Mira - AI moderated interviews that read how people feel
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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.
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The Say-Do Gap framing is the sharpest part of this — self-reported data being "socially edited" is exactly the failure mode most research tools quietly inherit. My honest question on the emotion layer: facial coding and voice-emotion signals vary a lot across cultures and neurotypes, so how do you keep the "feel" read from becoming its own bias, especially across 120 countries? Curious whether researchers can see and override the affect signals, or whether they're treated as ground truth in the final report.
Mira
@yers1t Researchers see the raw signal, not just a conclusion. Every emotion score is timestamped and tied back to the exact video/audio moment, so a researcher can watch the clip and agree, disagree, or override it. Nothing gets baked into a final report without that human check.
Two things... first, how does the "Real time facial encoding" work? Secondly, this seems like an interesting idea, but im curious regarding how you plan to deal with jobseekers, inevitably, despising software like this. Sure businesses trying to cut costs would love something like this, but on the consumer/job-seeker side how will you mitigate and ensure a candidate doesn't feel like a robot is screening them.
Mira
@lucapiekarski Great questions!
Real-time facial encoding: We track facial action units (the individual muscle movements that make up expressions) frame by frame during the session
On the job-seeker concern: Totally fair question, but MIRA isn't built for hiring or candidate screening. We're purpose-built for user research, think usability testing, customer interviews, product feedback sessions. Participants are opted-in research subjects, not candidates being evaluated for a job. The goal is understanding genuine reactions to a product or concept, not judging a person.
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.
"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.
the disagreement-not-resolved answer above is good, but does the participant themselves know that level of emotional inference is happening? "we're recording this call" is a different consent than "we're scoring your face for confusion/disengagement in real time." biometric emotion inference specifically is called out under GDPR and the EU AI Act in a way plain video recording isn't. across 120 countries with different disclosure standards, is that spelled out to participants upfront, or folded into a generic research-consent form
Mira
@galdayan You're completely right—under GDPR and the EU AI Act, scoring facial expressions is a totally different legal hurdle than just recording a video. We never hide this in a generic research consent form; participants get a clear, upfront disclosure that their eye movements and facial reactions will be analyzed in real time before they opt in. Because we use edge processing, we also explain that the computing happens locally on their device, meaning sensitive raw video is never stored or shipped across borders. We made this specific, high-bar biometric opt-in our automatic default across all 120+ countries, taking the entire disclosure burden off the research team.
@bharat_shekhawat edge processing was the detail I was missing, that actually changes the risk profile a lot since the sensitive data never leaves the device. making the high-bar opt-in the default everywhere instead of only where required is a good sign too, most teams do it the other way around and only add friction where regulators force them to.
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?