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.
Deeper emotional signal is great, but storing facial and voice data at scale raises the privacy stakes quite a bit- how long is that data retained?
Mira
@wyatt_cameron , Really important question, Wyatt.
A few specifics: participants are fully informed before any session that facial, voice, and eye data will be captured, explicit consent is required, not implied. Data is processed and stored with SOC 2 Type II compliance and GDPR alignment.
On retention: researchers control their own data retention settings. Raw biometric data is not shared with anyone outside the platform and is not used for model training without explicit permission.
Happy to share our full data processing agreement for any enterprise evaluations, what specific aspect would be most useful to dig into?
How does the facial coding and eye tracking actually work in remote sessions without users installing heavy software, and does that introduce any bias from people who decline to turn on their webcam?
Mira
@memetsanayi As long as users are connected to internet, we don't require you to install any software. Everything happens right on the web browser. Without facial data, there in bias introduced as long as enough people allow us to use webcab. We have seen in our research that the data for emotion converges beyond a point, which means we will have reduced bias as more people take the test.
Mira
@memetsanayi Everything works right in your browser, so you don’t need to download anything or install plugins. Participants just allow camera access, do a quick calibration, and then the session begins. It uses the regular webcam on your laptop or phone. There is no facial recognition involved; we only measure expressions, not identity.
Camera access is always based on full consent. If someone chooses not to allow camera access, they can still take part in the study. In that case, we do not collect gaze or facial signals, but we still measure emotion using voice tone and text. Declining the camera just removes one type of signal, but it does not exclude the participant or reduce the sample size.
The facial coding and emotion AI combo is wild — usually I get a wall of quotes to sift through, but here it flagged where respondents actually felt something. Made the insight pull way faster than expected.
Mira
@doanj7hh "Flagged where respondents actually felt something" is the signal we built toward — not a wall of quotes, but the specific moments that matter emotionally. Glad the insight pull was faster than expected.
Strength here is catching reaction people wouldn't admit to on a survey. Weakness is that everything depend on decent webcam and lighting, which won't be true for every participants in every country.
Mira
@ian_maxwell2 Fair and accurate, Ian.
The webcam dependency is a real constraint and we are honest about it. We handle it through pre-session calibration and per-frame confidence scoring, weak frames are omitted rather than guessed.
For participants in lower-quality environments the voice emotion and transcript layers still operate fully, so the study is never purely dependent on the video feed. That said, you are right that it is a variable, and it is why we are clearer about this in our documentation than most tools in the space.
How does the facial coding and emotion AI handle privacy and consent across different regions, especially with GDPR and other strict regulations?
Mira
@ufukaktugba
Hi Ufuk, great question.
Consent is handled per session: participants see a clear explanation of what data will be captured (facial, voice, eye) before any session begins and must actively opt in.
On GDPR: we are fully GDPR compliant. Data is processed within compliant infrastructure, participants can request deletion, and researchers control retention policies. We also have SOC 2 Type II certification.
For APAC markets we follow regional equivalents (PDPA in Singapore, PIPL in China). If you are evaluating for a specific region, happy to go deeper.
This is a really interesting take on AI-powered research.👏🏻
Going beyond transcripts to capture emotions and behavioral signals could unlock much richer insights for product teams. Curious, how do you balance those advanced features with participant privacy and consent during interviews?
Mira
@worksforme Hi Laiba — the balance is built into the session flow itself. Every participant gets a clear explanation of what will be captured before they join — webcam access for facial and eye data, microphone for voice emotion. They can decline any of these and still participate, with the system adapting to use only the available signals.
No facial recognition is used anywhere — we only measure expressions, not identity. Data is never sold or used for external training without explicit permission. Researchers control retention and deletion.
Happy to walk through the full consent flow if that would help.
How does the facial coding and emotion AI actually perform across different cultural contexts, and do participants need any special setup or permissions on their end for it to work smoothly?
Mira
@duyguridapagq Our emotion AI is trained to understand the demographic difference while predicting emotions using the face. Later we do a few normalisations based on the benchmarks we have collected to give you unbiased scores and emotion data points. We just need permission to use the microphone and the webcam.