ReplayAutonomous app testing for teams who ship at AI-speed
Promoted
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.
Dial
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 by Decode
@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!
Dial
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 by Decode
@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.
Dial
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?
Decode by Entropik
@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.
"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 by Decode
@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.
Respect for not hand-waving that, most launch threads would have. One thing I'd add: per-frame confidence is the model scoring its own certainty, so it won't catch systematic bias. A model can be high-confidence and wrong the same way across a whole population and never flag it. The only check I trust is human-coded ground truth sampled per region, which is painful to collect. Which regions have you actually validated against local human coders versus carried over from the base model?
Mira by Decode
Head of Sales at Decode here.
The conversation I have most with research and insights teams: "Our studies take too long, and leadership does not trust the findings."
Both problems have the same root cause. The tools being used only capture what people say, and analysis is manual. A 20-participant qual study typically takes 6-8 weeks from setup to report, arriving too late to influence the decision it was commissioned to support.
Mira compresses that timeline. Study setup with templates takes minutes. Recruitment from a 100M+ global panel is built in. Transcripts, themes, and reports are generated automatically. Emotional signal adds defensibility to findings.
If you run an insights function and want to understand what this looks like for your team's specific workflow, feel free to ask below.
Onepane
congrats! user interviews are the part of building I always end up skipping because of the effort. this could actually make me do them
Decode by Entropik
@ashmil_hussain Yes our entire mission behind Decode is to democratise user research. We want all the creators and builders to have data backing their creative decisions and AI guiding them to take better decisions.
SoundGate Guitar
Very impressive product. Combining AI moderation with emotion analysis and automated research reports could save research teams a huge amount of time. Congrats on the launch!!!
One question- how do you address privacy concerns around facial coding and emotion analysis, especially for participants in regulated industries or regions with stricter data protection laws?