Mira - AI moderated interviews that read how people feel

@ridhwikvinod Ridhwik, this is exactly the right question, and honestly the one we obsessed over the most.

A few things we do to avoid over-reading:

1. Confidence thresholds, not interpolation. If a frame doesn't meet our confidence threshold (lighting, angle, partial occlusion), we drop it entirely rather than fill in the gap. We'd rather have less data than wrong data.

2. Sustained patterns, not moments. A single frown frame means nothing. We look for emotional patterns sustained across 3–5 seconds minimum before flagging them as signal.

3. Triangulation across modalities. Facial expression is one input, we cross-reference with voice tone and eye tracking. Confusion and awkwardness have different voice signatures. That cross-modal agreement is what lifts confidence.

4. Baseline calibration. We establish a neutral baseline in the first 30 seconds of every session so "this person just frowns a lot" doesn't skew the read.

The honest answer is it's not perfect, but neither is a human moderator. The difference is we surface the signal with confidence scores, so researchers can decide what to trust. Happy to walk you through exactly how this works in a live session → https://www.entropik.io/book-demo

@david_vilalta David, this is the question we lost sleep over. A leading probe is almost worse than no probe — it plants the narrative.

A few design decisions we made:

Probes are behaviorally triggered but linguistically neutral. When Mira detects a signal — a hesitation, an emotional shift, a response that doesn't match the facial/voice pattern — it doesn't say "you seemed confused." It says something like "You paused there — tell me more about what was going through your mind." The trigger is emotional, the language is open.

No interpretive language in the probe. Mira never names the emotion it detected back to the participant. It asks outward, not inward. This is a deliberate constraint built into the probing engine.

Tunable per study type, not per participant. Concept testing probes are calibrated differently from usability or brand research — because the type of signal differs. But within a study, every participant gets the same probe structure. That keeps cross-participant comparisons valid.

Probe depth is capped. Max 2 levels of follow-up on any one signal — so the interview doesn't become an interrogation.

The honest caveat: no probe is perfectly neutral. But compared to human moderators who vary tone and word choice across 40 interviews — Mira is at least consistently neutral. Happy to show you a live session → https://www.entropik.io/book-demo

@david_vilalta Sharp concern, and one worth taking seriously.

The short answer: the questions might converge, but the insight won't — and here's why.

The emotional layer is proprietary to each brand's product. Two competing brands can ask participants the same question about their respective checkout flows. The language of the answers might look similar. But Mira's facial coding and voice emotion data will surface where frustration spikes, which exact moment trust drops, what triggers genuine delight — and that's product-specific. You can't benchmark emotion.

Research memory compounds differently for each brand. Mira builds cross-study intelligence over time — recurring themes, longitudinal shifts, segment-level emotional patterns. A brand that's been running studies for 12 months has a fundamentally different starting point than one that just launched. That proprietary research memory is not replicable even with identical questions.

Speed becomes the moat, not secrecy. If every brand could run the same study, the winner is the one that runs it first, iterates fastest, and acts before the category moves. Mira compresses weeks of manual research into hours. That time advantage is where competitive edge lives.

The ceiling isn't on insight quality — it's on how fast you can act on it. That's what we're really building for → https://www.entropik.io/book-demo

@ayeffrz Ayşe, thank you — "research instrument, not a chat wrapper" is exactly the bar we held ourselves to.

The decision to build the emotion layer into the interview flow rather than running it as a post-processing layer was deliberate. When emotion signals are captured in real time during the conversation, Mira can actually respond to them — adjusting its probing when it detects hesitation or conflict between what someone says and how they react. That closed loop is what makes it feel like a moderator, not a recorder.

The underlying models (facial coding, voice tone, eye tracking) are the same tech we've been building for 9 years across 150+ brand research programs — we just finally wired them into the interview itself.

Would love to show you what a full session looks like end to end → https://www.entropik.io/book-demo

@meryemuzuno5kq Right now, you would need camera access to have face emotion measurement or eye gaze tracking done. However, Mira also has the ability to measure emotions purely based on tone of the voice and also based on the words that are being used.