Inspired with Alexa von Tobel
00:09:33 · May Habib
[00:09:33] S0: Yeah. You know, really, like, going into a corporate job versus not starting a company as you did right right out of college was kind of the first big, like, fork in the road. And, you know, for me, I think... And I I don't know if it was the right decision or not. You know, Mark was classmate. I know his contemporary of yours. He was a year above me. I wrote the first Crimson article on Facebook. I started a small e commerce company in college with a couple classmates that we folded during college. But, you know, the entrepreneurial, you know, bug spirit, etcetera, like, was... It's just always been there. I think the, you know, the corporate jobs were the detour, not the entrepreneurship. But I I did end up going to Lehman Brothers and then Mubadala. And while at Mubadala, covered semiconductors and this little chip startup called Nvidia that I kept trying to get us to invest fifteen years ago. We really should've. And... Yeah. And, you know, it was a... Like, a now or never kinda thing. Like, I really... This is... These these jobs are so interesting. Right? And, like, you get to do such high stakes stuff. But I knew that if I didn't leave and really start, you know, start something, I never would. Like, they would just keep throwing more money, more responsibility. Right? More perks at you. And And so it wasn't like, gosh, I had already started something. But I really felt like I just needed to go and pursue this. And especially meeting Waseem, I mean, that was really the game changer for me. And, you know, it had been really a few years of kind of, like, batting around ideas, you know, kind of, like, founder dating. Right? Like, both, you know, kind of, like, ostensibly and figuratively, right, in terms of like seeing and trying to understand what made other founder partnerships work, right, that I was observing friends who, you know, had started companies and things that had really worked, things that hadn't. And so when I met Wassim, you know, we were such different people, but it all just kind of clicked together. Like the timing and the person. And so we moved to San Francisco. We were both in Dubai in early twenty fifteen, and started started Qordoba.
00:12:01 · Alexa von Tobel
[00:12:01] S1: I wanna talk a little bit about Qordoba morphing into Writer, and then I wanna talk about everything the Writer's doing because it's fascinating, and you're really on the edge of a movement, quite literally. Quickly talk about the decision to change the name and just give us a sense of what that happened. And and and I wanna give you the chance to talk about that because I think, you know, for all founders out there thinking, and I like to discuss the things that are not typical because I think it just helps people realize now you're a $2,000,000,000 plus and growing rapidly company, things don't always come perfectly easily. So talk a little bit about the the decision to to to transform into Writer.
00:12:45 · May Habib
[00:12:45] S0: So our our first company was focused on machine translation. And it's so funny because we we do a lot of translation now. Right? Writer is an agent platform for front office, and a lot of what people do when they go to market is, you know, modified by channel, modified by language. But, you know, back then, I mean, we were building statistical machine translation models. And even in some of our first, you know, more like, you know, just deeper machine learning models that we built on translation, we had different models for, like, one language direction than another language direction. Right? Like, this was, like, very, very primitive early days. And the big inflection point for us was the models we were building being able to generate English language. And that, you know, I remember the first all hands. We were across the street from where we are in San Francisco now. The first all hands where we presented a haiku on a slide that one of our models had had written. And it was like, what the fuck is this? And and that's a very different business, right, in terms of, you know, translation to, we called it, source language, right, creation. And so, in an ideal world, we would have sold the first company and that would have been like a very clean exit, and then we would have started our first, our second company. But it really didn't happen that way. And me and Wassim just had such high conviction that we needed to run this transformer thing. You know, these were encoder decoders. And we were solving, like, just a completely different problem. And, like, you know, we didn't know what the product would look like. We didn't know who the buyers would be. You know, we were even, like, we would take long walks and try to think about and imagine we were selling near the enterprise in our first company. Like, should this be a consumer company? Should this be a prosumer company? And after ChatGPT I thought, oh fuck. Like for a few months it was like, maybe we should have done consumer. Now I am very happy, right, that we are pure play and just so focused and so successful on delivering you know, really unmatched value to the enterprise. But that gives you an idea of just like, you know, how differently Mean Wasine thought about this new technology and how different it felt from what we were doing before.
