Workforce Engagement Management

Hello community,

We have been testing AI Scoring / Virtual Supervisor for QA automation and noticed some limitations around time-based evaluations.

Example:

• "Agent must greet within 10 seconds"
• "No excessive silence"
• "Dead air detection"

What we observed:
The AI seems heavily transcript-driven and not truly aware of acoustic timing or participant silence duration.

In some scenarios:

• customer speaks first
• agent remains silent
• but the AI still interprets the interaction as an active greeting/opening

This creates false positives for timing-based compliance rules.

Our current conclusion is:

• AI Scoring works very well for empathy, ownership, soft skills and conversational quality
• Acoustic/timestamp KPIs still belong to Speech Analytics / Topics / Acoustic Metrics

Curious how others are balancing:

• LLM-based evaluations
  vs
• deterministic acoustic metrics

Have you found effective prompt engineering strategies for timing-sensitive QA scenarios?

#AIScoring(VirtualSupervisor)

#AIScoring(VirtualSupervisor)

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Gabriel Garcia
NA
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Hello @Gabriel Garcia, how are you?

Your conclusion aligns with what I've seen in production.

AI Scoring today is much stronger for:

• semantic interpretation
• empathy
• compliance language
• conversational behavior
• than for deterministic acoustic analysis.
• Metrics like:
• greeting within X seconds
• silence thresholds
• interruption timing
• exact hold duration
• are still much more reliable using:
• Speech Analytics
• Acoustic Metrics
• Interaction Categories
• Topics/Programs

Prompt engineering can improve participant/context awareness, but it won't fully replace true timestamp/acoustic analysis because the LLM is primarily transcript-oriented.

I hope this answer is helpful to you.

Regards!

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Lilian Lira
Services and Developer Manager
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