Skip to content

Try Resonance 2 early.

Tell us a little about yourself and what you’re building.

A brief description of your use case.

We’ll use these details to review your application and contact you about access. Privacy policy

Get started free

Explore oruk

Contact centers

Contact center analytics with words and tone

Turn completed calls into transcripts with emotion and speaking-style scores. Find passages for quality review and compare patterns across your recordings.

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

Try your audio Build with the APIEstimate cost

No account needed to try the public demo.

Illustrative output

Words and delivery in one view

I have already explained this twice, and I need someone to resolve it today.

frustrated 0.91impatient 0.78energetic 0.42

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 call from your recording system, with your call ID and access controls.
Oruk returns
A transcript, segment timestamps, and emotion and speaking-style scores. Keep the model and request IDs with the result.
Your application
Link each passage back to playback in your QA tool. Queue candidate passages for a reviewer, then save their assessment alongside the call.

Evaluate the result in your pilot

Compare review time and the share of useful passages found against your existing sampling process. Inspect false positives across queues, accents, and recording conditions before expanding coverage.

Try the local review panel and export passage assessments

How it works

01

Upload completed audio

Upload the completed recording for analysis after the call.

02

Receive aligned outputs

Bring the transcript, timed labels, and request ID into your existing call analytics or quality-review workflow.

03

Route human review

Replay relevant passages and review the scores alongside the transcript and call context.

What you can build

  • Prioritize a manageable review queue instead of sampling calls at random
  • Compare vocal-expression labels across a queue
  • Compare aggregate patterns across queues, teams, or time periods
  • Locate the specific moments worth replaying in a coaching session
  • Add acoustic context to transcript search, so a query can find how it was said
  • Find passages with repeated frustration labels for review

Deployment safeguards

  • Do not use emotion labels as the sole employee-performance measure
  • Validate on the microphones, languages, accents, and call types in production
  • Retain uncertainty and multiple labels rather than one definitive state
  • Disclose recording and analysis according to applicable consent rules
Responsible-use guidance

Related resources

AI call analytics Voice call sentiment analysis Voice of customer analytics Sales call analysis Meeting speech understanding 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