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.
The facial coding combined with voice emotion analysis during a test interview genuinely caught moments I would have missed in a regular transcript. Feels closer to sitting with a respondent than a typical AI research tool.
Mira
@srammoa "Closer to sitting with a respondent" is one of the best ways I have heard it described. The transcript tells you what they said. Mira tries to preserve what was happening underneath that. Thank you for testing it.
the facial coding detail is wild, never seen a research tool that actually picks up on what people are feeling while answering. ran a quick test on our team and the voice emotion layer caught hesitation I would have totally missed in a normal transcript.
Mira
@nihalzsaki243g The hesitation catch is where the voice layer earns its place; that pause before the polished answer is usually the most honest moment in the whole session. Glad it surfaced. If you want to run a structured study, the first one is free this month. Please feel free to book a demo at https://www.entropik.io/platform/ai-moderator and mention PH20 on the call :)
Tried Mira for a quick concept test and the facial coding actually flagged a hesitation I never would have caught in a transcript alone. Slightly surreal watching it react to my own face, but genuinely useful.
Mira
@elanurybrp This is exactly the feedback that matters most, thank you, Elanur.
The hesitation flag mid-interview is one of those moments that traditional transcripts completely lose. You see it in real time, the participant moves on, and by the time you are reviewing notes it is gone.
Would love to hear more about what you were testing, if you want to run a more structured study, first one is free this month. https://aimoderator.entropik.io
Tried Mira for a quick concept test and the facial coding picked up subtle reactions I would have completely missed reading a transcript. The automated report came back faster than I expected and the emotional insights actually felt useful rather than just a novelty.
Mira
@krakl9tl6 "Useful rather than just a novelty" is exactly the bar we hold ourselves to. A lot of emotion AI tools add a layer but do not meaningfully connect it back to the insight. Glad the report turnaround and emotional signals both landed as useful for your concept test.
The facial coding piece is genuinely impressive — caught subtle reactions in a test session I wasn't even expecting to register. Most "AI researcher" tools I have tried just spit out summaries, this one actually digs into how people feel.
Mira
@sleymanpelv3z3 "Summaries vs actually digging into how people feel" is a real distinction. Most tools stop at summarising what was said. The signal that actually drives behaviour lives in a different layer. That is what Mira tries to preserve.
Tried the facial coding on a quick test and the emotion read was scarily accurate to what I was actually feeling during the open ends. The auto-generated themes also saved me a solid hour of tagging.
Mira
@burhanekipepz8 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.
the facial coding piece is wild, didn't think i'd see real emotional response analysis baked into a research tool at this price point
Mira
@serhatmant9b7b Accessibility was a deliberate decision. Emotion AI in research has historically been available only to large enterprise teams with big tooling budgets. Glad that came through.