Mira

About Mira

Mira is an AI research tool that runs the full qualitative study workflow - from planning and global participant recruitment to dynamic interviewing and automated reporting. It captures both what participants say and their non-verbal responses through real-time facial coding, voice emotion analysis, and webcam eye tracking. The platform is built on 17 patents, supports 70+ languages, and is used by brands including Unilever and Nestlé.

Review

Mira positions itself as an AI moderator that goes beyond transcription by reading emotional and attentional signals during live interviews. The tool handles recruitment, moderates conversations with follow-up probing, and generates research outputs like theme extraction and highlight reels. Its core differentiator is the simultaneous capture of verbal and non-verbal data, something the makers refer to as addressing the "say-do gap."

Key Features

• AI-moderated interviews that dynamically probe based on participant responses and detected emotional cues
• Real-time facial expression coding, voice emotion analysis, and webcam-based eye tracking, all running in-browser without extra hardware
• Built-in participant recruitment from a panel of over 100 million people across 120 countries, with demographic and screener filters
• Automated post-session output: speaker-separated transcripts, AI-generated themes, tags, summaries, key quotes, and a full research report with emotional timelines
• Cross-study intelligence that allows searching and comparing findings across multiple projects in a centralized repository

Pricing and Value

Pricing details are not publicly listed. The makers offer a first study free with the code PH20, but no information about ongoing subscription tiers, per-study costs, or enterprise plans is available at this time. The company has raised a $25M Series B, indicating significant backing, but the cost structure for regular use remains undefined in the provided materials.

Pros

• Captures non-verbal signals (facial expressions, voice tone, gaze) alongside transcripts, surfacing moments where verbal and emotional data disagree
• Recruitment is integrated directly into the platform, eliminating the need for third-party panel services
• Reports include confidence scores for emotional readings, and raw data remains accessible so researchers can verify AI-generated insights
• Works on standard webcams and microphones, with a pre-session calibration to improve data quality under varying conditions
• Supports 70+ languages and a large global panel, making multi-country studies logistically simpler

Cons

• Participants who decline webcam access lose the facial coding and eye tracking layers; the system falls back to voice and text only, which may reduce insight depth for those sessions
• Webcam-based eye tracking is less precise than lab-grade infrared equipment, so gaze data is limited to broader attention patterns rather than fine-grained heatmaps
• Mira is not well suited for small teams or solo researchers who need ad-hoc, conversational user interviews without structured reporting; the automated pipeline adds overhead for informal exploratory work

Mira fits teams that run frequent, structured qualitative studies and want to scale participant reach while capturing emotional and attentional data that transcript-only tools miss. Its automated reporting and cross-study repository could speed up synthesis for research leads at mid-size to large product organizations. Researchers who prefer full control over moderation style or need lab-precision biometrics will likely find gaps, but for high-volume concept testing or brand perception work where the say-do gap matters, the tool's direct signal capture is a functional match.

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