A (3:43)

That's incredible. I have 9 million questions about your background that we don't have time for right now. So I'm going to narrow it down to just one. When you took time off and traveled. Favorite spot.

B (3:53)

Oh, my God.

A (3:54)

You have to pick one.

B (3:55)

Okay, I'm going to pick. I'm going to pick two.

A (3:57)

Okay.

B (3:58)

So it's really hard, but I'm going to say I loved Japan. Yeah, Japan was. Is amazing. And my husband convinced us to spend three nights in the Sahara Desert or like traveling from Fez to Morocco, and then we spend a night in the Sahara. And I'm definitely old enough to remember Windows 95 screensavers. It looks exactly like that. Like in real life. Like a screensaver come to life.

A (4:30)

So when you're done founding and selling successful startups, you can write some travel memoirs and link them back to the tech experience.

B (4:37)

Exactly.

A (4:39)

So with all of your technical background, what made you. I mean, I don't know what made you. It makes sense to me why you wanted to found something for less technical people to be able to create products, kind of get a handle on things, but sort of. Was that, I don't know, when you met Sean and the two of you decided to do this, was that the initial idea or did you kind of iterate on some stuff before landing on impromptu.

B (5:00)

Yeah, no, actually that was not the original idea.

A (5:03)

Okay.

B (5:03)

We had no. We had no intentions on starting a huge company. We actually were started off by using the tech that he invented, the art, which we call now our AI core, which is our optimization tech, and we started just building AI apps For people, like, people needed our help to build these AI applications and we were like, hey, how do we help them do it faster and more accurately? That was always the goal. Like, how do we make sure that we get good outputs? I mean, because back then, like you would get, you know, it would hallucinate like crazy. I mean, it kind of still does, but like, it would, it, it was wild, the responses. And so one of the things that we need to do is really build trust with AI. And so no, we were just manually going and helping or people in our network build AI applications. And then after, you know, over time, we ended up building a bunch of these and we kind of took a step back. I, I actually literally have this recorded. The moment that this happened, We'. Oh, the infrastructure to build AI applications across these, across all of our customers is exactly the same. I wonder if we can agentically write this AI application. And Sean looks at me and goes, wait, you want an AI that builds AI? And I was like, well, is that possible? And he's like, no, it's not possible. And then he does the thing that he always does where he says no. And then, and then, you know, 20 minutes later he goes, well, maybe. Right, right, Maybe if we did this and this and this and fast forward till today and here we are, we have an AI that is very specialized, specialized builder to build actual AI applications.

A (6:49)

And so you're working primarily, your customers are primarily or entirely enterprise sized clients.

B (6:56)

Yeah, so they're, they're all, they're businesses, businesses of different sizes. Right. So they're mostly large enterprises and midsize enterprises. Typically if you have a SaaS application already, right. You know, you have a platform, you have a service that you're doing with a website or you have, you know, a running business. How do I transform that into an AI native company? Right. And that's really where the gap is. Right. Like you don't. You need to start from this legacy code base.

A (7:25)

I was going to say that's, that's the trillion dollar question. Except, I don't know, I keep thinking about the 98% accuracy and that might be the $2 trillion question.

B (7:34)

Well, here's the thing. Both of those have to.

A (7:36)

Yeah, exactly.

B (7:37)

Right. You, you can't have, you know, it doesn't matter if you can get 98% accuracy if you have to throw out your entire code base. Right. And it doesn't matter if you, you know, have to, you know, you're starting from a greenfield project and then your output kind of sucks. Right. So you Need a lot of these pieces to work together. And we've invented, you know, five new pieces of tech, from adaptive context engines to infinite memory to being able to, to our optimization core. To be able to ingest a code base and say, hey, I'm going to import this whole code base from GitHub and now add AI to it. That wasn't possible before we invented the things that we did.

A (8:17)

Right. To focus on the enterprise scale for a minute here. When you're working with a client of that size and complexity and teams of data scientists and so many stakeholders, and you start working with somebody who's less technical, but the idea is to help them build something kind of in that context, how do you approach it? And I don't know if the best way is to kind of ask you questions piece by piece or if you want to walk through kind of an example project, but when I'm messing around with AI and having it build something for my own purposes, right. I mean, obviously the threshold for success is much lower. It doesn't matter if it's not perfect just for me, but also the process, I can just kind of put my head down and mess around, see what I come up with. I'm at work, I'm in the team. It's a very different situation. Right, so what is that like for impromptu and the folks you're working with?

