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...
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What type of research are you running? Happy to walk through how Mira would work for your specific use case.
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.
Mira
@burcu192820 Glad the overlay made patterns visible that would have stayed buried in a transcript. On cross-cultural nuance — our models are trained across demographic groups, not on a single population, and we apply normalisation based on cultural benchmarks to reduce bias in emotional scoring. We do not claim perfect universality across cultures — emotion expression varies and it is an ongoing area of work. Our recommendation for global studies is always to combine the emotional signal with the transcript rather than reading either in isolation. The two together are more reliable than either alone.
Mira
Senior Director of Consumer Insights at Decode here.
Speaking as a practitioner, the feature I find most useful day-to-day is AI highlight reels. Instead of asking a stakeholder to watch hours of recordings, Mira clips the moments that matter: where a participant's hesitation revealed unspoken doubt, where genuine excitement came through before they could temper it, where what they said and what their face showed did not match.
Those moments are what change decisions in a review meeting. And they surface in minutes, not days of manual review.
The AI Copilot is the other one worth knowing about: you can ask "which participants mentioned trust concerns?" or "show me everyone who reacted negatively to the pricing slide" across an entire study instantly.
For researchers here, what type of qualitative research do you run most? Happy to walk through how this fits your workflow.
How does the facial coding and emotion AI actually perform across different cultures since expressions and emotional cues vary so widely globally?
Mira
@zgreg0z 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.
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?
Mira
@nkurumak28108 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 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.
Mira
@memetjr2q 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?
Mira
@ezelsukut 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.
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.
Mira
@miratngl8j5s Great question Miraç.
A few specifics on how we handle this:
Phones: Mira currently runs in-browser on desktop, participants join via a standard webcam, not a mobile camera. We found controlled desktop sessions produce more reliable facial signal than mobile, where camera angle and movement vary too much. For global panel recruitment, participants are briefed on the setup requirement before joining.
Lighting: Before every session, participants go through a quick calibration check, the system guides them to adjust their face position and lighting until confidence scores are acceptable. Frames that fall below our threshold are automatically excluded from the emotional signal rather than being interpolated. We never guess on weak data.
Global reliability: Our models are trained across demographic groups and we apply cultural normalisation benchmarks to reduce bias in emotional scoring. The signal is most reliable when used comparatively, patterns across a study group, rather than reading single participant moments in isolation.
Honest caveat: webcam-based tracking is less precise than lab infrared. For questions like where attention went first or whether someone showed hesitation at a specific moment, it works well. For pixel-level gaze accuracy, a lab setup still wins.
Happy to go deeper on any of this.