Listen Labs raises $27M Series A led by Sequoia to automate AI-powered customer research at scale

Summary

Listen Labs has raised a $27 million Series A led by Sequoia, with Conviction and Pair also participating, to scale AI-powered customer research. The company conducts AI-driven one-on-one interviews at volume — hundreds in parallel — then synthesizes the results to surface what customers actually want. Customers include Microsoft, Canva, and Chubbies.

The founding story is direct: Alfred and his co-founder built a consumer app that hit 20,000 downloads in a single day, burning through GPU costs at $1,000 per hour on personal credit cards. To understand what users wanted fast, they built a chatbot to interview them at scale. That prototype became Listen Labs.

Why it lands with enterprise

Large companies want customer feedback but are structurally blocked from getting it. Legal review, internal approval chains, and outsourcing dependencies mean most enterprise user research gets routed through consulting firms charging around $100,000 for roughly 20 interviews delivered 12 weeks later. Listen Labs replaces that workflow — faster, cheaper, and with far higher interview volume.

The Chubbies case is the clearest product example in the conversation. Chubbies used Listen to interview kids about shorts comfort. Children were more candid with the AI than with a human interviewer, and the resulting feedback led directly to a new product with a redesigned liner that is now growing quickly.

What the product actually does

The platform runs in three steps: find respondents through a database of millions of users, conduct AI video interviews that capture both verbal responses and on-screen behavior, and run analysis to surface signal. Listen has conducted more than 300,000 interviews to date and has built quality scoring for each participant — a respondent track record that lets the platform identify which users consistently give useful feedback and flag outlier or surprising statements separately.

The ceiling on the analysis side is deliberately set high. Alfred frames the competition as McKinsey charging a million dollars for a PowerPoint deck.

The longer bet

As AI agents increasingly handle the writing of code, the scarce input becomes knowing what to build. Alfred's argument is that talking to customers — the other half of the YC formula alongside writing code — becomes more valuable as the coding half gets automated. The implied end state is Listen wired directly into developer tools like Cursor or Devin, so customer feedback loops back into product changes with no human intermediary.

Sequoia's Brian Schreier led the round; he also led Sequoia's investment in Qualtrics, the largest prior outcome in the customer feedback category. Mike Bernal at Conviction also joined.