B (9:09)

Well, it's multifold. Right. Because the mission of our company is to make AI accurate and accessible. Right. So how do we make sure to get this technology into the hands of people who it's not that they haven't coded before? Because I talked to 20 year developer, like people have been developing for 20 years and this is a new technology that we're all learning at the same time. Right, Right. And so all of that comes with education, all of that. It's like if I said, hey, you're going to go walk into Sephora and now you need to go and find sunscreen amongst a thousand products, how do you do that? You know what I mean? So like it's, it's just, I do.

A (9:50)

I tried to get something from Sephora once for somebody else.

B (9:53)

It's hard.

A (9:55)

There's a lot going on.

B (9:56)

Exactly. It's the same thing.

A (9:58)

Right. So it's, it's, it's not a less technical person per se. Right. But it's someone who's less, less versed in what's happening in AI, which is a race to keep up with.

B (10:07)

Exactly. And so we in generative and we, we definitely think about like hey, everybody is learning generative for the first time. What are the pieces to educate anybody at any technical level. And so we do that not only with our ui, where the UI of the builder allows like actually knows what all seven pieces are to build the full seven pieces to make an AI application, but it asks you back and forth questions to make sure that you have supplied it with the right information and it goes and has a full conversation with you. And we also have a co build model. Right. So we as experts will also alongside of our AI tech, also build in collaboration with the organization and educate their teams with work alongside their teams so that, you know, it doesn't feel like we're just going to kind of leave you in lurch. Right. And so because enterprises we need like everything is changing so fast, right? You need to have someone who's keeping up with this while you're running your business and running your team and running, you know, all of the things internally. Someone needs to keep up with this stuff. And that's basically us.

A (11:21)

Yeah. What is that? You know, I use the word transformation all the time. You know, you just mentioned it and it's accurate. But what does it mean in this context of, you know, transforming an industry, transforming a business and the work that you're doing sort of, you know, on the ground level, right. I mean you've got that, that high perspective from your background and everything, but you're right there doing it. So you know, folks like me are looking at kind of broader trends and thinking like, wow, you know, the ability to AI is really changing, is really changing the way search works. Right. It's just a giant example. Right, right. Kind of a big glossy thing to say. But what are you, you know, what are you seeing? Are there, are there certain trends that are emerging, certain types of problems that people are having more success or less success, you know, with.

B (12:09)

Yeah. So I, so the, the larger trends as we're kind of working on these things together is a huge thing in physical AI, right? Like how do you move things in a physical space? We work with a ton of folks in that capacity. We also are really thinking about context, right. And memory and how as you think about like if code is going to be this, you know, arbitrary commodity, what then becomes important? And then that's the data, right? How do you then tackle this next frontier to create an end to end solution. So now on, you know, last week we announced our custom data models in straight into our builder. So now you have not only you can build an application, but it's built Right off of the custom data and it's trained in the exact way that you would want it to be trained. And so, so you get really highly accurate responses, right. And then that becomes really, you take it one level up. So you start thinking about multi tenancy, you start thinking about specialized models, you start thinking about AI doing decision making for you and that's where you really, where you get into this thing called provable AI. Like what's actually happening? What decisions is it making? How do I control and then thinking about the governance plane, well, If I have 10 applications across my organization, how do I make sure that it follows these rules and we're tackling all of those things in one fell swoop to be able to make sure that if you are going to go down this journey that it is not only accessible to anyone on your team and it scales appropriately, but it also feels like you have full control over it.

A (13:47)

Right. How big is the gap for a, you know, first time generative AI builder, to put it that way, from you know, something that I might build and say like oh this like seems to work right in my use case, going from that to something that's production ready. How big is that gap and how do you help people bridge it?

B (14:08)

Yeah, so great question. We actually think about it slightly differently. Where I can get you a chatbot in probably 10 minutes, it has all the production ready pieces that you need. Right. But what you actually are now what we're thinking about is enterprise scale. How do you do that with terabytes of data? How do you do that in real time across millions of users? How do you do that, you know, where you have full governance and control. Right. So like today in our self serve platform, if you're on like a big enough plan, I can get you very fast, very accurate system in probably 10 minutes. But it's the bridging the gap as you grow and have multiple interconnected AI workflows and you know, being able to make sure that they're all accurate and all running to the best of their ability.

A (15:02)

Right. You used the word accessibility a minute ago and you know, talking about AI being accessible to first timers, less technical people, what does that mean, you know, kind of day to day in your work and maybe like are there ways that people use their kind of misconceptions people have about, about that term?

