A few people have asked how Mira actually works in practice, so wanted to write this up.
The full flow from start to finish:
1. Set up your study (5 minutes)
Choose a template Customer Discovery, Concept Testing, UX, Brand Perception, NPS follow-up, and more. Or build your own discussion guide. Mira generates contextual follow-up questions automatically, so you do not need to script every question.
2. Recruit participants (built in)
Access 100M+ participants across 120 countries directly from the platform. Set demographic filters, screener questions, and Mira handles recruitment. No third-party panel needed.
3. Run the AI moderated interview
Participants join via link no app download. Mira moderates the conversation, asks follow-up questions intelligently, and reads facial expressions, voice emotion, and eye gaze in real time during the session. Works on a standard webcam.
4. Get your report (minutes, not days)
Automatic transcript with speaker separation. AI themes, tags, summaries, and key quotes extracted automatically. Emotional signal overlaid on each moment. Full research report generated executive summary, findings, evidence, recommendations.
5. Share and store
AI highlight reels for stakeholders no one watches 40-minute recordings. Everything stored in a searchable research repository. Cross-study intelligence lets you compare findings across multiple projects over time.
The part most people ask about:
The emotional layer runs during the interview not after. So when a participant says "I like it" but their face shows hesitation, Mira catches it and probes deeper in the same conversation. That is the core difference from transcript-only tools.
First study is free this month happy to help anyone set one up. Drop a comment below or book here: https://www.entropik.io/book-dem...
See Mira on Product Hunt: https://www.producthunt.com/post...
What type of research are you running? Happy to walk through how Mira would work for your specific use case.
The facial coding combined with voice emotion analysis during a test interview genuinely caught moments I would have missed in a regular transcript. Feels closer to sitting with a respondent than a typical AI research tool.
Mira
@srammoa "Closer to sitting with a respondent" is one of the best ways I have heard it described. The transcript tells you what they said. Mira tries to preserve what was happening underneath that. Thank you for testing it.
the facial coding detail is wild, never seen a research tool that actually picks up on what people are feeling while answering. ran a quick test on our team and the voice emotion layer caught hesitation I would have totally missed in a normal transcript.
Mira
@nihalzsaki243g The hesitation catch is where the voice layer earns its place; that pause before the polished answer is usually the most honest moment in the whole session. Glad it surfaced. If you want to run a structured study, the first one is free this month. Please feel free to book a demo at https://www.entropik.io/platform/ai-moderator and mention PH20 on the call :)
Tried Mira for a quick concept test and the facial coding actually flagged a hesitation I never would have caught in a transcript alone. Slightly surreal watching it react to my own face, but genuinely useful.
Mira
@elanurybrp This is exactly the feedback that matters most, thank you, Elanur.
The hesitation flag mid-interview is one of those moments that traditional transcripts completely lose. You see it in real time, the participant moves on, and by the time you are reviewing notes it is gone.
Would love to hear more about what you were testing, if you want to run a more structured study, first one is free this month. https://aimoderator.entropik.io
Tried Mira for a quick concept test and the facial coding picked up subtle reactions I would have completely missed reading a transcript. The automated report came back faster than I expected and the emotional insights actually felt useful rather than just a novelty.
Mira
@krakl9tl6 "Useful rather than just a novelty" is exactly the bar we hold ourselves to. A lot of emotion AI tools add a layer but do not meaningfully connect it back to the insight. Glad the report turnaround and emotional signals both landed as useful for your concept test.
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.
Mira
@sleymanpelv3z3 "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 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.
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?