How AI Agents Are Transforming Customer Support, with Decagon’s Jesse Zhang

**Elad** (0:05)
Hello, and welcome to No Priors. Today, I'm talking to 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.

**Jesse Zhang** (0:28)
Of course, thanks for having me, Elad.

**Elad** (0:30)
Absolutely. Maybe we can start a little bit with sort of your background and what Decagon does. You're a serial founder. You started another company before this in Antikbot. And now 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, Eventbrite, Vanta, Substack, and many others. So you've really started to carve out a real space for the company. Can you tell us a little bit more about what Decagon does, how it works, what the focus is of the company?

**Jesse Zhang** (1:02)
Of course. Yeah. So quick background on me. I 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. That company was eventually bought by Niantic, and then I left to start this company. Oshman and I, we met through mutual friends, officially met at this VC offsite. When we got together, we were like, okay, biggest learning from first company is that can't really overthink things too much.
We started by just obviously being interested in AI agents. It's very exciting technology, arguably the coolest thing from this generation. We just talked to a bunch of customers like the ones you listed. We, I think over the years have gotten a lot better at figuring out how to talk to folks and what questions to ask. Through that process, we arrived at our current use case as maybe what we think is the golden use case for these AI agents, which is customer interactions, customer service. The use case is very tailor made for what LLMs are good at. We started building from there. We still weren't thinking too much about 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 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 we have a huge sort of focus on transparency, I guess. So when people use us, especially these larger companies, 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 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 do you come up with answers, and if I want 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. Pretty much any company that has a large size of all support operation is a good fit for us.

**Elad** (3:15)
That makes a lot of sense. It's interesting because I feel like one of the things that's been really striking over say the last year in the AI world is the CEO of Klarna posted on X or tweeted about the impact that AI has had on their customer support or service team. And Klarna is sort of like a buy now pay later service out of Europe.
And his tweet basically said in the first four weeks, they handled 2.3 million customer service chats. The customer satisfaction was on par with humans. It's 25% reduction in repeat inquiries relative to people. It resolved customer errands or issues in two minutes versus 11 minutes for a human agent. And instantly, they were live 24-7 in 23 markets and 35 languages because AI supports so many things. And so, you know, it had a huge impact on that company. And I think they sort of shifted 700 full time agents to do other work, right? In terms of the impact of Klarna itself as an organization. What sort of impact have you been seeing with your customers as they adopt this sort of technology? And how do you think through the lens of what you're really bringing to these customers and the sort of satisfaction that their own end users have?