B (15:20)

Yeah. So I think AI accessibility at this stage is allowing anyone with an idea, not just to use AI to build old technologies. Right. It's not just using generative to make HTML, CSS JavaScript, like all those are great, right? We absolutely need those. But how do we use AI to actually build AI to put generative thinking, living breathing applications into people's hands? And so it's not only new for developers, but it's new for the business owners. It's new when you're thinking about companies being disrupted. It's new, new for the venture community, it's new for everyone. And so how do we democratize and actually have this new technology to be disseminated to anyone who wants it? Because now, you know, I can, I. We have customers who, you know, are doing these amazing things and now can enable these in like, even more amazing things for their customers. So, you know, we have a mom and daughter team who is, you know, building financial literacy, or we have a CPG brand who takes Ocean plastic and turns that into activewear, right? Like, so not just the super technical large enterprise companies, but like every across the spectrum.

A (16:46)

The example of the mom and daughter team, like, I can wrap my head around that easily because I've, I've, you know, typed in like, help me code a dashboard for podcast analytics, right? I've gone on that journey. How does AI help? And impromptu, in your experience, how did it help the process of recycling ocean waste to create activewear.

B (17:08)

So really thinking about how do you bring all of these systems together, right? How do you make recommendations to the founders and their teams internally to make sure that if you're spinning up an event to collect the, the, the bottles, like, how do I spin up a new app, a new city across the country to do the exact same thing? How do I operationalize this process? Tell me how to do it. AI, right? And so putting all. But in order to do that, that's very custom data, right, that you're not just going to get from a general model, right? You need their system and their models because that's not a very typical, typical use case. And so those are the sorts of things that now you enable. I use AI every single day in my, in my system to build this company. It's unheard of going from five years ago, building a company then till now. Like my day to day is completely different.

A (18:10)

Yeah. Well, it's funny, as you were saying about when you and Sean, I want to call him Dr. Sean.

B (18:15)

You should, you should call him Dr. Sean.

A (18:18)

When, when you and Dr. Sean, when you and your co founder got together, you know, and the question of, wait, you want me to build AI that builds AI and like you said, fast forward and now, you know, I mean, heard Jensen say it, but lots of people are saying it, that AI is going to mostly generate tokens to be used by agents. Other AI systems, like. Right. Like the majority of the tokens as we go forward are not going to be creating output that human sees humans see. Excuse me, but that, you know, another AI is going to act with.

B (18:48)

Yeah.

A (18:49)

And it's amazing.

B (18:50)

I'm sorry, yeah, no, it's truly, it's truly amazing. You know, we're working with some customers who. And some, you know, people's dreams who, you know, would have never had the ability to do this. But then also you're thinking about like, oh, actually I don't, like eventually we're not going to need a full human into this at all. Right. And so we can actually AI generate human in the loop systems, but we can also just, you know, generate a full end to end API that you know, goes out from the front end, does a whole system, doesn't AI generate and puts it back as an API back into the customer system. And like no one ever knows that we're doing anything right because it's fully containerized and all of those things. So we're already seeing that day to day.

A (19:40)

My guest is Shanaya Levin. Shanaya is CEO and co founder of Impromptu with an E E M P R O M PTU AI. I want to get that out there if you're listening now and want to look it up later. And we're talking about Impromptu's work helping people of all technical abilities, but certainly people who are less used to building with generative AI build products and at enterprise scale and production ready and the whole thing. And so I want to ask you kind of a more technical question here. Impromptu uses Nvidia cuda.

B (20:14)

Yeah.

A (20:15)

Can you talk about how the CUDA libraries for embedding and classification in particular improve your performance and make AI efficient. All that good stuff. But how it actually translates to the work that you're doing and then in particular the work that your customers are doing in creating with these tools.

B (20:33)

Yeah. So we consider an Impromptu to be a mixed code builder instead of no code or pro code, it's kind of, we call it a mixed code under the hood though parts of it are essentially a big embedding and classification machine. Right. So every time, you know, we map a new feature or iterator design or you know, you search your custom data model, you know, we're turning that into vectors and making bigger decisions. On top of that now we have our own kind of custom models and our own secret toss Alongside of that, but we use Nvidia's Cuda libraries so that all of the, you know, the heavy math natively runs on GPUs instead of slowly on CPUs. And that really gives us two big wins. Right. First, it's performance. So all of the everyday creators in our UI get that instant feedback. They can request a feature or upload or tweak a prompt and the AI just kind of responds. And so they can iterate much faster, ship much more useful products. And then the second thing is efficiency. So you. Cuda lets us serve a ton of those workloads in like a relatively small GPU footprint, whether that's on our, you know, in our cloud or our customers vpc.

