Every product team knows they should talk to their users more. In practice, many teams still only hear from a handful of users each quarter, usually when a roadmap question has already become urgent.
The gap isn’t about caring. It’s about logistics. Scheduling across time zones, finding participants who show up, moderating without leading, transcribing hours of audio, synthesizing it into something actionable. It’s a full-time job disguised as a simple question: “What do our users think?”
The compromise everyone makes
So teams compromise. Low-response surveys. Session recordings that show what but never why. Tiny samples extrapolated wildly. The result: products shaped by guesswork instead of understanding.
What if the interview could run itself?
This is the question that led to Maren. Not “what if we automated interviews?” Automation implies checkbox surveys in a chatbot wrapper. The real question: what if an AI could have the kind of thoughtful, adaptive conversation that a trained qualitative researcher would?
That’s what Maren does. You define what you want to learn, Maren talks to your users, and then synthesizes everything across dozens or hundreds of interviews into themes and insights your team can act on.
Early signals
What surprised us most in early Maren sessions was how often participants gave the answer a founder usually has to work hard to reach: the awkward objection, the workaround they had normalised, the moment they nearly gave up. No social pressure, no judgment, no awkward silences. People are remarkably candid when they feel safe.
The other signal was depth. Compared with static text fields, Maren conversations invite people to explain, qualify, and give examples. That’s the difference between data and understanding.
What we’re building toward
Maren is still early. We’re building in the open, learning from every interview conducted.
Our goal isn’t to replace human researchers. It’s to make research accessible to every product team. If you’ve ever wished you could talk to more users without the logistical overhead, that’s what Maren is for.