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.
How does the facial coding and eye tracking actually work in a remote setting without making participants uncomfortable or needing specialized hardware?
Mira
@layda138459 Everything runs in the browser — no app download, no plugin, no special hardware. Participants just allow webcam and microphone access when they join, same as any video call. Before the session starts there is a quick calibration check for lighting and camera positioning, which also helps participants get comfortable with the setup before the interview begins. The calibration doubles as a way to normalise the experience — by the time the actual interview starts, most participants have forgotten the camera is doing anything beyond a normal call.
Mira
@layda138459 Everything runs in the browser on a standard laptop or phone, no external hardware and nothing to install. Participants grant camera access upfront, run a short calibration, and the session feels like a normal video call from there.
If someone isn't comfortable on camera, they decline and still complete the study, with emotion read from voice and text instead.
How does the facial coding and eye tracking actually work in practice for remote participants, especially since webcam quality and lighting vary so much across users?
Mira
@ersinyzak1i75 We do not need a very high end webcam to run our models. In fact our model can run on almost all commodity webcams as long as there is good visibility of the face. We however guide the users to adjust face, lights before each session to make sure we capture good data.
Mira
@ersinyzak1i75 This is a great question and we hear this constantly from a lot of our customers during the initial stages. We have designed our system to work with all kinds of webcams, not just the best ones. Before joining, participants go through a quick calibration and environment check. Each video frame receives a confidence score, and we omit any weak frames rather than guessing. We never base our findings on a single video feed, since we combine signals from the entire group. Webcam gaze tracking is less precise than lab infrared, but for questions like whether someone saw a brand element or where their attention went first, it works well. Plus, it lets us study hundreds of real-world settings instead of just a few in a lab. We do not use facial recognition at all; we only measure expressions, not identity.
The facial coding during interviews is genuinely impressive, you can actually see hesitation or excitement that pure transcripts would completely miss. Wish more research tools took emotion seriously like this.
Mira
@mahmutkrc1pj5 Yes, we focus on understanding non verbal cues during interactions and discussions in all of our products. We believe that missing emotion signals might lead to incomplete view of customer's views during interviews and that is why we invested years perfecting multi modal emotion models to provide a comprehensive analysis of customer interviews.
Mira
@mahmutkrc1pj5 Thank you, Mahmut, this is exactly why we built it. The moment before someone chooses their words is often the most honest part of the whole interview. That is the layer that changes decisions.
How does the facial coding and eye tracking actually work in remote sessions without users installing heavy software, and does that introduce any bias from people who decline to turn on their webcam?
Mira
@memetsanayi As long as users are connected to internet, we don't require you to install any software. Everything happens right on the web browser. Without facial data, there in bias introduced as long as enough people allow us to use webcab. We have seen in our research that the data for emotion converges beyond a point, which means we will have reduced bias as more people take the test.
Mira
@memetsanayi Everything works right in your browser, so you don’t need to download anything or install plugins. Participants just allow camera access, do a quick calibration, and then the session begins. It uses the regular webcam on your laptop or phone. There is no facial recognition involved; we only measure expressions, not identity.
Camera access is always based on full consent. If someone chooses not to allow camera access, they can still take part in the study. In that case, we do not collect gaze or facial signals, but we still measure emotion using voice tone and text. Declining the camera just removes one type of signal, but it does not exclude the participant or reduce the sample size.
the facial coding piece is wild, watching the emotion data overlay during interviews made patterns jump out that a transcript alone would totally miss. really curious how it handles cross-cultural nuance though.
Mira
@burcu192820 Glad the overlay made patterns visible that would have stayed buried in a transcript. On cross-cultural nuance — our models are trained across demographic groups, not on a single population, and we apply normalisation based on cultural benchmarks to reduce bias in emotional scoring. We do not claim perfect universality across cultures — emotion expression varies and it is an ongoing area of work. Our recommendation for global studies is always to combine the emotional signal with the transcript rather than reading either in isolation. The two together are more reliable than either alone.
Mira
Head of Data Science at Decode here.
The question we kept asking ourselves: how do you build an AI that understands the difference between what someone says and what they mean?
The answer is multimodal signal fusion — combining facial action units, vocal pitch, speech rate, micro-expressions, and gaze patterns into a single emotional signal per moment of the interview. Not post-hoc analysis. In real time, during the conversation.
The Voice Emotion AI specifically analyzes confidence, hesitation, excitement, and frustration from the audio layer independently of the transcript. Tone often carries a completely different story than words. That layer is invisible in every transcript-only tool.
17 patents cover the core methodologies. Happy to discuss the technical depth of any of these.
Mira
Senior Director of Consumer Insights at Decode here.
Speaking as a practitioner, the feature I find most useful day-to-day is AI highlight reels. Instead of asking a stakeholder to watch hours of recordings, Mira clips the moments that matter: where a participant's hesitation revealed unspoken doubt, where genuine excitement came through before they could temper it, where what they said and what their face showed did not match.
Those moments are what change decisions in a review meeting. And they surface in minutes, not days of manual review.
The AI Copilot is the other one worth knowing about: you can ask "which participants mentioned trust concerns?" or "show me everyone who reacted negatively to the pricing slide" across an entire study instantly.
For researchers here, what type of qualitative research do you run most? Happy to walk through how this fits your workflow.