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 during interviews is genuinely impressive, you can actually see hesitation or excitement that pure transcripts would completely miss. Wish more research tools took emotion seriously like this.
Mira
@mahmutkrc1pj5 Yes, we focus on understanding non verbal cues during interactions and discussions in all of our products. We believe that missing emotion signals might lead to incomplete view of customer's views during interviews and that is why we invested years perfecting multi modal emotion models to provide a comprehensive analysis of customer interviews.
Mira
@mahmutkrc1pj5 Thank you, Mahmut, this is exactly why we built it. The moment before someone chooses their words is often the most honest part of the whole interview. That is the layer that changes decisions.
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.
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.
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.
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.
Mira
Hi all,
Marketing lead at Decode here.
I have spent the last few months working closely with this product to build the launch. The thing that struck me most: most AI interview tools stop at the transcript. Mira treats that as the starting point.
A few capabilities people miss: AI follow-up probing that automatically asks "why?" and "tell me more" mid-interview based on what the participant actually said. AI highlight reels that automatically pull emotional moments, so you do not have to share 40-minute recordings with stakeholders. Cross-study intelligence that finds recurring themes across multiple research projects over time.
The multimodal layer — emotion AI on top of the interview — makes findings more defensible. You are not just quoting a participant. You are showing what they felt when they said it.
Happy to answer questions about the product or how we built the launch.