🎙️ Capabilities Showcase

From One Hour of Audio
Ten Hours of Intelligence

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What your meeting tool captured · what we add on top

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📝

Motion · Text Recap

  • 17 action items captured
  • 13 topic sections summarized
  • Decisions logged as bullet points
  • No speaker-level conviction
  • No acoustic stress markers
  • No conviction-vs-content divergence
  • No group-dynamics surfacing
🎧

Beyond Physician · Voice Intelligence

  • All of Motion's text layer
  • Plus per-speaker conviction scoring
  • Plus 6-dimension emotional posture
  • Plus acoustic stress and disfluency mapping
  • Plus conviction-vs-content divergence flags
  • Plus group dominance and alignment
  • Patent-pending · zero third-party APIs

What this analysis produces

    🎯 Motion-Foil Pattern

    Action Items · what Motion captured vs. what BP reveals

    17 action items from Motion. Each one happened at a specific moment. Each commit moment has an acoustic signature.

    ⏳ Pending Phase B Run 2

    BP conviction scores and acoustic flags populate once N=4 speaker diarization completes. Motion captures and estimated timestamps shown below now.

    # Motion captured Motion owner ~ Time BP-confirmed owner Conviction Flag
    👥 Per-Speaker Intelligence

    4 voices · 70 minutes · two sides of the table

    ISIA on one side, OSA on the other. Each speaker gets a six-dimension acoustic profile, a conviction score, and a quote bank tied to audio playback.

    🔗 Cross-Speaker Dynamics

    Who held the room · who said what · who agreed with whom

    Beyond per-speaker analysis: how the 4 voices interacted, where conviction concentrated, where alignment broke.

    Who held the room

    Speaking time as a fraction of total meeting.

    Pitcher-vs-decider acoustic asymmetry

    Average emotion scores aggregated by side. The OSA side projects maximum-confidence selling mode · the ISIA side carries roughly 2x the concern signal. Healthy pitcher-decider asymmetry · flat parity would be a red flag.

    Where acoustic patterns ≠ stated content

    The marquee Beyond Physician findings · moments the meeting recap cannot surface.

    How each voice fills space

    Per-speaker fillers per minute · plus top three filler patterns. David's "like" concentrates in the 20:00 equity explainer; Patel's "right" is confirmation-seeking, not hesitation.

    Who emphasized · who just talked

    Who navigated the agenda

    First utterance per topic · indicator of who steered the meeting from section to section. Patel opened 6 of 13 · he wasn't just dominant in speaking time, he set the topic flow.

    The 13 sections · who held each

    David ran the SELL · Patel ran his own PRACTICE OPS · two parallel meetings in one.

    📜 Patent-Pending Framework

    How Beyond Physician reads voice

    Verbatim transcription + proprietary acoustic conviction analysis + dimension-mapped quote extraction. Zero third-party APIs. All MIT/ISC/BSD components.

    Proprietary 60 · 25 · 15 weighting

    Every score is a weighted blend of three independent layers. The split is the patent-pending product of years of voice research on healthcare interview data.

    60%
    Sentiment
    25%
    Emotional Conviction
    15%
    Audio Analysis

    9 phases · per recording

    PhaseStepWhat happens

    Six universal emotion dimensions · proprietary BP layer

    DimensionAcoustic signature
    🔐 Self-owned · no third parties

    📏 Sample size acknowledgment