Decagon raises Series C at $1.5B to deploy AI customer service agents for large consumer businesses

ever since I got ever since I got sick, I've been I've been trailing you by by like 10 points off your game. Well, Jesse from Decagon in the studio. He's certainly been on his game. How you doing, Jesse? What's going on? Welcome to the stream. Good to see you. Thanks for having me. Yeah, thanks for hopping on.

Uh, give us the news. Uh, what does the company do and what's the breaking news with you? Yeah. Hey everyone watching. I'm Jesse. I'm one of the co-founders of Decagon. If you don't know about us, we're essentially an AI customer service agent.

And so what this means is, you know, imagine next time you're, you know, booking a hotel room or something, right? There's an AI agent that's there that you can call or or chat with live and it knows everything about you. It can book rooms for you. It can upgrade.

It can answer questions about your loy loyalty points and and so on.

And so uh essentially what we do is we work with these large you know usually consumer businesses that have a lot of customers and a lot of you know contact center volume and we're there to both you know drive a ton of efficiency for them because now they're human agents don't have to deal with these more mundane issues uh but also we are able to give a better customer experience for the uh the actual customers.

So, that's what we do. And then today, we're very excited to announce our series C-led by A16Z and Excel at a 1. 5. Let's go. Give it a bigger hit, John. Oh, there we go. Um, talk to me about the early the early uh beach head for the company.

I feel like uh LLMs are so generalizable and yet uh go to market is so specific. Every vertical industry has slightly different customer service needs, whether it's a Shopify store that needs access to an inventory management system or refunds or Stripe versus a SAS product. What has been most interesting to you?

What's been uh where where has adoption been the strongest early on? Yeah, it's a good question. So I would say that with most of the use cases out there, the the ones that have, you know, had the had the most impact from AI has been ones where there is a little bit of complexity, right?

Where where you need the AI to go and take actions for you or look up data for you.

And it's kind of adding this extra layer uh beyond what you know traditional chat bots have been able to do because I'm sure both of you have used chatbots in the past and usually it's a pretty frustrating experience because it's it's horrible.

Yesterday I was trying to cancel uh I was trying to cancel Spectrum Internet at my at our old office and good luck and couldn't do it on the website. This is 100% deliberate by them by the way. Couldn't do it on the website. Of course, like you know this is a CRUD app.

I should be able to click a button and update the You have to wonder if it's intentional. It's 100% intentional. They broke one password so I couldn't log in. Finally, they say, "Hey, text us. " And I'm texting with their chatbot.

It texts me natural language, but then asks me to click a drop down for two different options every single time. Finally get through all that flow and they're like, "You need to call us. And by the way, we're closed today. " It was awful. It was the worst. Yeah.

Is is that is that is that some maybe friction in the sales process where people are like, "I get it that you want to make this more frictionless for our customers, but you're going to increase our churn, you know, 10%. I don't know if we should sign up. " Are there actually trade-offs there? I'm super interested. Yeah.

No, that that is that is one use case, right? So to to your question, what use cases are good? It is where you need a little bit of complexity because in the old days, the reason why you can't do that today is because it's really easy to game that system.

Like sure, they made a decision tree, then people, someone would make a Reddit post about, hey, here's how you actually cancel easily and everyone will just do it immediately and it impacts your revenue, right? So the nice thing about LM now and the thing that it really unlocks is this level of nuance.

you're able to create much more complex flows and sort of situations that are tailored to specific users. And because of that, you're you're able to cover way more ground, right? So, we have customers, for example, that use us to process refunds.

We have customers that use us to, you know, uh either book flights or reorder a credit card or or things like that. And that's really powerful because it just wasn't possible before, right?

before you're kind of in this more, you know, dumb decision tree that you have to get forced down a certain path and maybe that path is kind of what you want, but you want like something slightly different and you're just forced to go down the path. You get stuck and you get escalated, right?

So, that that's one of the main benefits of of these LLMs. Last question from my side.

I know we're we're going crazy on the timing, but um the in my previous jobs when I've hired a really allstar customer service agent, the thing that's always impressed me is when they're able to expand out of their role as just customer service, just doing specific flows and actually turn into sort of a salesperson and actually uh turn an interaction into customer education or upselling or cross-selling or uh you know, hey, yes, you said you wanted to cancel, but I think you want to cancel for a reason that we can actually alleviate by switching you around or giving you a discount or doing something like that.

Some of that can be deterministic. We've seen this with like uh are you sure you want to cancel? We'll give you 20% off. But the real best customer service agents tend to do it with more touch. Is that something that you're seeing already being able to be handled by LLMs?

Do you think that's coming with more agent workflows? Um how do you see the role of the customer service agent or human evolving over the next couple years?

We work one with our customers a lot on that exact concept which is what is the definition of customer experience over time because when you start you you're generally working on the lowhanging fruit like you know processing transactions or looking up information and FAQ right right exactly but if you end up having a really really good AI experience and if you if you just think about it with the products in your life right you're just going to interact with it more like it's not that you're going to it's like oh I'm done with this if it's a good experience you're going to interact with it a lot more and that opens up a much broader surface area for what customer experience means.

You can start being proactive instead of just reactive. Uh you can start looking at revenue generating use cases like you just mentioned, right? And it doesn't just have to be a cost center, which is what a lot of enterprises look at customer service, right?

It's like, oh, it's this thing that it's not really strategic to us. We're just going to put it off to the side and try to save as much money as possible. Yeah. AI is changing a lot of that.

And you know a lot of the the customers that use Deagon are super excited by this concept that hey if you have a good system there it almost becomes essentially a concierge for your product right it becomes like a a new UI that customers can use to interact with you because if it's really good and it can do everything like yeah maybe people are just chatting with it instead of using an app or kind of going through a bunch of you know drop downs and stuff like that.

So yeah, what's happening in the call? Sorry to interrupt. What's happening in the call center BO market right now? Is there already pretty massive impact from companies like DecaGon, Intercom, Sierra, etc.? The Yeah, the call call center market is definitely thinking about this space a lot as you can see, right?

When u when AI first came out, a lot of their stocks went down by quite a bit because it's it's one of the most obvious use cases for for AI.

I would say the the best BPOS out there are are really figuring out how to become AI native and either partnering with solutions like us or spending a lot of effort in in building their own.

I think partnership would probably be the realistic path because when customers when end customers buy these services they they usually kind of see BPOS as a service provider. So there is room for partnership but that's typically the way way it'll happen. Yeah.

I mean, just the advent of software as a service or just websites in some ways reduced the amount of humans in a sales interaction because you could go through the Shopify checkout by yourself and yet there was still a role for the humans. So, I'm sure that will be evolving. Um, but it's exciting stuff.

Thank you so much for stopping by. Congrats on the round and congrats on uh picking up uh Max Scaria a couple months ago, star sales leader. I went to uh I went to college with Max.