Unlike AI tools that stop at interview + transcript, Mira is a full AI researcher — plans studies, recruits globally (100M+ panel, 120 countries), runs dynamic interviews with intelligent probing, and uniquely captures what participants say AND feel via real-time facial coding, voice emotion AI, and webcam eye tracking. Extracts themes, generates insights, and produces research reports automatically. 17 patents. 70+ languages. Trusted by Unilever, Nestlé and 150+ global brands. $25M Series B.
Forum Threads
The say/feel gap in research; how big is the problem actually, and what do you do about it?
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.
What do transcripts miss that sends you back to the recording?
Nine years of working closely with insights teams across CPG, BFSI, and tech has given us a consistent observation: the transcript is where researchers start, but the recording is where they go when the transcript isn't enough.
They go back to hear how someone said something. To check whether the pause before an answer was genuine uncertainty or just thinking time. To see whether the person's expression changed when they described a feature they claimed to like. To catch the moment when engagement visibly dropped when they stopped leaning forward, when their eyes moved away from the screen even while their words stayed polite and positive.
That gap between the transcript and the recording is where a significant amount of research insight lives. And it's also where the most time gets consumed in manual analysis. Researchers who run 20 or 30 interviews know what it's like to sit through hours of footage looking for the three or four moments that actually matter.
This observation that the transcript captures what was said but not how or with what underlying feeling is fundamentally what drove us to build the emotion and behavioural layer into Mira. Not to replace researcher interpretation, but to give researchers a way to find the right moments faster.
I'm genuinely curious: what's the signal you find yourself wishing you had after you've finished going through a set of interviews? What consistently drives you back to the recording that the transcript alone doesn't give you? And have you found any workarounds annotation practices, tagging systems, collaborative review setups that help you capture more of what the transcript loses?
What would actually make you trust an AI moderator's findings?
We launched Mira on Product Hunt today, and the conversations in the comments have been the most interesting part of the whole day.
Researchers asked about probe neutrality whether the follow-up questions an AI asks mid-interview can lead a participant, rather than uncover them. They asked about cultural calibration, whether emotion models trained largely on Western data can accurately read a participant in Jakarta or Nairobi. They asked whether the say/feel mismatch gets surfaced as raw evidence or quietly resolved into a single confidence score.
These are serious questions. And they made me realize something: the bar for trust in AI-moderated research is fundamentally different from other AI tools.
If a writing assistant gets something wrong, you catch it before you publish. If an AI coding tool hallucinates, your tests fail. But if a research tool misreads how participants felt during a concept test, and that feeds into a product decision, the error is invisible. By the time the product ships and the market responds, the research moment is long gone.
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."
The facial coding and emotion AI pieces genuinely surprised me, that kind of read on participants usually takes a trained researcher. Watched it pull themes from a test interview in minutes.
Mira
@cal_turkan32795 "That kind of read usually takes a trained researcher" is the most accurate description of what we were trying to automate. The themes in minutes is the time compression — the depth of signal you are working from stays the same.
the facial coding during interviews genuinely caught me off guard, you can actually see the moment a respondent's expression shifts on a tricky question and it ties back to the insight automatically.
Mira
@vedat205337 The "ties back to the insight automatically" is the AI Copilot connecting the emotional moment to the theme it contributed to. You are not just seeing a flag; you are seeing why it mattered in the context of the full study.
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.
Mira
@medal411 Looking forward to it :) When you're ready → https://www.entropik.io/book-demo & do not forget to mention "PH20" on the demo.
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.
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