One red-hot category within the generative AI space is customer support, which isn’t surprising, really, once you consider the tech’s potential to chop contact center costs while increasing scale. Critics argue that generative AI-powered customer support tech could depress wages, result in layoffs and ultimately deliver a more error-prone end-user experience. Proponents, alternatively, say that generative AI will augment — not replace — staff, while enabling them to concentrate on more meaningful tasks.
Jesse Zhang is within the proponents camp. After all, he’s just a little biased. Together with Ashwin Sreenivas, Zhang co-founded Decagon, a generative AI platform to automate various elements of customer support channels.
Zhang is well aware of how stiff the competition is out there for AI-powered customer support, which spans not only tech giants like Google and Amazon but startups resembling Parloa, Retell AI and Cognigy (which recently raised $100 million). By one estimate, the sector may very well be price $2.89 billion by 2032, up from $308.4 million in 2022.
But Zhang thinks that each Decagon’s engineering expertise and go-to-market approach give it a bonus. “After we first began, the prevailing advice we received was to not pursue the client support space, since it was too crowded,” Zhang told TechCrunch. “Ultimately, the thing that worked for us was to aggressively prioritize what customers wanted and maintain laser concentrate on what customers would get value from. That’s the difference between an actual business and a flashy AI demo.”
Each Zhang and Sreenivas have technical backgrounds, having worked at each startups and bigger tech orgs. Zhang was a software engineer at Google before becoming a trader at Citadel, the market-making firm, and founding Lowkey, a social gaming platform that was acquired by Pokémon GO maker Niantic in 2021. Sreenivas was a deployment strategist at Palantir before co-founding computer vision startup Helia, which he sold to unicorn Scale AI in 2020.
Decagon, which sells primarily to enterprises and “high-growth” startups, develops what amount to customer support chatbots. The bots, driven by first- and third-party AI models, are fine-tunable, able to ingesting a businesses’ knowledge bases and historical customer conversations to achieve greater contextual understanding of issues.
“As we began constructing, we realized that ‘human-like bots’ entails lots, since human agents are able to complex reasoning, taking actions and analyzing conversations after the very fact,” Zhang said. “From talking to customers, it’s clear that while everyone wants greater operational efficiency, it cannot come on the expense of customer experience — nobody likes chatbots.”
So how aren’t Decagon’s bots like traditional chatbots? Well, Zhang says they learn from past conversations and feedback. Perhaps more importantly, they will integrate with other apps to take actions on behalf of the client or agent, like processing a refund, categorizing an incoming message or helping write a support article.
On the back end, firms get analytics and control over Decagon’s bots and their conversations.
“Human agents are able to investigate conversations to note trends and find improvements,” Zhang said. “Our AI-powered analytics dashboard routinely reviews and tags customer conversations to discover themes, flag anomalies and suggest additions to their knowledge base to raised address customer inquiries.”
Now, generative AI has a fame for being, well, lower than perfect — and, in some cases, ethically compromised. What would Zhang say to firms wary that Decagon’s bots will tell someone to eat glue or write an article stuffed with plagiarized content, or that Decagon will train its in-house models on their data?
Principally, he says, don’t worry. “Providing customers with the crucial guardrails and monitoring for his or her AI agents has been necessary,” he said. “We optimize our models for our customers, but we do that in a way which ensures that it’s unattainable for any data to be inadvertently exposed to a different customer. For example, a model that generates a solution for customer A would never have any exposure to data from customer B.”
Decagon’s tech — while subject to the identical limitations as every other generative AI-powered app on the market — has been attracting name-brand clients as of late, like Eventbrite, Bilt and Substack, helping Decagon to achieve break-even. Notable investors have climbed aboard the enterprise, too, including Box CEO Aaron Levie, Airtable CEO Howie Liu and Lattice CEO Jack Altman.
Thus far, Decagon has raised $35 million across seed and Series A rounds that had participation from Andreessen Horowitz, Accel (which led the Series A), A* and entrepreneur Elad Gil. Zhang says that the money is being put toward product development and expanding Decagon’s San Francisco-based workforce.
“A key challenge is that customers equate AI agents to previous generation chatbots, which don’t actually get the job done,” Zhang said. “The shopper support market is saturated with older chatbots, which have eroded lost consumer trust. Latest solutions from this generation must cut through the noise of the incumbents.”
