That new financing values the company, which makes AI customer service agents, at $15.8 billion, CNBC reported Monday (May 6).
Sierra was founded three years ago by OpenAI Chairman and former Salesforce Co-CEO Bret Taylor, along with former Google executive Clay Bavor. Taylor told CNBC the company employs a “constellation of models” as well its own fine-tuned proprietary layers.
The company said it surpassed $150 million in annual recurring revenue, or ARR, in eight quarters. Taylor said that the growth timeline is unprecedented in traditional software and underlines “intense demand in the market.”
“There’s a really big addressable market and immediate opportunity,” Taylor said. “We’ve sort of digitized the last remaining analog channel, which is the telephone line — it’s a better experience. You don’t need to wait on hold. These agents are naturally multilingual.”
Taylor estimated that $400 billion is spent annually on customer service, with most of that shifting to AI agents.
Advertisement: Scroll to Continue
Sierra was valued at $10 billion last year after raising $350 million. According to the CNBC report, its customers include the likes of Prudential, Cigna, Blue Cross Blue Shield and Rocket Mortgage, as well as a third of the world’s biggest banks.
In other agentic AI news, PYMNTS wrote recently about the way the technology has shifted from “frontier technology to operational table stakes,” as illustrated by recent earnings calls from players such as Visa and marketplace announcements from companies like Square and Ramp.
“Agentic AI represents a shift from tools that inform decisions to systems that execute them. For CFOs, this changes the calculus,” that report said. “The question is no longer whether artificial intelligence can improve finance operations, but whether it can do so within a framework of control and accountability.”
This is where the “agentic AI harness” comes into play. Though the term might seem technical, its implications are deeply operational. The harness is not the model itself, but the system that dictates how models function in the real world.
“It defines what an AI agent can access, what it is allowed to do, how it is monitored and when it must defer to a human,” PYMNTS added. “For chief financial officers, understanding this layer is becoming as important as understanding internal controls or capital allocation.”
