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.
Reading how people feel vs what they say is where most user research dies. If the emotion detection is even directionally right, that's a big unlock for solo founders who can't afford research teams.
Mira
@medal411 "Even directionally right" is an honest way to frame it, and that is genuinely where the value sits for a solo founder. You are not running a clinical study. You are trying to know whether the hesitation you sensed in three conversations is real or in your head.
Mira gives you a signal to pressure-test that instinct, faster than you could manually review recordings. First study is free this month if you want to try it on something live.
@productrambler "Pressure-testing your instinct" is exactly the job to be done. Might take you up on that free first study — will reach out.
Tried it on a small concept test and the emotional read from facial coding picked up hesitations I would have totally missed in a regular interview. Insane that it handles recruiting and synthesis in one pass.
Mira
@poyraz853752 The recruiting + synthesis in one pass is one of the things we are most proud of — most tools make you stitch together three or four platforms to get from question to insight. The hesitation catch is exactly where the emotional layer earns its place. That pause before the polished answer is usually the most honest signal in the whole session. Glad it surfaced something useful for your concept test.
The facial coding and emotion layer actually feels different from typical survey tools — I ran a quick concept test and the sentiment data picked up nuances I usually miss in write-ups.
Mira
@gllfevp Thank you for testing it on a real concept. The hesitation read is the whole point. People rarely say "I'm unsure" out loud, but the face shows it, and that gap between what's said and what's felt is exactly what Decode is built to surface
How does the facial coding and eye tracking actually work on participants who don't have webcams or who join from mobile devices?
Mira
@meryemuzuno5kq Right now, you would need camera access to have face emotion measurement or eye gaze tracking done. However, Mira also has the ability to measure emotions purely based on tone of the voice and also based on the words that are being used.
What stands out is the integration of facial coding and voice emotion AI directly into the interview flow rather than bolting them on as an afterthought. That feels like a real research instrument, not just a chat wrapper dressed up with sentiment scores.
Mira
@ayeffrz Ayşe, thank you — "research instrument, not a chat wrapper" is exactly the bar we held ourselves to.
The decision to build the emotion layer into the interview flow rather than running it as a post-processing layer was deliberate. When emotion signals are captured in real time during the conversation, Mira can actually respond to them — adjusting its probing when it detects hesitation or conflict between what someone says and how they react. That closed loop is what makes it feel like a moderator, not a recorder.
The underlying models (facial coding, voice tone, eye tracking) are the same tech we've been building for 9 years across 150+ brand research programs — we just finally wired them into the interview itself.
Would love to show you what a full session looks like end to end → https://www.entropik.io/book-demo
I have run plenty of user interviews by hand, so the moderation part I get. The reads-how-people-feel part is where I would love more detail: inferring emotion from voice or wording is powerful, but it is also the kind of signal that can mislead a decision (someone nervous is not someone negative). How do you present that layer to the researcher, as a hint to probe further or as scored data? The difference feels important.
Mira
@virko_kask Virko, you've named the exact distinction that separates useful emotion data from noise — and it shaped how we built the output layer.
The short answer: it's a hint to probe further, not a verdict.
We made a deliberate decision not to present emotion as scored data; the researcher is supposed to act on it directly. A nervousness signal doesn't get labeled "negative response." It gets flagged as a moment worth returning to, a timestamp when verbal and non-verbal signals diverged or when an emotion was sustained long enough to be meaningful.
In the report, it looks like a highlighted clip with the emotional trace underneath it. The researcher sees: what was said, what the face and voice were doing at that moment, and how long it lasted. No single-number sentiment score. No resolved verdict. The interpretation is yours.
Where we do add structure, we distinguish between transient signals (a quick flash of surprise, a one-second hesitation) and sustained patterns (3–5 seconds of consistent emotional signal across modalities). Transient signals are surfaced as context. Sustained patterns are what get elevated as findings. That threshold matters for exactly the reason you named, nervousness is not negativity, but sustained disengagement during a key product moment is worth a conversation.
The emotional layer is evidence, not a conclusion. Would love to show you what this looks like on a real study, especially with your interview background. I think you'd spot things most researchers miss. Happy to set up a call → https://www.entropik.io/book-demo
Congratulations on launch! A lot of the questions are about accuracy and privacy, so I'll ask a different one: how does the emotion reading hold up across cultures and languages? People show feelings differently depending on background, and my audience is fairly reserved by nature. Does the model account for that, or is it mostly calibrated to more expressive participants?
Mira
@alieksia Anastasiia, this is one of the most important questions anyone can ask about emotion AI — and one we've had to answer in practice across 150+ brands in markets where emotional expressiveness varies significantly.
A few things we do specifically for this:
Individual baseline calibration is not a universal norm. At the start of every session, Mira establishes a neutral baseline for that participant. What matters is deviation from their baseline, not deviation from an average. A reserved participant who shifts even slightly is flagged because for them, that shift is meaningful. You're not comparing them to a participant in a different country who naturally gestures more.
Multimodal cross-referencing helps here significantly. Voice tone and pacing convey emotional signals across cultures in ways that facial expressions alone don't. Someone from a more reserved cultural background who shows minimal facial movement will often convey more signals in their voice, hesitation, changes in pacing, or a drop in confidence. Mira reads both simultaneously.
We've trained specifically on diverse participant pools. Our models have been built on research across APAC, MENA, Europe, and the Americas, not just Western expressive populations. That said, we're transparent: some cultural nuances remain an active area for improvement, and we'd rather flag lower-confidence signals than assert false precision.
The output accounts for this contextually. Signals are always shown with confidence scores and duration markers. A researcher working with a reserved audience can set their own threshold for what's worth acting on.
If your audience is particularly reserved by nature, I'd love to show you a session with that kind of participant profile, specifically, it's actually where the multimodal approach shows its biggest advantage over facial-only tools.
Worth a call? https://www.entropik.io/book-demo
@mridhu_varshini_ thank you for taking the time to explain this so fully. The baseline calibration and the multimodal reading for reserved participants answer my concern well. I'll keep Mira in mind for when I'm at that research stage.
Mira
@alieksia , sure. We can reconnect anytime when the research stage comes in. Happy to be connected :)