Decagon Takes Lead in AI-Driven Customer Support

Customer support is a rapidly growing category in the generative AI space, which isn’t surprising, considering its potential to reduce contact center costs while increasing scalability. Critics argue that generative AI-powered customer support could lower wages, cause layoffs, and result in a more error-prone user experience. However, proponents believe that generative AI will enhance, rather than replace, human workers, allowing them to concentrate on more meaningful tasks.

Jesse Zhang is among the proponents, and understandably so. Along with Ashwin Sreenivas, Zhang co-founded Decagon, a generative AI platform designed to automate various aspects of customer support.

Zhang is well aware of the intense competition in the AI-powered customer support market, which includes tech giants like Google and Amazon, as well as startups like Parloa, Retell AI, and Cognigy (which recently secured $100 million in funding). The market for AI-powered customer support is projected to grow significantly, potentially reaching $2.89 billion by 2032, up from $308.4 million in 2022.

However, Zhang thinks that Decagon’s engineering expertise and go-to-market approach are advantageous. Zhang said, “When we first started, the prevailing advice we received was to not pursue the customer support space, because it was too crowded,” He continued, “Ultimately, the thing that worked for us was to aggressively prioritize what customers wanted and maintain laser focus on what customers would get value from. That’s the difference between a real business and a flashy AI demo.”

Both Zhang and Sreenivas bring strong technical backgrounds and experience from both startups and large tech organizations to Decagon. Zhang previously worked as a software engineer at Google, transitioned to a trader role at Citadel, and later founded Lowkey, a social gaming platform acquired by Pokémon GO creator Niantic in 2021. Sreenivas, on the other hand, was a deployment strategist at Palantir before co-founding the computer vision startup Helia, which he sold to the unicorn Scale AI in 2020.

Decagon primarily serves enterprises and high-growth startups by developing advanced customer support chatbots. These bots, powered by both first- and third-party AI models, are highly customizable. They can assimilate a business’s knowledge bases and historical customer conversations, enabling them to understand and address customer issues with greater contextual accuracy.

Read Customer Support Automation of Increased Customer Satisfaction

“As we started building, we realized that ‘human-like bots’ entails a lot, since human agents are capable of complex reasoning, taking actions and analyzing conversations after the fact. From talking to customers, it’s clear that while everyone wants greater operational efficiency, it cannot come at the expense of customer experience, no one likes chatbots.” said Zhang.

Decagon uses generative AI

Decagon uses generative AI tech to respond to customer queries

So how are Decagon’s bots different from traditional chatbots? According to Zhang, these bots learn from past conversations and feedback. More importantly, they can integrate with other applications to perform actions on behalf of the customer or agent, such as processing refunds, categorizing incoming messages, or assisting in writing support articles.

From the company’s perspective, Decagon provides comprehensive analytics and full control over the bots and their interactions, ensuring a tailored and efficient customer support experience.

“Human agents are able to analyze conversations to notice trends and find improvements. Our AI-powered analytics dashboard automatically reviews and tags customer conversations to identify themes, flag anomalies and suggest additions to their knowledge base to better address customer inquiries.” said Zhang in a statement.

Generative AI often gets a bad rap for being imperfect and sometimes ethically dubious. Companies might fear that Decagon’s bots could make inappropriate recommendations, like telling someone to eat glue or generate plagiarized content, or that Decagon might train its models using their data.

Zhang reassures that such concerns are unwarranted. “Providing customers with the necessary guardrails and monitoring for their AI agents has been crucial,” he said. “We optimize our models for our customers in a way that make it impossible for any data to be inadvertently exposed to another customer. For example, a model generating an answer for customer A would never have access to data from customer B.”

Despite sharing the inherent limitations of all generative AI applications, Decagon’s technology has recently attracted prominent clients such as Eventbrite, Bilt, and Substack, helping the company reach break-even. The venture has also drawn notable investors, including Box CEO Aaron Levie, Airtable CEO Howie Liu, and Lattice CEO Jack Altman.

Read 5 Key Features of the Best Customer Support Software

To date, Decagon has raised $35 million through seed and Series A rounds, with participation from Andreessen Horowitz, Accel (which led the Series A), A* Partners, and entrepreneur Elad Gil. According to Zhang, the funds are being allocated toward product development and expanding Decagon’s San Francisco-based workforce.

Zhang said, “A key challenge is that customers equate AI agents to previous generation chatbots, which don’t actually get the job done. The customer support market is saturated with older chatbots, which have eroded lost consumer trust. New solutions from this generation must cut through the noise of the incumbents.”