Mira - AI moderated interviews that read how people feel

@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.

@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.

@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.

@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.

@serhatmant9b7b Accessibility was a deliberate decision. Emotion AI in research has historically been available only to large enterprise teams with big tooling budgets. Glad that came through.

@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.

@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.

@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.

@srammoa "Closer to sitting with a respondent" is one of the best ways I have heard it described. The transcript tells you what they said. Mira tries to preserve what was happening underneath that. Thank you for testing it.

@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.

@nihalzsaki243g The hesitation catch is where the voice layer earns its place; that pause before the polished answer is usually the most honest moment in the whole session. Glad it surfaced. If you want to run a structured study, the first one is free this month. Please feel free to book a demo at https://www.entropik.io/platform/ai-moderator and mention PH20 on the call :)