AI Agents Talking to AI Agents: Reinventing Commerce with Decagon CEO Jesse Zhang
episode
No Priors: Artificial Intelligence | Technology | Startups
31 min
1 speaker
6 chapters
transcribed 1 month ago
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What is Decagon and how does it use AI agents for customer service?
Today we're lucky to have with us on NoPriors, Jesse Zhang. Jesse is the co-founder and CEO of Decagon, which provides customer service and other related AI for all sorts of different enterprises, including banks, telecom providers, airlines, and of course many of the biggest and most important tech companies. Jesse Prior started Loki, which was acquired by Neantic, and we're very excited to have him join us today on No Priors. Jesse, thanks for joining us today on No Priors. Thanks for having me. about Decagon and why you started the company, how you started it, how you all got going.
Yeah, of course. So Tekugon, for those who are not really familiar with us, we're an AI customer service agent. And so you can kind of think of us, you know, if we're working with a large bank or airline or just people that have large contact volume, the AI's job is to, you know, have a very engaging and personalized conversation with the user and resolve it and, you know, save the the company a bunch of money and, you know, ideally drive more revenue in the future because folks are more engaged. And as we as we've grown, it's kind of becoming more and more of a you can think of like a conversational UI for the brand where it's it's how every user can interact with it. And we often use the term like concierge to describe this.
But um that's what we do.
And you're working right now with some big banks or some of the world's biggest banks, you're working with airlines, telcos, like you've actually gotten to very big customers very quickly. How did how did you go about doing that or how did it happen?
Yeah. So I mean, as you know, we started out mostly with the like digital native companies. A lot of startups do that. And digital natives, of course, are much more willing to try out startups. They can move faster. They could be like late stage tech companies and things like that. Yeah. Like uh like Ripling, Notion, folks like them were they were like great partners and they also just helped us iterate on the product a lot. So that's where we started. As we've gone on, I think just naturally we're kind of pulled up market just because of the demand. And And as you might imagine, those that's where most of the large contact volumes are. So it just happened a lot faster than we thought. And I would say a lot of these enterprises also moved a lot faster than we would have expected.
Um, so that's that's why we ended up there. Mm-hmm.
I think that's one of the underappreciated things about AI traction is a lot of companies are willing to try things in a way they weren't willing to before because it's such a big technology shift. And so all these markets are kind of open now that weren't before or that would be much harder to do.
Yeah. Um, I mean another specific dynamic is that at the enterprise, it's becoming a lot more of a top down motion. So, you know, in the past any of these technologies could have been just like one team trying to vet it or decide it, but now it's like a it's an AI transformation and the C suite of the board are all like very big on how do we adopt AI. And, you know, customer service is often one of the biggest areas for probably the most low hanging fruit. So um that's how these conversations have progressed. And
how much of an impact are you having in terms of some of these teams? So I know that you're giving a lot of leverage to these customer service orgs. Like are you making people two times more productive? Or I'm just sort of curious, is there a way to measure the the outcome here?
Yeah. I mean, most of the large enterprises, they'll the first thing they'll measure is just what is the I guess like efficiency that you're getting them. So whatever they're spending on their contact center or their operation, how much can you cut that down by? And we've done case studies now where, you know, folks have been able to cut that down by, you know, sixty, seventy percent.
Oh wow.
That's like a great success case, right? Because it's like a very clear business case you can show it to everyone.
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Chapters
6 chapters
1
What is Decagon and how does it use AI agents for customer service?
0:05–5:22
2
How did Decagon land its first large enterprise customers?
5:22–9:36
3
What productivity gains do Decagon’s AI agents deliver for contact centers?
9:36–14:00
4
How does Decagon integrate AI agents into existing customer workflows and tech stacks?
14:00–15:53
5
What lessons did Jesse learn as a second‑time founder building Decagon?
15:53–20:36
6
What hiring philosophy and culture does Decagon use to scale its team?
20:36–31:20
Speakers
1 identifiedMore from No Priors: Artificial Intelligence | Technology | Startups
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