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 actually caught a reaction I would have completely missed reviewing the transcript alone. Seeing emotion data layered with what people said felt like a real research upgrade, not just another AI wrapper.
Mira
@memetjr2q Thank you for putting it through a real test. That gap between what the transcript says and what the face shows is exactly what Mira is built to catch, most tools only ever see the words.
How does the facial coding and eye tracking piece actually work in practice, do participants need to opt in and run any special setup on their end?
Mira
@ezelsukut Everything runs in the browser, no downloads or setup. Participants grant camera access, run a short calibration (only for eye tracking), and the session starts on a standard laptop or phone with in-built cam. Fully consent driven.
How does the facial coding and eye tracking actually work in practice with participants who are on their phones or in different lighting conditions? Curious how reliable that data really is across such a massive global panel.
Mira
@miratngl8j5s Great question Miraç.
A few specifics on how we handle this:
Phones: Mira currently runs in-browser on desktop, participants join via a standard webcam, not a mobile camera. We found controlled desktop sessions produce more reliable facial signal than mobile, where camera angle and movement vary too much. For global panel recruitment, participants are briefed on the setup requirement before joining.
Lighting: Before every session, participants go through a quick calibration check, the system guides them to adjust their face position and lighting until confidence scores are acceptable. Frames that fall below our threshold are automatically excluded from the emotional signal rather than being interpolated. We never guess on weak data.
Global reliability: Our models are trained across demographic groups and we apply cultural normalisation benchmarks to reduce bias in emotional scoring. The signal is most reliable when used comparatively, patterns across a study group, rather than reading single participant moments in isolation.
Honest caveat: webcam-based tracking is less precise than lab infrared. For questions like where attention went first or whether someone showed hesitation at a specific moment, it works well. For pixel-level gaze accuracy, a lab setup still wins.
Happy to go deeper on any of this.
When Mira spots a say/feel mismatch and digs deeper on its own, how do you keep that follow-up from leading the participant? A moderator reacting to visible confusion can easily plant the doubt rather than uncover it. Is the probe neutral by design, or tuned per study?
Mira
@david_vilalta David, this is the question we lost sleep over. A leading probe is almost worse than no probe — it plants the narrative.
A few design decisions we made:
Probes are behaviorally triggered but linguistically neutral. When Mira detects a signal — a hesitation, an emotional shift, a response that doesn't match the facial/voice pattern — it doesn't say "you seemed confused." It says something like "You paused there — tell me more about what was going through your mind." The trigger is emotional, the language is open.
No interpretive language in the probe. Mira never names the emotion it detected back to the participant. It asks outward, not inward. This is a deliberate constraint built into the probing engine.
Tunable per study type, not per participant. Concept testing probes are calibrated differently from usability or brand research — because the type of signal differs. But within a study, every participant gets the same probe structure. That keeps cross-participant comparisons valid.
Probe depth is capped. Max 2 levels of follow-up on any one signal — so the interview doesn't become an interrogation.
The honest caveat: no probe is perfectly neutral. But compared to human moderators who vary tone and word choice across 40 interviews — Mira is at least consistently neutral. Happy to show you a live session → https://www.entropik.io/book-demo
What happens when it works fine and every brand in a category runs creators against the same queries, does it become an arms race where the UGC cancels out, or is there a ceiling on how much citation share you can actually buy back?
Mira
@david_vilalta Sharp concern, and one worth taking seriously.
The short answer: the questions might converge, but the insight won't — and here's why.
The emotional layer is proprietary to each brand's product. Two competing brands can ask participants the same question about their respective checkout flows. The language of the answers might look similar. But Mira's facial coding and voice emotion data will surface where frustration spikes, which exact moment trust drops, what triggers genuine delight — and that's product-specific. You can't benchmark emotion.
Research memory compounds differently for each brand. Mira builds cross-study intelligence over time — recurring themes, longitudinal shifts, segment-level emotional patterns. A brand that's been running studies for 12 months has a fundamentally different starting point than one that just launched. That proprietary research memory is not replicable even with identical questions.
Speed becomes the moat, not secrecy. If every brand could run the same study, the winner is the one that runs it first, iterates fastest, and acts before the category moves. Mira compresses weeks of manual research into hours. That time advantage is where competitive edge lives.
The ceiling isn't on insight quality — it's on how fast you can act on it. That's what we're really building for → https://www.entropik.io/book-demo
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."
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.