00:15:15 · May Habib
And, you know, now I am very excited, right, that we get to serve so much of our original founding mission of Qordoba around, you know, really making language a non issue for people and for businesses. But it did feel like we were abandoning something that we had, A, worked really hard for and that we had raised money on, promised a lot of people things, and also made a lot of promises to customers. But you know, really over the course of about an eighteen month period, right, we've we've sunset all of that revenue and really built Writer from from the ground up. It definitely meant also from a people perspective, right, we rebuilt the team and started something new. And I am really grateful that we made the jump when we did because I I think, you know, now we are the only pure play full stack generative AI company in the enterprise that's not a hyperscaler or like a massively funded research lab. And so much of that is because, you know, we were able to spend a couple years before the hype cycle really focused and really the only player in town in terms of what we were able to offer the enterprise. And so we've been able to, you know, just bring, like, just build customer reference after customer reference in highly regulated industries doing, you know, pretty complex agentic implementations now. But it's a five year history of being able to serve the enterprise with AI. And it was a tough transition. It was definitely a tough first year or two in terms of like just the eating glass pane of transitioning customers and humans, right, out of the business. But you know, by early twenty one, right, we had really found our way as Writer and raised that first A, that first kind of big round in the fall of twenty twenty one, just as generative AI was becoming a thing. So yeah, I'm grateful for the whole journey, but it's been ten years in natural language processing and machine learning to get here. And now I get to talk about our research road map when I meet customers and potential partners. And, you know, there's just so much depth having really been a team that's been in so many innings, right, of this revolution and having the depth of being the only research lab that, you know, for years has focused on problems that real companies have. And, yeah, that started a decade ago. I love that. I'm
00:17:45 · Alexa von Tobel
[00:17:45] S1: on it in plain English for everybody out there, and obviously, Writer is involved. What is... What problem is Writer solving? And give us a sense of who your customers are, and what does that look like?
00:17:55 · May Habib
[00:17:55] S0: Yeah. So Writer wins in AI when there are two or three requirements that a company has. When a use case requires high precision, high quality, high consistency and reliability. Right? People choose Writer. A lot of these use cases tend to be front office, tend to be sales and marketing. Right? If you are now really semi automating the way a wealth advisor communicates to a client after they have a sit down meeting, right? Everybody records calls now. We've got a great CRM. It's captured everything. But really being able to understand what does Alexa need? What are Alexa's financial goals? How do I bring the best of what I know about Alexa with really what my organized knows about Alexa. Right? A a lot of what we do is make explicit what the organization knows implicitly, but doesn't apply consistently. And the thing with other AI tools is you can go ask Copilot or ChatGPT, you know, the same question asked 50 different ways by 50 different people, and you will get, you know, 48 or 49 slightly different answers. And that's just not... That doesn't pass the bar, right, in these regulated industries. And so in healthcare, in financial services, in CPG, there are millions of use cases like this, right? Micro use cases. But they all really ladder up to better high end customer support, customer service. They all ladder up into making relationship selling much more productive, much more consistent, better user outcomes, better company outcomes. And so much of the front office is really calcified into these systems of record that don't talk to each other and produce, like, pretty rigid business rules. But so much of this type of selling is dependent on unstructured information, unstructured know how. Right? And the beauty of AgenTik AI is it's really good at the unstructured. And so, you know, we really shine in these kinds of use cases. Most of our customers are in highly regulated industries. But we've got a lot of technology customers too. Microsoft, Salesforce, Nvidia, HubSpot, Qualcomm. Lots of folks who build their own AI use Writer in front office because a much more scalable solution. Because if you are now going to automate something, if you're gonna gentify something, you've gotta be pretty certain that the guardrails are there, right, for you to get the kind of outcome that you need. One of