Mira - AI moderated interviews that read how people feel

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Unlike AI tools that stop at interview + transcript, Mira is a full AI researcher — plans studies, recruits globally (100M+ panel, 120 countries), runs dynamic interviews with intelligent probing, and uniquely captures what participants say AND feel via real-time facial coding, voice emotion AI, and webcam eye tracking. Extracts themes, generates insights, and produces research reports automatically. 17 patents. 70+ languages. Trusted by Unilever, Nestlé and 150+ global brands. $25M Series B.

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How does the facial coding and emotion AI actually perform across different cultures since expressions and emotional cues vary so widely globally?

 Our models are trained across demographic groups rather than on a single population, and we apply normalisation based on cultural benchmarks we have collected to reduce bias in emotional scoring. The goal is to measure genuine reactions, not to apply one cultural standard globally. We also let researchers set study-specific calibration. Emotion expression does vary across cultures and we do not claim perfect universality — it is an ongoing area of work. For global studies we recommend always combining the emotional signal with the transcript and not reading it in isolation.

 

The facial coding piece is genuinely impressive — caught subtle reactions in a test session I wasn't even expecting to register. Most "AI researcher" tools I have tried just spit out summaries, this one actually digs into how people feel.

 "Summaries vs actually digging into how people feel" is a real distinction. Most tools stop at summarising what was said. The signal that actually drives behaviour lives in a different layer. That is what Mira tries to preserve.

The facial coding and emotion AI pieces genuinely surprised me, that kind of read on participants usually takes a trained researcher. Watched it pull themes from a test interview in minutes.

 "That kind of read usually takes a trained researcher" is the most accurate description of what we were trying to automate. The themes in minutes is the time compression — the depth of signal you are working from stays the same.

How does the facial coding and eye tracking actually work in practice — is it through a browser plugin, a mobile app, or do participants need special hardware?

 No plugin, no app, no special hardware. Everything runs in the browser; participants just grant access to the webcam and microphone when they join. There is a quick calibration check before the session starts to make sure lighting and camera positioning are good. Works on any standard laptop or desktop webcam.

the facial coding during interviews genuinely caught me off guard, you can actually see the moment a respondent's expression shifts on a tricky question and it ties back to the insight automatically.

 The "ties back to the insight automatically" is the AI Copilot connecting the emotional moment to the theme it contributed to. You are not just seeing a flag; you are seeing why it mattered in the context of the full study.

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.

 "Even directionally right" is an honest way to frame it, and that is genuinely where the value sits for a solo founder. You are not running a clinical study. You are trying to know whether the hesitation you sensed in three conversations is real or in your head.

Mira gives you a signal to pressure-test that instinct, faster than you could manually review recordings. First study is free this month if you want to try it on something live.

 "Pressure-testing your instinct" is exactly the job to be done. Might take you up on that free first study — will reach out.

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.

 The recruiting + synthesis in one pass is one of the things we are most proud of — most tools make you stitch together three or four platforms to get from question to insight. The hesitation catch is exactly where the emotional layer earns its place. That pause before the polished answer is usually the most honest signal in the whole session. Glad it surfaced something useful for your concept test.

 

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.

 Thank you for putting it through a real test. That gap between what the transcript says and what the face shows is exactly what Mira is built to catch, most tools only ever see the words.

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?

 Everything runs in the browser, no downloads or setup. Participants grant camera access, run a short calibration (only for eye tracking), and the session starts on a standard laptop or phone with in-built cam. Fully consent driven.

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.

 Thank you for testing it on a real concept. The hesitation read is the whole point. People rarely say "I'm unsure" out loud, but the face shows it, and that gap between what's said and what's felt is exactly what Decode is built to surface