Energy — User Intent Classification for Enhanced Agent Interaction

Overview

The primary purpose of this DAG is to classify user intents based on interactions within the contact center, thereby facilitating improved communication between users and agents. The data sources include CRM systems, IT Service Management (ITSM) tools, and interaction logs, which provide a rich dataset for analysis. The ingestion pipeline captures this data in real-time, ensuring that the most current interactions are processed. The processing steps involve extracting user intents through natura

The primary purpose of this DAG is to classify user intents based on interactions within the contact center, thereby facilitating improved communication between users and agents. The data sources include CRM systems, IT Service Management (ITSM) tools, and interaction logs, which provide a rich dataset for analysis. The ingestion pipeline captures this data in real-time, ensuring that the most current interactions are processed. The processing steps involve extracting user intents through natural language processing, normalizing the data to ensure consistency, and applying advanced classification models to accurately categorize intents. Quality control measures are implemented at each stage to validate the accuracy of classifications, including automated checks and manual reviews. The outputs of this DAG are exposed via APIs, enabling seamless integration with RAG agents, allowing them to respond more effectively to user inquiries. Key performance indicators (KPIs) such as classification accuracy, response time improvements, and user satisfaction scores are monitored to gauge the effectiveness of the system. The business value lies in enhanced customer service, reduced handling times, and increased agent productivity, ultimately leading to improved customer loyalty and operational efficiency in the energy sector.

Part of the AI Assistants & Contact Center solution for the Energy industry.

Use cases

• Improved customer satisfaction through accurate intent recognition
• Increased agent efficiency by reducing response times
• Enhanced operational insights from detailed interaction analysis
• Higher retention rates due to better customer engagement
• Cost savings from streamlined contact center operations