Customer interactions hold a wealth of insights, but manual evaluation is slow, inconsistent, and reactive. This session reveals how AI-powered conversation intelligence transforms raw call audio into actionable business insights. By leveraging Google’s advanced speech-to-text and emotion-analysis technologies, Neo4j graph databases, and Generative AI, we automate the identification of customer pain points, sentiment trends, and agent performance metrics—enabling real-time, scalable quality assurance and continuous coaching.
We’ll walk through a cross-functional pipeline: from live call ingestion and transcription, sentiment and intent analysis, to structured graph storage in Neo4j, and finally visualization of interaction patterns and agent scorecards. Attendees will see how this framework equips managers with real-time dashboards, automated evaluation frameworks, and triggers for root-cause investigations—raising the bar for customer care excellence.

Key Takeaways
- From Voice to Insights via Google’s Audio Tech
# Learn how to use Google’s speech-to-text API, audio emotion detection, and sentiment analysis to extract intent, emotion, and language patterns from live customer calls.
# Understand the importance of accurately capturing both what customers say and how they say it.

- Modeling Conversational Analytics in Neo4j
# See how to structure call metadata, speakers, intents, sentiment flows, and tags within a Neo4j graph
# Discover why relationships (e.g., call → query type → agent response) empower pattern discovery and timely insight alerts .

- Automated Detection of Pain Points & Coaching Needs
# Learn how AI identifies recurring customer complaints, root-causes, and potential service failures automatically
# Dive into agent performance scoring frameworks—measuring factors like empathy, response accuracy, and compliance—at scale.

- Live Quality Scoring & Manager Dashboards
# Understand how real-time dashboards ingest graph updates from Neo4j to reflect emerging trends, agent scoring, and customer sentiment distribution.
# Explore alerting systems that notify managers of low-quality exchanges or rising negative sentiment clusters.

- Scalable, Explainable, and Continuous Improvement
# See how graphs enable explainability: managers can trace a low score to specific call segments and transcripts.
# Learn how the pipeline supports continuous learning—enhancing speech models, conversation flows, and agent training based on data-driven insights.