There's a well-documented phenomenon in consumer and user research that most practitioners know intuitively but rarely name directly: people don't report their experience accurately.
Not because they're dishonest. Because self-reporting is hard. In the moment of an interview, participants are performing a version of themselves. They round off hesitation. They describe their behaviour more charitably than it actually was. They say "yes, I'd probably use this" when what they felt was closer to "maybe, under the right circumstances, if the price were different." The social pressure of being in a conversation, even with an AI, shapes what gets said.
This is what we call the Say-Do Gap. The distance between what someone tells you and what they actually feel or do.
It shows up everywhere. In concept tests where participants say they love a product they'd never buy. In usability sessions where someone says "this makes sense" while visibly struggling. In brand perception studies where stated attitudes don't match purchasing behaviour.
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
@mridhu_varshini_ Thank you for the detailed answer, this is genuinely thoughtful design. The transient vs sustained threshold is the right cut, and surfacing the divergence as a clip with the trace under it, instead of a number, is exactly the difference between evidence and verdict. I am heads-down on my own launch for the next two weeks, but I would honestly enjoy seeing this on a real study after that. Good luck with the launch, Mira deserves the attention it is getting.
Mira
@virko_kask Thank you, Virko, this genuinely made my day :) The way you framed it, "evidence not verdict," is exactly the distinction we spent a long time trying to get right in the design. It's validating to hear it land that way from someone who's run as many interviews as you have.
And yes!!!! Mira deserves this attention, and honestly, so do you for asking the questions that pushed us to articulate it properly. This launch has been our killer ship moment, years of building, finally in front of the right people.
Two weeks go fast. Ping me when you're out the other side, and we'll get something on the calendar. I genuinely think you'll have interesting observations after seeing it in a real study.
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 you prevent Emotion AI from overinterpreting facial expressions or cultural differences in user reactions?
Mira
@robert_dimla We avoid cultural overfitting by rejecting static global priors and instead computing affect as temporal deltas against a per user baseline. Our attention layer gates visual action units against orthogonal acoustic and semantic vectors down-weighting isolated facial anomalies as noise unless validated across channels or via active verbal probing.
Two things... first, how does the "Real time facial encoding" work? Secondly, this seems like an interesting idea, but im curious regarding how you plan to deal with jobseekers, inevitably, despising software like this. Sure businesses trying to cut costs would love something like this, but on the consumer/job-seeker side how will you mitigate and ensure a candidate doesn't feel like a robot is screening them.
Mira
@lucapiekarski Great questions!
Real-time facial encoding: We track facial action units (the individual muscle movements that make up expressions) frame by frame during the session
On the job-seeker concern: Totally fair question, but MIRA isn't built for hiring or candidate screening. We're purpose-built for user research, think usability testing, customer interviews, product feedback sessions. Participants are opted-in research subjects, not candidates being evaluated for a job. The goal is understanding genuine reactions to a product or concept, not judging a person.