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.

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?

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.

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.

Founder at @Decode by Entropik here. One of the things that surprises people most about MIRA is that we offer eye tracking: gaze, fixation, dwell time, heatmaps, attention of interest, using a standard consumer webcam. No Tobii. No IR sensors. No specialized hardware of any kind. 

The immediate question is always: how? 

One of the sharpest questions we got in our Product Hunt comments this week came from a researcher who asked something I've been thinking about ever since:
When an AI moderator detects hesitation or an emotional shift mid-interview and decides to probe deeper how do you make sure the follow-up question uncovers the participant's thought rather than planting it?
This is a real and serious concern. A human moderator reacting to visible confusion can easily lead a participant without realising it. The way you phrase a follow-up the word you choose, the tone you use, even which moment you decide to probe shapes what the participant says next.
"You seemed unsure about that" is a very different probe from "tell me more about what was going through your mind." One suggests an interpretation. The other opens space. And at scale, across 30 or 40 participants, that difference accumulates into a systematic bias in your data.
With AI, the risk is that the same leading framing could be applied consistently across every participant, in a way that's invisible to the researcher reviewing the transcript afterward.
The question I want to put to researchers who run qualitative studies regularly: how do you train human moderators for probe neutrality? What are the specific rules you give them about when and how to follow up?
And do you think AI moderation is more or less susceptible to leading than a human moderator who varies their tone, word choice, and demeanour across sessions? Is consistency in AI probing a feature or a risk?

We're Decode by Entropik. Tomorrow we're launching Mira on Product Hunt. But we want to be honest about where AI moderation works vs. where it doesn't.

Where AI wins:

Consistency every participant gets the same quality of follow-up probing

We built Mira to solve a specific problem: AI interviewers that could ask questions, but couldn't read the room.
Traditional qual research is slow. AI tools that came before could moderate but they missed the emotional signal. A participant says "yes" while their face shows confusion. That gap is where insights get lost.
Mira detects emotion in real time via facial coding and voice AI, adapts follow-up questions based on how people actually feel, and delivers synthesis in 48 hours across 70+ languages.
We're launching on Product Hunt on July 7 and would love to hear from the research community:
What's the biggest bottleneck in your current qual research workflow recruiting, moderation, analysis, or something else entirely?
Drop your answer below we read every response and it directly shapes what we build next.