Long Bootstrapped Journey into Cutting-Edge Generative AI: Jeff Kuo, Founder of Ragic (Part 2)

Sramana Mitra: What was the project? What was Ragic and your master’s thesis that you were trying to productize?

Jeff Kuo: I was studying semantic web and AI-related subjects. Back then, Semantic Web was quite popular. Ragic came out of Semantic Web to map information data in a graph-based model so that it can be shared between different organizations.

Long Bootstrapped Journey into Cutting-Edge Generative AI: Jeff Kuo, Founder of Ragic (Part 1)

Businesses are finding unexpected benefits by incorporating Generative AI into their product roadmaps. Ragic has built a $5M ARR no-code platform in the market. A Generative AI front-end is adding unprecedented usability and adoption momentum. Fascinating!

Sramana Mitra: Alright, Jeff, let’s go to the very beginning of your journey. Where are you from? Where were you born, raised? What kind of background?

Jeff Kuo: I was born and raised in Taipei, Taiwan. I mostly grew up here, but in my childhood, I lived for two years in the US in Michigan.

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 7)

Sramana Mitra: It’s interesting. Now, where to from here? You said that 2023 was great. You’ve been doubling every year. It’s almost venture scale growth without raising huge amounts of money, which is all fabulous. What are you trying to accomplish and how do you want to play this?

John Wallace: Next, it must be, first and foremost, more of the same. We don’t have to let go of the horse that got us here.

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 6)

Sramana Mitra: So, a fraction of their media spend is your business model. What kind of deal sizes are we talking and, and what do you need to sell? In the pandemic, I heard from a lot of people that they were able to close deals without having to meet people. Even very large deals. They were closing without having to meet people. So what was the model of actually selling these engagements?

John Wallace: Yes. The media plans that run through LiftLab are almost an order of magnitude from small to big. So, the prices just adjust to that.

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 5)

Sramana Mitra: How long did it take you to build out all the specs that you were getting from these customers?

John Wallace: We were experimenting only for about a year and a half. Then we spent probably another nine months building the modelling platform.

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 4)

Sramana Mitra: So, what you’re saying is that you got this input about what the market was looking for, and because they were paying customers on the first piece of the functionality, you were able to get them to give you access to data. Because in all of this, as you know, in building AI products, access to data is one of the big gating items so that you can develop anything without problems.

John Wallace: Yes. We cannot build these products in a laboratory. We need to do it in the trenches with customer data.

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 3)

Sramana Mitra: Now, let’s double click down on the concept of experiments. Once you were in these POC situations and started to gain some traction, what kinds of experiments are customers running?

Building a Capital Efficient Second Venture: LiftLab CEO John Wallace (Part 2)

Sramana Mitra: So let’s double click down on a couple of things here. You mentioned a different dataset. So, what dataset is LiftLab using?

John Wallace: In our last company, we were essentially working with user level data, device level data, and log data. Now, we’re working with aggregated data. So we’re working with time series, and the methods are much more around econometrics. So it, it was a new data set and new algorithms.

Sramana Mitra: And what, what is the source of this data?