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.
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.
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.
I have to confess I was worried at first because I read "interview" and assumed it was another AI recruitment solution... which opens a huge debate about ethical recruitment and the legislation around automated decision making. However, digging deeper I realise this is actually about user experience feedback... which I guess means there is no reason not to try and capture the "give aways" in terms of facial expressions and pauses etc. There has been some work done around this in the recruitment space (I remember one video platform differentiating between whether a candidate looked to the left or right when answering because it suggested whether they were using the creative or logical part of their brain... as above I think this has now been outlawed in HR processes). But in your space, I think you have the opportunity to get creative and you certainly seem to have done some great work. Best of luck to you.
Mira
@martin_tanner Martin, this is a genuinely important distinction you have drawn, and you are right on both counts.
Facial coding in recruitment is rightly controversial and in many jurisdictions rightly restricted. The core ethical problem is using biometric signals to make high-stakes decisions about people; employment, credit, access, without their meaningful understanding or control. That is a fundamentally different situation from what we do.
In consumer and user research, participants choose to take part, they can stop at any time, and the output of the research does not affect them, it affects product decisions. The emotional signals Mira captures are used to help brands understand how people genuinely respond to products and concepts. No decision is made about them as individuals.
We also do not use facial recognition; we measure expressions, not identity. Participants are anonymous at the analysis level.
You are right that the research space is where this can be done responsibly. That is why we built here. Appreciate you thinking it through properly rather than reacting to the phrase "facial coding."
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.
Mira
Product Manger at Decode here
One thing that always fascinated me about qualitative research: researchers don't just analyze what people say. They spend hours replaying interviews to understand how they said it.
A micro-expression of disgust at the pricing slide, a long pause before "I'd probably use it," hands that stopped moving the moment they said they were "comfortable."
That's the invisible layer of qual research. The part that turns a quote into an actual insight.
Mira is built around that gap, it conducts interviews in 75+ languages, probes emotionally in real time, and simultaneously captures vocal hesitation, facial emotion, and where attention went. So researchers stop losing those moments to memory and manual replay.
The goal isn't to replace researchers.
It's to give them superpowers, so they can spend more time discovering why people behave the way they do, instead of manually reviewing hours of recordings.
Happy to answer any questions about how any of this works.