A (21:47)

Right. And how does CUDA help? I mean, you talked about this a little bit to put a point on it. How does CUDA help kind of bridge the gap? And I'm wondering from your perspective how big or small that gap really is these days between kind of the cutting edge AI research that's being done and then the tools that, you know, your customers are actually able to use to build things.

B (22:09)

Yeah, I think at this point the gap is mostly filled, if I do say so myself. Today you don't need a big machine learning team to build something sophisticated. Cuda helps us with that hard math. And so our platform turns all of that into this fast, affordable, just works experience for non technical and technical teams. Because technical versus non technical, those now are, they go together now. Right. It's generative or not generative. Right. And so as we're thinking about that, like all of those things, I've now been able to do it in a, you know, a self serve. If you really want to do that or if you want the pros to actually build it for you, you can just throw that out. Right. We can actually just use our own systems to build it on your behalf and do the integration for you. But all of the things under the hood are basically, basically all like taken care of.

A (23:12)

All right, so this is the point where I come back to this 98% thing that, you know, I was like, I don't want to ask up front because probably it's secret sauce, we're not going to get too into it, but can you get into to any of the details of how it works?

B (23:24)

Yeah, so it does work a little bit differently than most other systems. And what we found is that a lot of people talk about benchmarks. Model benchmarks. Right, sure. Model benchmarks are great. Everybody has a model benchmark. Excellent. But how do you translate that model benchmark to the everyday enterprise who's like, I, I have a goal. I have things to do right. I have stuff to do right? And so our accuracy is actually redefining that success, which is task success. And because we work with so many use cases and so many customers and enterprises, we allow the user to define what success means to them, what that task is, what success looks like. And we will use our full optimization engine that optimizes the entire system, including the model, the data, the prompts, the evals, all of the under secret sauce under the hood, to optimize in real time towards that goal that the user sets. And we measure it directly in our dashboard, so you'll see a big number of how we improve that accuracy towards the goal that the user set. The other thing that might not be so obvious is we have two modes. One is for, it's called manual optimization, which are all for all of those technical, deep, technical developers who want to tinker and turn knobs, just like myself. Right? I want to do that. But you can actually see the output towards that accuracy and it will actually give you suggestions. You can click the button however many times you want to click it and see, you know, improve it however many times you want. After about 30 runs, though, we can turn on something called automatic optimization. So if you're less technical and you're like, I don't know what the heck that is, that sounds smart. It is, but you don't want to tinker with it yourself. We'll just do the automatic optimization for you up to, you know, 98% of that task accuracy. And we're always improving it. It's always, you know, we're always trying to get to, you know, three nines, which I definitely presume that we will one day.

A (25:32)

And so that, you know, that speaks to. And you brought this up before, and obviously it's a big, A big thing right now in the, in the industry, in the marketplace is building trust.

B (25:41)

Definitely.

A (25:42)

Are your, you know, your customers, like, are they looking at like, does that dashboard, that figure of optimization, does that do a lot to improve the trust? Is it really just seeing the results and being able to say, like, yes, it helped me get this project, you know, off the ground and running and it's working well? How do you build trust, you know, right now when there's kind of this tension between the race to build out all the infrastructure in the data centers and everybody wants chips and can't get them, and then, you know, everything that we've Been talking about.

B (26:17)

Yeah. So we subscribe to something called provable AI and we think that that is the absolute way to build trust. Right. And make it so that it is accessible. Right. If you can't see it, feel it, touch it, in a theoretical sense, because it's all bytes and it's all number, ones and zeros. Right.

A (26:38)

Well, until we get to physical AI, like you mentioned, and then, you know, until.

B (26:41)

Right, exactly. So thinking about that, you know, seeing, okay, what decisions is the AI making, how do I roll back? Right. How do I, you know, if I have infinite memory, you know, how, like what, you know, is involved in this decision. Right. Like if I'm doing a custom data model, you know, how what data is kind of going in, being able to showcase, like, here are all the runs that we took. Right. Showing those accuracy numbers in the dashboard. Right. We always try to showcase, you know, provable. Hey, this is the number, this is the suggestion. This is the, you know, the set that you need to be able to do. Now, can you disconnect those things for privacy concerns? Absolutely. But proving to the user that this is, we're doing what we say we're doing is of the utmost importance.

