Listen Labs has raised $69 million in Series B funding to scale its AI-powered customer interview platform, valuing the startup at $500 million after a viral billboard hiring stunt put it on the map. Ribbit Capital led the round, with Evantic, Sequoia Capital, Conviction, and Pear VC participating, bringing the company's total capital raised to $100 million.

The billboard stunt behind the AI customer interview startup

The recruiting stunt that first drew attention was deliberately unconventional. Founder Alfred Wahlforss spent $5,000 on a San Francisco billboard displaying what looked like gibberish. Decoded, the AI tokens led to a coding challenge: build a digital bouncer for a famously selective Berlin nightclub. Thousands attempted it, 430 solved it, and some of those solvers were ultimately hired. The puzzle doubled as a filter for exactly the kind of problem-solvers the company wanted.

Fixing broken market research with AI

Listen's AI researcher finds participants, conducts in-depth interviews, and delivers insights in hours rather than weeks. The platform aims to dissolve the traditional trade-off between quantitative surveys, which offer statistical precision but miss nuance, and qualitative interviews, which deliver depth but cannot scale. It works in four steps:

  • Create a study with AI assistance.
  • Recruit from a global network of 30 million people.
  • Run AI-moderated interviews with automatic follow-up questions.
  • Package the results into executive-ready reports.

Wahlforss argues that open-ended video conversations generate more honesty than multiple-choice forms, where respondents can simply guess the intended answer. In nine months, the company grew annualized revenue 15 times to eight figures and conducted more than one million interviews. The model sits squarely within the broader push to apply AI tools to core business workflows.

Tackling research fraud at scale

Building a usable panel meant confronting rampant fraud. Wahlforss says even large companies sent fake "enterprise buyers" that the system flagged immediately. Listen built a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity and flag suspicious patterns. Emeritus, an online education company, reported that roughly 20 percent of its survey responses were previously fraudulent or low quality, a figure it reduced to nearly zero after adopting Listen. That kind of cleanup directly affects how much trust executives can place in the resulting data.

How customers use the platform

Several companies have adopted the platform for faster insights, and the reported results help explain the investor enthusiasm:

  • Microsoft cut research timelines from four to six weeks down to days or hours, and collected user video stories for a milestone campaign within a single day.
  • Simple Modern tested a product concept and received feedback from 120 people in about 2.5 hours.
  • Chubbies grew youth research participation 24 times and uncovered a product flaw through AI interviews.

What's next for Listen Labs

Wahlforss outlined a roadmap that includes simulating customers from past interviews and, eventually, automated actions such as offering discounts to churning users, with guardrails to keep companies in the loop. The company says it does not train on customer data and scrubs sensitive personal information automatically. It grew from 5 to 40 employees in 2024 and plans to reach 150 this year, betting that in the AI era the companies that listen to customers fastest will win.

Listen Labs funding questions, answered

How much has Listen Labs raised in total?

The new $69 million Series B brings the company's total capital raised to $100 million, at a $500 million valuation.

Who led the funding round?

Ribbit Capital led the Series B, with Evantic, Sequoia Capital, Conviction, and Pear VC participating.

How does Listen prevent fake responses?

Its "quality guard" cross-references LinkedIn profiles against video responses to verify identity and flag suspicious patterns, which one customer credited with cutting fraudulent responses from about 20 percent to nearly zero.