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Explore oruk

Meeting intelligence

Review the words and tone of meetings

Search recorded meetings with transcripts and timed emotion and speaking-style labels. Replay changes in delivery alongside the words.

For teams building this workflow into their own product. Start with prerecorded English audio; Oruk returns structured analysis through the API.

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Illustrative output

One timeline for words and changes in delivery

That sounds promising. I still want to understand what happens if the timeline slips.

hopeful 0.72skeptical 0.61formal 0.48

Example labels for this interface concept. Analyze your recording to inspect actual model output.

Unified analysis request
curl https://speech-api.oruk.ai/v1/audio/analysis \
  -H "Authorization: Bearer $ORUK_API_KEY" \
  -H "X-Request-ID: $(uuidgen)" \
  -F "model=oruk-resonance" \
  -F "file=@recording.wav"

Example implementation

Where Oruk fits in your workflow

Your input
A completed English meeting recording with participant consent and access inherited from the meeting workspace.
Oruk returns
Transcript segments, timestamps, and vocal-expression scores for the recording.
Your application
Index the transcript in your meeting product, add filters for acoustic labels, and let users jump to the matching audio before adding a note.

Evaluate the result in your pilot

Compare how quickly users retrieve relevant passages with transcript-only search. Review whether acoustic filters help on your actual microphones and meeting formats.

How it works

01

Capture with consent

Record the meeting using the product’s existing consent and retention controls.

02

Analyze after completion

Submit the file to unified analysis and retain only the response fields needed by the application.

03

Support review

Let users search the transcript, inspect timed labels, and listen to the recording in context.

What you can build

  • Retrieve moments with both text and delivery context
  • Support qualitative meeting review
  • Summarize aggregate expression patterns with uncertainty
  • Improve search across recorded discussions

Deployment safeguards

  • Do not infer agreement, honesty, intent, or private mental state from a label
  • Keep access to sensitive meeting analysis tightly scoped
  • Provide deletion and correction workflows in the host product
  • Evaluate the actual room acoustics and microphone configuration
Responsible-use guidance

Related resources

AI call analytics Contact center analytics Voice call sentiment analysis Voice of customer analytics Sales call analysis Qualitative research speech analysis Speech analytics API Speech emotion recognition API API capabilities and scope API documentation Build a speaker-turn review workflow Stream transcription and phrase emotion Pricing