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.
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 :)
"Reads how people feel" is the interesting (and risky) part. When it detects hesitation mid-interview, does it adapt its questioning in the moment or just annotate for the researcher afterward? I'm running beta-user interviews right now and what I always miss is what people didn't say, I would love to know if you surface that.
Mira
@chielephant Anthony, you've named the exact thing most AI interview tools get wrong: they annotate after, when the moment is already gone.
Mira adapts in the moment. When it detects hesitation, an emotional shift, or a mismatch between what's said and how it's said, it probes during the conversation rather than as a post-hoc tag. "You paused there. Tell me more about what was going through your mind." The follow-up happens while the participant is still in the thought, not after they've moved on.
On what people didn't say: this is where the multimodal layer earns its place. Mira flags moments when vocal confidence dropped without a corresponding verbal signal, when attention shifted during a key question, and when facial affect changed but the spoken answer was flat. Those are the gaps, the things a transcript alone will never show you. They surface in the report as flagged moments with timestamps, so you can go directly to "here's where something was happening that wasn't being said."
For beta-user interviews specifically, that layer tends to be most valuable on product moments — the exact second someone's expression changes when they hit a friction point, even while they're still saying "yeah, this makes sense."
Would love to show you what this looks like on a real session, especially with beta interviews, the signal density is usually high → https://www.entropik.io/book-demo
How do i verify the accuracy of Facial coding and eye tracking?
Mira
@jitender_pankaj1 Our algorithms are trained and validated with millions of data points we collected over the years with our in house data collection and tagging platform. We ensure each tag has high inter rater reliability before accepting it in our dataset. Our eye tracking algorithms are frequently tested against the data from a physical eye trackers.
How does the pricing actually scale for smaller teams or solo researchers who don't need the full enterprise setup, and is there any way to try it before committing?
Mira
@barwcts The best starting point is the free study, no commitment, no credit card. Mention PH when you book: Book a Demo: See Entropik Decode in Action
For pricing beyond that, it scales based on study volume which can be purchased in smaller blocks. Happy to walk through what makes sense for your setup. Drop a note or book a quick call.
Respect for not hand-waving that, most launch threads would have. One thing I'd add: per-frame confidence is the model scoring its own certainty, so it won't catch systematic bias. A model can be high-confidence and wrong the same way across a whole population and never flag it. The only check I trust is human-coded ground truth sampled per region, which is painful to collect. Which regions have you actually validated against local human coders versus carried over from the base model?
The Say-Do Gap framing is the sharpest part of this — self-reported data being "socially edited" is exactly the failure mode most research tools quietly inherit. My honest question on the emotion layer: facial coding and voice-emotion signals vary a lot across cultures and neurotypes, so how do you keep the "feel" read from becoming its own bias, especially across 120 countries? Curious whether researchers can see and override the affect signals, or whether they're treated as ground truth in the final report.