A (27:38)

Yeah, yeah. To switch gears a little bit as a woman in tech. Right. Which like everything else, in particular, everything else in tech has changed a lot very quickly in certain ways. And, you know, not to speak for you, but probably not so much in other ways. But then, you know, working now with a lot of people who are, you know, the whole idea of making AI accessible. Right. How does your experience as you as a woman in tech, you know, co founding and running this company now and working with folks, how does that inform kind of how you see, you know, barriers to entry? Whether it's, oh, I'm new to Generative and I want to start working with Generative, or, you know, oh, I've never done something at production scale or, oh, like I'm a woman and I've experienced what I've experienced and so I'm interested in this stuff, but I don't know.

B (28:33)

Like, yeah, it definitely really, you know, hits my heart. I can tell you a very quick story, so please. When I, I studied, obviously studied business, computer science, but I had a small web development company that I started when I was 19 and then my first big job, like big, big job after I was in an analyst was working at Google and I was like, so I had two choices, you know, go to search, Google search at the time and then go to this thing called developer tools. And at the time I was like, I just seen the internship, which I know kind of, but it felt like this really overwhelming technical, like I'm going to be working with some of the biggest, smartest developers that you know, ever. It's very intimidating. And then I ended up choosing to go to developer tools because I thought that would be easier. It was not, but. And obviously changed the course of my entire life. Yeah, but that one decision and I don't want anyone else to make a technical decision like that out of fear, right. I don't want women or kids that I talk to. I have 12 year olds emailing me, right. Thinking about how do they build generative systems. And I never want that to feel like you have an idea and you can't execute it. Right. And so we take great care with whatever technical ability you have to take advantage of this technology. Because I constantly think about, you know, going into these rooms, right, with, with people. And I think luckily now I've, I've built things for millions of people, but at the same time I know deeply what that feels like to be told that you're not technical enough to be told or to have someone, you know, talk to Sean and not talk to me. Right. And you know, those sorts of things. So I think that it is, we are at a place in our evolution that now that barrier is fully down, right? It's fully, it's fully unblocked. Everybody is running. We are, you know, I'm talking to, you know, people who have gotten laid off from jobs. You know, how do I take this knowledge that I've gained over the last, you know, 10 years and start monetizing it into a real business and are making real money off of this using generative. And then also completely on the other end right? At these large enterprises where they're like, hey, I know we use AI internally, but I still don't know really how to use it, right. Like, how do I set myself up for success? How do I put this in front of users to help businesses reach their goals? I mean, it's all over the place, right? So thinking about all of those different stakeholders is really important to us.

A (31:32)

All right, so keep thinking about all those stakeholders, right?

B (31:35)

Yeah.

A (31:36)

The notion that studying computer science, the 12 year olds, right. Studying computer science may not necessarily be the route to take now if you want to go into a, we'll just call it a computer career, so to speak, right? If it's not. If it is, tell me. But if it's not, if coding skills aren't necessarily like the Top thing looking forward that you need to be successful with AI, with technology, with generative everything. What are some of the skills, some of the mindsets, some of the knowledge that you would advise the 12 year old or you know, the.

B (32:13)

Yeah.

A (32:14)

The 50 year old who got laid off and, and wants to write, wants to do something new.

B (32:18)

Yeah.

A (32:19)

What would you tell them to get into and how would you guide them?

B (32:22)

So as the. I also have six children. I happen to have my youngest sister as an intern at my company. And so I think about this all the time, you know, and I, I think that for kids coming out of the, out of school today, I don't, I, you know, I know that the headlines are thinking about, you know, compute, like computer science is out and you can't get jobs as junior developers. But when I studied computer science, it was not just about coding. Right? It wasn't. Computer science is not, is about cog, is about cognition. It's about thinking in systems, it's about critical thinking skills. It's about, you know, how do you break problems down into small shippable chunks. It's about, you know, building on top of and merging that creative energy, that creative ability with, you know, technology that's always changing. Right. Like, you know, I was in school and did everything on a Java application and that allowed me to go into the Android system. But then Google decided that we're going to use Kotlin now. You know what I mean? So like, like that I took, I.

A (33:35)

Took Intro to CS and it was taught in scheme. So yeah, you know, things change.

B (33:40)

Yeah, right, exactly. I mean the first class was like we did assembly code. Do you know what? It's so like, I think that people are underestimating the quality of a good computer science education because I think that the critical skills around how do I think about these larger problems that are going on in the world and how do I solve them? Needs critical thinking skills and that needs to be taught and practiced. Right?

