Definition
Conversation intelligence (CI) is a class of software that applies AI — speech-to-text, NLP and large language models — to customer conversations. It converts audio into structured data, then extracts business meaning: what customers wanted, how it was handled, what happened next.
How it works
- Transcription: audio to text, with speaker separation.
- Semantic analysis: topics, sentiment, quality scoring against a scorecard, compliance flags.
- Aggregation: dashboards, trends, actionable alerts.
What it's NOT
- Not a voicebot — CI doesn't talk to customers.
- Not agent assist — CI runs post-call, not during the call (in most implementations).
- Not IVR — CI analyzes what happened, not how the call was routed.
Use cases
Quality assurance (auto-QA on 100% of calls), compliance monitoring, sales coaching, root-cause analysis of contact reasons, CX and NPS explanation.
Conversation intelligence vs speech analytics vs call analytics
Three related but distinct terms:
- Speech analytics — the audio-to-data layer (transcription, phrase spotting, sentiment).
- Conversation intelligence — the business layer on top (scoring, insights, action).
- Call center analytics — the reporting/dashboard layer combining both.
How to evaluate a platform
Checklist:
- Coverage: 100% or sampled?
- Languages: does your primary language work well? Test on your calls.
- Data residency: EU/on-premise options?
- Scorecard fit: can it map your existing QA sheet 1:1?
- Pricing model: per-seat vs per-minute — which matches your seasonality?
- Real-time or post-call? Post-call is standard; real-time is a different category.