Analysis Pipeline

Understand how NLP models map transcript events to signals, risk flags, and KPI extraction outputs.

Pipeline Overview

The analysis pipeline transforms transcript artifacts into structured findings, entities, and metrics that can support coaching, QA/compliance review, and operational reporting.

Signal Detection Categories

- Sentiment and emotional trajectory
- Objections and objection category mapping
- Buying intent and opportunity cues
- Urgency and escalation indicators
- Topic and intent classification
- QA and compliance language signals

Stage-by-Stage Processing

1. Transcript Preparation

Load transcript and segment artifacts with speaker context and metadata normalization.

2. Signal Detection

Detect language patterns tied to intent, sentiment, objections, urgency, and policy-relevant behavior.

3. Finding and Entity Structuring

Persist structured findings/entities with traceable context for retrieval and downstream scoring.

4. KPI Mapping

Map findings and entities into metric-level and rubric-level outputs for reporting and scorecards.

5. Delivery Packaging

Publish call-level outputs to dashboard detail views, API retrieval paths, and export artifacts.

From Findings to Metrics

Analysis stages produce structured findings and entities first. KPI mapping then converts those artifacts into metric outputs and score components so dashboards, exports, and APIs share a common interpretation layer.

Concrete Example Flow

Step 1: Customer says: "I need to think about price."
Step 2: Objection detector flags an objection event.
Step 3: Objection is categorized as pricing.
Step 4: Sentiment trajectory softens during objection exchange.
Step 5: Follow-up risk is flagged if next-step commitment is weak.
Step 6: Output bundle includes objection category, sentiment shift, and follow-up recommendation.

Representative Output Artifacts

- Call-level summaries and highlighted evidence snippets.
- Signal bundles (intent, objection, urgency, risk/opportunity).
- KPI/scorecard-ready metric outputs.
- Review and governance context for QA/compliance workflows.

Quality and Governance Notes

Pipeline quality depends on transcript fidelity, dictionary/rubric alignment, and metadata completeness. Use canonical KPI definitions and documented interpretation rules to avoid inconsistent scoring across teams and reporting layers.