Architected and deployed a GenAI-powered voice analytics pipeline at Citi, leveraging Whisper, LLMs, and embedding-based clustering to process 50M+ customer calls. Extracted complaint themes and sentiment, delivering $14M in cost savings and $72M in incremental revenue.
This large-scale voice analytics solution processed millions of customer service calls to extract actionable insights. The pipeline combined state-of-the-art speech recognition with advanced natural language understanding to identify customer pain points, sentiment trends, and operational inefficiencies.
Technical implementation: • Distributed speech-to-text processing with Whisper models • Multi-stage NLP pipeline for intent and sentiment analysis • Advanced embedding techniques for call clustering and categorization • Real-time dashboard for operational insights • Automated alert system for critical customer issues • Integration with existing CRM and customer service platforms
The solution provided unprecedented visibility into customer interactions at scale, enabling data-driven improvements across the entire customer service operation.