Generative AI Call Guidance for Real-Time Context-Based Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current customer-agent interaction systems lack context-based, real-time recommendations, failing to adapt to problematic behaviors during conversations, thereby limiting the ability of agents to provide effective customer service or technical support.
Innovation Solution
An AI-based call response system integrating a real-time interactive guidance module and a generative AI module to analyze customer-agent interactions, providing context-based responses and recommendations by transcribing conversations, identifying problematic behaviors, and generating alerts and prompts for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic interactive recommendations are provided, then the system is simple to implement, but the recommendations are not adaptable to the context of the interaction between customer and agent
Solution Approach 1:
The system continuously monitors the conversation transcript in real-time and uses this feedback to dynamically adjust recommendations. The generative AI model processes ongoing dialogue context and provides contextually relevant suggestions that adapt to the evolving interaction between customer and agent, resolving the contradiction between adaptability and complexity through intelligent feedback loops
Solution Approach 2:
The system automatically generates, monitors, and adjusts recommendations without requiring manual intervention. The generative AI model self-regulates based on conversation context, autonomously providing adaptive recommendations that match the real-time interaction state, thereby achieving high adaptability while managing system complexity through automated self-service mechanisms
2Productivity
If real-time analysis of conversations is performed, then context-based recommendations can be provided, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by continuously monitoring and indexing conversation transcripts as they occur. Pre-processing techniques prepare the conversation context in advance, allowing the generative AI model to quickly generate recommendations without significant processing delays, thus achieving real-time responsiveness while minimizing computational time consumption
Solution Approach 2:
The system processes only the necessary portions of the conversation context rather than analyzing every detail exhaustively. By selectively focusing on key interaction elements and using efficient generative models, the system achieves real-time recommendation generation with reduced processing time and computational resource consumption
3Reliability
If agents receive frequent recommendations, then their performance improves, but disruption to the natural flow of conversation increases
Solution Approach 1:
The system dynamically adjusts recommendation delivery based on conversation state and context. The generative AI model analyzes ongoing dialogue to determine optimal moments for intervention, providing recommendations only when they add value without disrupting natural conversation flow. This dynamic adaptation maintains agent performance while preserving ease of operation
Solution Approach 2:
The system uses feedback from conversation monitoring to intelligently timing recommendations. By continuously assessing interaction context and agent-customer engagement levels, the system provides feedback-based recommendations at optimal moments that support agent performance while minimizing disruption to natural conversation flow
Data Source
AI summary
An artificial intelligence (AI)-based call response system and methods are provided that are configured to provide a context-based recommendation during a monitored conversation. The AI-based call response system includes a processor to perform conversation analysis operations, including determining transcribed words for the monitored conversation, analyzing the words using one or more machine learning (ML) models to produce a score associated with a model identifier (ID) identifying a ML model, comparing the score to a predefined threshold of the ML model, generating an alert when the score meets or exceeds the threshold, the alert including the model ID and a call identifier (ID) identifying the monitored conversation, creating one or more prompts with each prompt comprising an executable instruction that prompts, queries, or requests an output from a large language model for a response, retrieving the response for each of the prompts, and providing the response to a user.