Decagon

“We know that companies like Harvey and Decagon are really fine tuning models now.”

“Decagon... helped support Chime's cost-to-serve reductions.”

“it's increasingly bumping into customers, including Decagon, a conversational AI platform that trained its voice product on ElevenLabs and now competes with it.”

“moats are most often discovered, not designed.”

“"Decagon has done an amazing job at this. Like, I think they go in, they basically evangelize that they're doing customer support better than anyone else. And then they're very good about saying here are the benchmarks that we are signing up to hit and then they hit them."”

“Decagon has done an amazing job at this. Like, I think they go in, they basically evangelize that they're doing customer support better than anyone else. And then they're very good about saying here are the benchmarks that we are signing up to hit and then they hit them.”

“Ramp (~$1.4B ARR) and Decagon (~$35M ARR) bracketing the group at the high and low end”

“investors include Bain Capital Ventures, Accel, and a16z”

“Decagon has crossed $100 million in annualized revenue”

“Decagon's differentiation strategy centers on deployment speed and ease of customization rather than headcount or funding.”

“This is what Cursor did. This is what sort of Decagon and Harvey are in the process of doing now.”

“If you look at the markets that Harvey or Open Evidence or Decagon or any of these folks at Sierra entered, you know, four or five years ago, three or four years ago... it was big, big markets that could be in the roadmaps of these labs.”

“Even if you have a, quote, dumber model, you can get it to higher performance on that specific task. So when we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models. We end up getting all three things. It is better at the task, it is cheaper, and it is faster.”

“Decagon shifted 90% of its workflow to open source models, but the driver was latency and control for voice agents — not cost reduction.”

“I actually spent a while building agents for myself to capture all the business context well so it just kind of looks over my shoulder all the time and is constantly compiling context — here are the people that we've hired, here are the deals that we're working on, here are the current challenges that we have.”

“The reasoning models mostly for like the Claude Codes of the world, but they are also really good for Duet, for example.”

“Within basically a month, they spun up like seven new journeys on Decagon. And it'd taken a year to get three. So it's just kind of the speed of iteration.”

“Decagon shifted 90% of its workflow to open source models, but the driver was latency and control for voice agents — not cost reduction. Cost savings were a 'nice side effect.'”

“An AI agent should just be the front door of your business or your brand. And every interaction, whether it's reactive or proactive with a customer, should be handled by AI.”

“Salesforce is very horizontal, Zendesk is very horizontal — all these solutions are very horizontal because you just gain more from having that scale and having a very robust and deep product than you do from having very vertical-specific features.”

“If you're a Decagon and you're a startup doing inference on customer support queries for a very boring consumer product, there is no reason why you should pay marquee prices when something 10x cheaper is available. And it would be horribly bad policy to ban that.”

“I think 90% of Decagon's workloads run on open source models. And that is because they are at kind of production scale.”

“We're investors in Decagon, customer support. Like you can pretty easily see a business model that is priced on the resolution of a task because it's so measurable.”

“In Zhang's telling, they aren't competitors, and open source models' success isn't coming at the expense of frontier labs. Instead, they're two phases of the same life cycle, with expensive frontier models being used to prove out use cases that can be passed along to cheaper open source alternatives as they mature.”

“reflecting traction from AI support products like Sierra ($15.8B) and Decagon ($4.5B).”

“AI-native companies including Cursor, Decagon, LangChain, and OpenAI are hiring SDRs at 50% higher rates relative to their GTM mix versus all digital natives.”

“Customer support saw the steepest decline of any GTM role, down 37%, reflecting traction from AI support products like Sierra ($15.8B) and Decagon ($4.5B).”

“Decagon ($1.5B)... The switching cost lives in the workflow integration and the data corpus. The prompts are irrelevant.”

“Decagon cited as an example of a company building specialized workflow intelligence through multi-step support task sequences.”

“Decagon, a voice agent startup that raised $250 million at a $4.5 billion valuation in January”

“Hunter Douglas, a multinational window treatment company that began using Decagon voice agents to handle customer service calls about 6 months ago”

“Jesse Zhang, founder & CEO, Decagon”