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 idea of AI moderating interviews based on how people feel is interesting because tone can be pretty subjective. I’m curious how you balance consistency with making the conversation feel natural rather than scripted.
Mira
@amjad_shaik
Great question. To clarify how this actually works: Mira doesn't change questions based on detected emotion the conversation flow adapts only based on what the participant says (their actual answers), which keeps it natural rather than scripted.
The emotion layer runs separately, in parallel. It's predicting/measuring how someone feels while a question is being asked or answered, and that signal is surfaced to the researcher as a distinct layer of insight not fed back into the conversation logic itself.
So the moderation stays consistent and conversational because it's driven by response content, while the emotional analysis exists purely as a research lens on top, for understanding reactions after the fact. Two separate systems doing two separate jobs.
The Say-Do gap framing is sharp, and running recruit → moderate → analyze → report as one agent is the ambitious part. For a team that already has research infra, two setup questions: can I bring my own recruited participants into a Mira study, or is moderation locked to your 100M panel? And can I export the raw session data — transcripts plus the behavioral signals you capture — into our own repo, or does the analysis stay inside Mira's dashboards?
Mira
@noctis06 Good questions, both get asked by every team with existing infra, so let me be precise rather than salesy.
Bring your own participants: yes. Moderation isn't locked to our panel — you can pipe in your own recruited participants and Mira runs the same moderate → analyze pipeline on them. Two ways teams do this depending on setup:
Link-based: we generate a session link per participant, you distribute through your own recruiting/incentive flow, they land in the same moderated session experience.
API-based: if you've got a recruiting/panel system already, you can push participants in via API and we handle scheduling + session invites from there.
On export: yes, and I'll be specific about what "raw" means. CSV export gives you:
Full transcripts (turn-by-turn, timestamped)
The say-do gap annotations themselves (where the model flagged claimed vs. observed behavior divergence, with the reasoning trace)
Nothing stays trapped in the dashboard-only view. The dashboard is a convenience layer, not the source of truth — the source of truth is the same data you'd get in the export.
I think the difficult part is whether those signals truly reflect what someone is feeling at that moment. Sometimes the face expression, eye movement can mean different things depending on the person and the context. How do you validate that the emotional signals are accurate ?
Mira
@reda_roqai_chaoui That's the right challenge to raise, and honestly, it's why we don't treat any single signal as ground truth.
A raised eyebrow or gaze shift can mean genuinely different things depending on the person, culture, and context we agree completely. That's precisely why MIRA fuses multiple channels (facial, vocal, speech patterns) rather than scoring emotion off one modality. When signals agree across channels, confidence in the read is high. When they conflict, that's flagged as ambiguous rather than forced into a clean label.
running facial coding and eye tracking across 120 countries means dealing with wildly different consent and biometric data laws (BIPA in Illinois, GDPR in the EU, etc). is that handled per-region automatically or does it fall on the researcher to configure what's legal where they're recruiting from?
Mira
@omri_ben_shoham1 It is handled automatically by our platform—researchers never have to manually configure local legal frameworks. We are fully GDPR compliant (and SOC 2 Type II certified), seamlessly managing global biometric laws like BIPA across 120+ countries. First, we require explicit consent from every single respondent before initiating eye tracking and facial coding, ensuring universal legal alignment. Second, we leverage Edge Processing to calculate gaze and facial metrics locally in real time, meaning sensitive raw video streams are never transmitted across borders or stored centrally. You simply launch your study, and our infrastructure ensures every session is legally watertight and privacy-first!
the edge processing point is the one that actually reassures me, not shipping raw video across borders closes off a whole category of risk. the part I'd still want to see before trusting it fully is what the consent flow actually looks like from the respondent's side, is it a real explanation of what's being captured or a buried checkbox they click through to get to the questions
Mira
@omri_ben_shoham1 When it comes to building trust. To ensure the consent flow is never a 'buried checkbox,' we use a strict, two-step opt-in process before a respondent ever enters the study:1. We integrate with third-party panels where respondents must explicitly opt in to webcam and microphone access at the profile level. Only participants who have already consented to webcam-based studies receive the invite link. 2. In-App Instruction & Consent Screen (Second Gate) Even with panel-level pre-screening, we do not assume consent. When a respondent clicks the study link, they arrive at a dedicated instruction screen before the test begins.
two gates is a solid answer, that actually addresses it. one more thing I'd wonder about as a respondent: can I revoke consent mid-session if I get uncomfortable partway through, or is it all-or-nothing at the start?
How do i verify the accuracy of Facial coding and eye tracking?
Mira
@jitender_pankaj1 Our algorithms are trained and validated with millions of data points we collected over the years with our in house data collection and tagging platform. We ensure each tag has high inter rater reliability before accepting it in our dataset. Our eye tracking algorithms are frequently tested against the data from a physical eye trackers.
How does the pricing actually scale for smaller teams or solo researchers who don't need the full enterprise setup, and is there any way to try it before committing?
Mira
@barwcts The best starting point is the free study, no commitment, no credit card. Mention PH when you book: Book a Demo: See Entropik Decode in Action
For pricing beyond that, it scales based on study volume which can be purchased in smaller blocks. Happy to walk through what makes sense for your setup. Drop a note or book a quick call.
"Said yes, looked confused" catching that in real time instead of
buried in hour 3 of a recording is the kind of detail that makes me
trust the rest of the data way more.
Mira
@ulykbek11 When our models see the face or voice disagreeing with the transcript, the AI doesn't guess it just uses the live agent to ask the user: "I noticed a quick pause there what were you thinking?" That real-time feedback loop is what turns messy, subjective signals into data you can actually build on.