check that research and make sure it's accurate. Oh, yes. And we saw plenty of people go for attorneys getting themselves in trouble early on with chat with you PT. That's right. There were cases that were from the future in their briefs. Yes. Which is not real. Not real. Exactly. Real. Yeah. And one of the things I've actually noticed, I don't know if you've noticed this when you're interacting with some of these large language models. They're almost like they're playing into like really trying to be friends with you almost. Oh, it, you know, okay. This is, and I bet there's a way to get it to stop doing that if you talk to it and tell it to calm down because I, this was early on. And even though I'm like, hey, I found her and all this stuff. I'm still figuring out how to use it for my own self. Just, you know, like we all are. Yeah. And I was like, I'm not going to stop it on whatever, who the heck knows what it was. Know what I've published. I never even sent it anywhere. But I was going on and about something. And I put it in the chat, you PT and asked it what I thought and said, this is, I've, this is one of the best opeds I've ever seen. And I was like, okay, I'm not that delusional. Like there's no way. So yes, I talk, how do you as, okay, first of all as a user of it, you have to know to be like understanding it. But when you're actually building stuff at scale, that's helping customers, real people like, how do you manage that kind of like over? I love you kind of feeling from your, well, for, I think it's on a case by case basis. And that's probably a question that's more for the folks over at Anthropic and open AI, right? Because they're the ones who are returning the natural language response to your product, right? And they're building the, they're really in the model. But if you, if you think about it and, you know, I always, I always, I say things to them and we follow up. And I say something like, oh, what about this? And they're like, oh, you're absolutely right. What a brilliant discovery that you made. I'll factor that into my decision now. And then it goes back and it gives you, I was like, I don't need that. Right. And so like if you're using it for, so I mean, this is not the main, I've personal experience with, but like a lot of people using it for their coding, right? Because, because it just makes you 10 times more efficient or 100 times more efficient. But like, does that kind of, this maybe even be a dumb question. Does that kind of like your code is amazing, even if it isn't happened even there? Or is that, well, actually, I had a, I had a little bit of experience with this. We did an AI coding week at carcars. Oh, cool. And what we did was we, as an engineering team, we paused work for a week. And we all started working with these three AI coding assistants, cursor, wind surf, and GitHub co-pilot. Oh, nice. And even people at my level, I was writing code with natural language during that week. And as I was doing it, I was building a predictive model that would help me predict who was going to win the masters. Oh, nice. And as I was going into it, I was doing it completely, that's right. He likes golf, he likes golf, everything. It was, it was completely prompt driven development. It was writing all Python code for me. And I was just prompting it. And I was saying, oh, but what about this? And what about that? And it would come back and say, oh, that's a great point. Let me incorporate that. Oh, good. So because you have the skill and the knowledge you could all of a sudden become like hyper efficient because you knew, but you knew how to talk to it. That's right. And I convinced Chatshipy to get Chatshipy to tell me that it's SkyNet for the past three years. So I'm very efficient to prompt. And how does it answer you, Ana? It hasn't yet told me that it's judgment day. So I think we're in good shape right now. I had a long conversation early on when I was using Chatshipy to create imagery. I got into this philosophical conversation with it if it was an artist. Oh, interesting. Yeah. Yeah. And it would not say that it was an artist. Decline to define itself that way, which I thought was interesting. Yeah, that's really interesting. It's really cool to see how it impacts things like art because my sister-in-law, she's currently at MassArt. Oh, very cool. And she's going there. And we've had conversations about AI and how they should be using it versus how they shouldn't be using it. And these are all conversations that every student needs to be having. And this technology supporting art is not new, right? No. And I won't have good examples in terms of the names of the artists, like during the Renaissance, like there were certain, probably it was like, you know, Leonardo da Vinci, or people who created like certain ways to do shadows or mirrors or lighting where it was like tracing and things like that. So even though when you see the end result, it's this most amazing thing ever. There was a little technology helping them. Graphic design. Yeah. Graphic design. Yeah. It's a technology way to do art. Right. Yeah. And again, back to the democratizing. It democratizes art. Like, I, I'll never be a musician. I'll never be a great artist, but it's fun to play with. So it gives you that creative zoom. Maybe not to be a professional, but to like play. Yeah. Like, have your voice. Have your voice equal. Honestly, I'm going to stay away from that. I'm tone deaf. I don't even think AI can fix me. Okay. You're beyond the hope of anything there. But that's okay. Well, so what's next for you and for Cargo Groozy, working on any new cool stuff for you, just perfecting your natural language search. We're just going to continue to forge ahead and take advantage of AI where we can and see kind of where this all goes, because this is a rapidly evolving space and it changes on a daily basis on an hourly basis sometimes. I mean, it feels like sometimes you wake up the next you go to bed and I bet there's the head of engineering or data you deal with this all the time you go to bed. And I say, great, I have a plan. I know what I'm going to do. I'm going to tell my team in the morning, XYZ, you wake up in the morning, you watch the news or hear the news or whatever. I'll swipe the news and it's all out the window. You got to start over. I have a daily TLDR AI newsletter that I read that I spend time reading every single day from 5 a.m. to 8 a.m. I'm reading articles about what's happened in AI on the day before. Isn't that amazing? And the turn is so it's just so much faster, right? It is. And how does that, I mean, one last question before we go, like for your teams, right? So you have a lot of people who are for you and you need to keep them feeling like comfortable in the space and all that. Why do you handle that as a manager of people who have to keep up with us all the time? Is it stressful? Like what's it? I don't really expect them to have to keep up with it as much as I do. So I'm keeping up with it. And then when there are breakthroughs that I think we can take advantage of, that's when we'll start to train people up on those things. So my personal goal is to shelter them from some of that and be the person who has to put the extra effort in and then find the value and then help us, you know, see and realize that value as a team. That's great. So you basically are relying on your human qualities of communication, filtering and watching out for your teammates to get you through this crazy tech time. That's it. Cool. Well, with that, I think we'll wrap it up and it's so good to see you. I'm so glad we met at Johns event and I hope you'll come back and tell us all the other cool things you're building. And I want to hear more about WPI and Holy Cross. Absolutely. 100%. Okay. Thank you so much. Thank you, Cara. It was a pleasure. Thank you for joining us on Building AI Boston. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.