Mira

AI moderated interviews that read how people feel

User Experience

Analytics

Artificial Intelligence

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"Reads how people feel" is the interesting (and risky) part. When it detects hesitation mid-interview, does it adapt its questioning in the moment or just annotate for the researcher afterward? I'm running beta-user interviews right now and what I always miss is what people didn't say, I would love to know if you surface that.

Per-frame confidence scoring is the right instinct. The bit I'd push on at 120-country scale is cross-cultural validity of the facial and voice layer. Most action-unit and voice-emotion models train on largely Western data, and expression-to-affect doesn't transfer cleanly: gaze aversion, smile intensity, vocal pitch carry different meaning across cultures, so a 'how they feel' score can be confidently miscalibrated for a Jakarta panel while looking fine on a London one. Do you re-validate the emotion mapping per region, or is it one global model?

Congratulations on launch! A lot of the questions are about accuracy and privacy, so I'll ask a different one: how does the emotion reading hold up across cultures and languages? People show feelings differently depending on background, and my audience is fairly reserved by nature. Does the model account for that, or is it mostly calibrated to more expressive participants?

The mid-interview probe on the say/feel mismatch is the part that interests me. I run the cheap cousin of this for my own app, a panel of simulated user personas that scores LLM output before a change ships, and the one thing simulation can't give me is exactly that hesitation signal you're reading off real faces. When facial coding and the transcript disagree, which one do your reports trust? I'd want the raw disagreement surfaced, not resolved for me.

I have run plenty of user interviews by hand, so the moderation part I get. The reads-how-people-feel part is where I would love more detail: inferring emotion from voice or wording is powerful, but it is also the kind of signal that can mislead a decision (someone nervous is not someone negative). How do you present that layer to the researcher, as a hint to probe further or as scored data? The difference feels important.

What stands out is the integration of facial coding and voice emotion AI directly into the interview flow rather than bolting them on as an afterthought. That feels like a real research instrument, not just a chat wrapper dressed up with sentiment scores.

What happens when it works fine and every brand in a category runs creators against the same queries, does it become an arms race where the UGC cancels out, or is there a ceiling on how much citation share you can actually buy back?

When Mira spots a say/feel mismatch and digs deeper on its own, how do you keep that follow-up from leading the participant? A moderator reacting to visible confusion can easily plant the doubt rather than uncover it. Is the probe neutral by design, or tuned per study?

How does the facial coding and eye tracking actually work on participants who don't have webcams or who join from mobile devices?

Congrats team! “Reads how people feel” is a big claim and I mean that as a compliment, it’s the actual gap in AI-moderated interviews. My question: how do you separate signal from noise? Someone frowning might be confused by the product, or just awkward on camera with an AI voice. Would love to know what you do to avoid over-reading emotion, since that’s what would make or break trust in the insights.

How does the facial coding and eye tracking actually work in practice with participants who are on their phones or in different lighting conditions? Curious how reliable that data really is across such a massive global panel.

The facial coding and emotion layer actually feels different from typical survey tools — I ran a quick concept test and the sentiment data picked up nuances I usually miss in write-ups.

How does the facial coding and eye tracking piece actually work in practice, do participants need to opt in and run any special setup on their end?

The facial coding during interviews actually caught a reaction I would have completely missed reviewing the transcript alone. Seeing emotion data layered with what people said felt like a real research upgrade, not just another AI wrapper.

Tried the facial coding on a quick test and the emotion read was scarily accurate to what I was actually feeling during the open ends. The auto-generated themes also saved me a solid hour of tagging.

Tried it on a small concept test and the emotional read from facial coding picked up hesitations I would have totally missed in a regular interview. Insane that it handles recruiting and synthesis in one pass.

Reading how people feel vs what they say is where most user research dies. If the emotion detection is even directionally right, that's a big unlock for solo founders who can't afford research teams.

How does the facial coding and eye tracking actually work in a remote setting without making participants uncomfortable or needing specialized hardware?

the facial coding piece is wild, watching the emotion data overlay during interviews made patterns jump out that a transcript alone would totally miss. really curious how it handles cross-cultural nuance though.