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Pipeline

How AI Analyzes Sales Calls

VOCAL uses a staged AI workflow to transform raw call audio into transcripts, speaker-aware signals, KPI outputs, and actionable coaching, QA, and revenue intelligence.

End-to-End Sales Call Analysis Pipeline

  1. 1. Call Ingestion

    Collect recordings from telephony sources or uploads, normalize metadata, and start stage orchestration jobs.

  2. 2. Speech-to-Text Transcription

    Generate timestamped transcript output with confidence-aware language artifacts that support search and downstream analysis.

  3. 3. Speaker Separation / Diarization

    Separate agent and customer speakers to preserve role context for talk ratio, interruption, and adherence analysis.

  4. 4. NLP Signal Detection

    Infer intent, objections, topic, urgency, sentiment shifts, buying indicators, and risk cues from call dialogue.

  5. 5. KPI Extraction

    Map detected signals to measurable metrics and scorecard frameworks used by managers and operations teams.

  6. 6. Output Delivery

    Deliver call-level outputs to dashboards, APIs, and export workflows for coaching, QA, and operational action.

Stage-by-Stage Mechanics

1. Call Ingestion

  • - Recording capture from telephony and uploaded media sources
  • - Metadata normalization for tenant, source, and call context
  • - Job orchestration initiates stage processing

2. Transcription

  • - Timestamped transcript generation for full call playback context
  • - Confidence-aware text artifacts for downstream interpretation
  • - Stored in `transcripts` and `transcript_segments` outputs

3. Speaker Separation / Diarization

  • - Distinguishes agent and customer contributions
  • - Enables talk ratio and interruption analysis
  • - Preserves role context for quality and adherence scoring

4. NLP / Signal Detection

  • - Objection, intent, urgency, and buying-signal detection
  • - Sentiment and topic analysis with context grounding
  • - Persisted in v2 findings/entity structures for retrieval

5. KPI Extraction

  • - Converts signal-level events into metric outputs
  • - Aligns to scorecards at call, agent, and team levels
  • - Supports coaching, QA, and operations dashboards

6. Delivery of Outputs

  • - Published to call-detail views and dashboard summaries
  • - Available through API retrieval patterns
  • - Exported for BI and workflow automation pipelines

Example Analysis Flow

Step 1: Customer raises a pricing objection during discovery.
Step 2: AI tags objection category as pricing and marks a sentiment downturn.
Step 3: Agent reframes with financing and timeline options.
Step 4: Customer language shifts from resistance to tentative interest.
Step 5: Output bundle includes pricing objection, financing interest, and follow-up recommendation.

What the System Stores

Call and Recording Artifacts

  • - calls
  • - recordings
  • - source metadata

Orchestration State

  • - jobs
  • - stage status
  • - processing events

Transcript Layer

  • - transcripts
  • - transcript_segments
  • - speaker context

Analysis Layer

  • - analysis_runs_v2
  • - analysis_stage_runs_v2
  • - provider/model context

Signal Outputs

  • - analysis_findings_v2
  • - analysis_entities_v2
  • - evidence references

Metric Outputs

  • - analysis_metrics_v2
  • - scorecard inputs
  • - reporting aggregates

Quality Considerations and Common Failure Modes

Poor audio quality can reduce transcript confidence and downstream extraction quality.
Overlapping speakers can distort talk-ratio and interruption metrics without robust diarization.
Missing metadata can weaken attribution and operational routing context.
Very short calls may not provide enough signal density for stable KPI interpretation.
Domain-specific terminology can require dictionary and prompt tuning to improve recall.
Manager review remains important for edge cases and high-stakes decisions.

FAQ

How does AI know who is speaking?

Speaker diarization separates and labels turns so analysis can preserve role context for agent and customer behavior.

Can AI detect sentiment from transcripts alone?

Yes, transcript language supports sentiment inference, and confidence improves when paired with timing and turn-structure context.

How are objections detected?

Models evaluate language patterns, topic context, and phrase-level evidence to classify objection events and categories.

What happens if audio quality is poor?

Lower quality audio can reduce transcript reliability and signal precision; review workflows should prioritize low-confidence calls.

Can outputs be reviewed by managers or analysts?

Yes. Structured outputs are designed for human review in dashboards, call detail views, and exported datasets.

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