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.
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.
The facial coding and emotion AI combo is wild — usually I get a wall of quotes to sift through, but here it flagged where respondents actually felt something. Made the insight pull way faster than expected.
Mira
@doanj7hh "Flagged where respondents actually felt something" is the signal we built toward — not a wall of quotes, but the specific moments that matter emotionally. Glad the insight pull was faster than expected.
Mira
Hi all,
Marketing lead at Decode here.
I have spent the last few months working closely with this product to build the launch. The thing that struck me most: most AI interview tools stop at the transcript. Mira treats that as the starting point.
A few capabilities people miss: AI follow-up probing that automatically asks "why?" and "tell me more" mid-interview based on what the participant actually said. AI highlight reels that automatically pull emotional moments, so you do not have to share 40-minute recordings with stakeholders. Cross-study intelligence that finds recurring themes across multiple research projects over time.
The multimodal layer — emotion AI on top of the interview — makes findings more defensible. You are not just quoting a participant. You are showing what they felt when they said it.
Happy to answer questions about the product or how we built the launch.
Strength here is catching reaction people wouldn't admit to on a survey. Weakness is that everything depend on decent webcam and lighting, which won't be true for every participants in every country.
Mira
@ian_maxwell2 Fair and accurate, Ian.
The webcam dependency is a real constraint and we are honest about it. We handle it through pre-session calibration and per-frame confidence scoring, weak frames are omitted rather than guessed.
For participants in lower-quality environments the voice emotion and transcript layers still operate fully, so the study is never purely dependent on the video feed. That said, you are right that it is a variable, and it is why we are clearer about this in our documentation than most tools in the space.
How does the facial coding and emotion AI handle privacy and consent across different regions, especially with GDPR and other strict regulations?
Mira
@ufukaktugba
Hi Ufuk, great question.
Consent is handled per session: participants see a clear explanation of what data will be captured (facial, voice, eye) before any session begins and must actively opt in.
On GDPR: we are fully GDPR compliant. Data is processed within compliant infrastructure, participants can request deletion, and researchers control retention policies. We also have SOC 2 Type II certification.
For APAC markets we follow regional equivalents (PDPA in Singapore, PIPL in China). If you are evaluating for a specific region, happy to go deeper.
This is a really interesting take on AI-powered research.👏🏻
Going beyond transcripts to capture emotions and behavioral signals could unlock much richer insights for product teams. Curious, how do you balance those advanced features with participant privacy and consent during interviews?
Mira
@worksforme Hi Laiba — the balance is built into the session flow itself. Every participant gets a clear explanation of what will be captured before they join — webcam access for facial and eye data, microphone for voice emotion. They can decline any of these and still participate, with the system adapting to use only the available signals.
No facial recognition is used anywhere — we only measure expressions, not identity. Data is never sold or used for external training without explicit permission. Researchers control retention and deletion.
Happy to walk through the full consent flow if that would help.
How does the facial coding and emotion AI actually perform across different cultural contexts, and do participants need any special setup or permissions on their end for it to work smoothly?
Mira
@duyguridapagq Our emotion AI is trained to understand the demographic difference while predicting emotions using the face. Later we do a few normalisations based on the benchmarks we have collected to give you unbiased scores and emotion data points. We just need permission to use the microphone and the webcam.