No Priors Ep. 97 | With Decagon CEO and Co-Founder Jesse Zhang

(Robot sound) Hello, and welcome to No Priors. Today, I'm talking with Jesse Zhang, co-founder of Decagon. Decagon is an early stage company building enterprise grade generative AI for customer support. Founded in August of 2023, their platform is already being used by large enterprises and fast-growing startups like Rippling, Notion, Duolingo, ClassPass, Eventbrite, Vanta, and more. Jesse, welcome to No Priors.

Of course. Thanks for having me, Elisha.

Absolutely. Maybe we can start a little bit, um, with sort of your background and what Decagon does. You know, you're a serial founder. You started another company before this, Syntactic Bot, and, um, you know, now y- you and Ashwin have started Decagon, and you've been working on it for a while and have seen some really interesting adoption from companies like Rippling, Notion, Eventbright, Vanta, Substack, and many others, right? So you- you've really started to carve out a real, um, space for the company. Could you tell us a little bit more about what Decagon got- does, how it works, what the focus is of the company?

Of course, yeah. So quick background on me. Um, grew up in Boulder. Did a lot of math contests, stuff like that, growing up. Studied CS at Harvard. As you mentioned, started a company right out of school. Uh, that company was eventually bought by Niantic, and then I left to start this company. Uh, Ashwin and I, we- we met through mutual friends, um, officially met at this, uh, VC offsite, and when we got together, we were like, okay, um, biggest learning from first company is that can't really overthink things too much. We, um, we started by just kind of obviously being interested in AI agents. It's very exciting technology, arguably like the coolest thing from- from this generation. And, uh, we just, you know, talked to a bunch of customers like the ones you listed. Um, we, I think, over the years have gotten a lot better at figuring out, you know, how to talk to folks and, you know, how- what's- what questions to ask, and, uh, through that process, we kind of arrived at, you know, our current use case as maybe what we think is like the golden use case for- for these AI agents, which is customer interactions, customer service. Um, the- the use case is very tailor-made for- for what LLMs are good at. And so we started building from there, right? And we still w- weren't thinking too much about, you know, division or anything yet. It was just like, all right, we had a lot of customers in front of us. How can we make it so that they're happy and, uh, they really like what we're building? And then that led to kind of where we're at now. I would say right now, as a company, Decagon, we ship these AI agents for folks to use on the customer service, customer experience side. The thing that's made us special so far is, um, we have a huge sort of focus on transparency, I guess, so when people use us, especially these larger companies, it's- it's very important for them that the AI agent is not a black box, that they feel like, okay, even though LLMs are cool and, like, you know, there's- there's a lot of things you can do with them that they can see how decisions are being made, like what data is being used, how you come up with answers, and if I wanted to get feedback, I can, that sort of thing. So currently, we're in production with a bunch of these- these large folks that have large support teams. Um, pretty much any company that has a large sizable support operation is- is a good fit for us.