A (34:10)

Yeah. Even with the metaphor, and this may have been from another episode recently, the metaphor of like conducting the orchestra.

B (34:20)

Right.

A (34:20)

You know, you may not be writing all the code anymore, but you're conducting the orchestra. Humans, bots, a mix.

B (34:26)

Absolutely.

A (34:26)

And that's exactly what you're talking about are those big picture thinking skills.

B (34:30)

Yeah. Like even, even as a, you know, a PM for a very long time in my career, all, you know, always on very technical products. But thinking about system design, right. You're going to need to read and understand all of the code that the AI writes and that is a skill in and of itself outside of writing code. Right. So, you know, it's just like if I decided to go read Japanese tomorrow, right. Like, I still need to practice that. Just the same way I need to learn to practice to read code and then keep that skill up to speed. And so from people coming out of college, I think computer science education is not necessarily dead. Despite the headlines. I think that the types of jobs and the types of skills that should be taught in computer science education just need to be. Some need to be de emphasized and some need to be re emphasized.

A (35:18)

That kind of leads us to, you know, sort of our last forward looking question here and given the conversation, I kind of want to ask you from the two points of view, the individual or small team and then the enterprise scale or you know, the organization. Right. And of whatever size, but certainly the enterprise scale, where do you see the biggest opportunities for AI to really make a difference and to empower folks and to, you know, lead to, I mean, whatever the successes are given the context. Like where do you see the biggest opportunities, you know, in the next, whatever. Two years is almost too long, right? But whatever the next window in your head is.

B (35:54)

Yeah. So I think the real opportunities are in fully remaking our entire world. Honestly, like, I think that's, I, I, I genuinely, I genuinely believe that like there are problems that if people.

A (36:12)

Can you open source the plan when you and Dr. Sean sail on it?

B (36:16)

Yeah, absolutely, absolutely. I'll tell you a small joke.

A (36:20)

Sorry, I didn't mean to interrupt.

B (36:21)

No, no, no worries. We had a customer who was, you know, just trying something out and in our, in our platform they're like, you know, you actually have to click the button that says build. Right, Build. Tell it to start. And he found that the, he found a ticket that was like, it's broken. And I was like, you know, very soon we should be able to have AI that reads minds, but today we can't. You still have to press the button. And so maybe like, I think that like from every industry it, you know, as we continue to work on accuracy, right. As we continue to increase dexterity for robots, as we continue to, you know, pull camera data and you know, do analyses, as we continue to map networks, as we continue to add data to these systems, I think that every industry will shift exactly the same way we did for mobile, right? Mobile was a thing. We all had mobile web. It was kind of gross. Then the apps came out and then, then we had social and now we do everything. We can't live without our phones. Right. And so it's exactly the same technological evolution as this we've seen in history. And so is it a big deal to have this shift as close to the last shift? Absolutely. But I do think that we will continue to evolve every industry. And I think if certain, like, people got out of the way, we. We could make sure to do, like, new climate change and we could, you know, solve these huge existential crises and we could, like, these are all, in my opinion, I could be wrong about this, but really solvable problems. The problems are human problems that we keep creating, but I feel like we've moved out of the way, out of our own way. We could really make sure to make the world a truly better place.

A (38:20)

I want to leave it there. You said truly better place. That's a perfect spot to end on. Shania, this has been so enjoyable and so informative. Thank you for taking the time again, for listeners who would like to find out more about the work you're doing, there's the website again. I'll spell it. It's with an E. Impromptu. E M, P R O M P T U AI Other places folks should go. Social media research papers.

B (38:46)

Yeah, absolutely. You can follow me on LinkedIn. I do a lot of yapping about my thoughts all the time.

A (38:53)

Perfect.

B (38:53)

I just started a TikTok about fun behind the scenes of building an AI company with AI.

A (39:00)

Oh, cool.

B (39:01)

And so you can follow us there and we'll be publishing lots of things coming out really soon in the near future.

A (39:08)

Great. And the TikTok is under impromptu.

B (39:12)

Shanaya. S H A N E A.

A (39:14)

Perfect. Well, Shania, again, thanks for taking the time. This has been great. And I, for one, look forward to keeping track of what you and Sean are up to, because certainly the outcomes and everything you're talking about on the technical side, but the approach that you bring to it, I think, is it's a great one to have and amplify in the world right now. So thank you.

B (39:36)

Thank you so much for having me.