Empromtu AI: Democratizing Production-Ready AI Development

Key Quotes

"I studied business and computer science and then I spent some time at Google working on developer tools for Google Home and Android, helping build Android applications for millions of developers all over the world. And then I spent some time at eBay working on both ads and more traditional machine learning, which was super fun. And then I got tired of, let's just, you know, working at big companies, so I was like, I'm going to try this startup thing."

Shanae Leven describes her early career path, highlighting her experience at major tech companies like Google and eBay. This quote establishes her technical foundation and her eventual desire to move into the startup environment, setting the stage for her entrepreneurial journey.

"And then I ended up really starting off building just a very simple app with Luvable. Tried to do that and of course, being the technical person that I am, I really broke it. I just full, full-on broke it. And then I put a pause on that. I ended up taking a role for a short amount of time at a subsidiary of Fox Sports to help them with their AI. And right around the same time, I ended up meeting Sean, Dr. Sean Robinson, who is my co-founder."

Leven recounts a personal experience of breaking a simple app, which led her to pause her solo venture and eventually meet her co-founder. This anecdote illustrates the challenges of AI development and the serendipitous nature of finding a collaborator with complementary expertise.

"And he ended up saying, 'I invented this thing to get up to 98% accurate outputs out of AI.' And I looked at him and I said, 'What? Wait, what? Like, you can get up to 98% and we can bottle this immediately?' Yeah, like, what is going on? And so I was like, I needed that. I needed that in a number of places. At Codey, it's really hard to get code like, right? Really hard to get AI outputs to be accurate. And that is the next frontier."

This quote captures Leven's immediate reaction to her co-founder's claim of achieving 98% accuracy in AI outputs. Her astonishment and recognition of the critical need for accuracy underscore the significance of their discovery and its potential to address a major challenge in the AI field.

"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?' And that was always the goal, like, how do we make sure that we get good outputs?"

Leven explains the initial, less ambitious origins of their company, which focused on using their co-founder's optimization technology to help clients build AI applications. This highlights their core mission of improving the speed and accuracy of AI development, even before envisioning a large-scale platform.

"The mission of our company is to make AI accurate and accessible. 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 talk to 20-year developers, like, people have been developing for 20 years, and this is a new technology that we're all learning at the same time."

Leven articulates the dual mission of Empromptu AI: to ensure accuracy and accessibility in AI. She clarifies that accessibility extends beyond those with no coding experience, encompassing even seasoned developers who are navigating the evolving landscape of AI technology.

"We consider Empromptu 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, you know, parts of it are essentially, you know, a big embedding and classification machine. So every time, you know, we map a new feature or iterate a 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."

Leven describes Empromptu's approach as a "mixed-code builder," differentiating it from traditional no-code or pro-code platforms. She explains that at its core, the system relies on embedding and classification processes, which involve converting data into vectors for decision-making.

"And that really gives us two big wins, right? First, it's performance. So like, all of the everyday creators in our UI get that instant feedback. They can, you know, request a feature or upload or tweak a prompt or, and the AI just kind of responds, and so they can iterate much faster, ship more, much more useful products. And then the second thing is efficiency, right? So CUDA lets us serve a ton of those workloads in like a relatively small GPU footprint."

Leven details the benefits of using NVIDIA's CUDA libraries, emphasizing performance and efficiency. She explains that CUDA enables instant feedback for users, allowing for faster iteration and the development of more useful products, while also optimizing resource utilization.