GenAI Contact Center Real-Time Call Audio Integration on GCP

In the modern customer service landscape, leveraging real-time call audio data with Generative AI (GenAI) can significantly enhance contact center operations. Integrating this capability on Google Cloud Platform (GCP) allows for scalable, efficient, and intelligent solutions that support agent assist and real-time call analytics. This blog will outline the architecture for a scalable audio capture solution using WebSockets on Google Kubernetes Engine (GKE) with call metadata, providing a detailed example using Genesys Audio Hook. We will cover how to push PCM audio data to Pub/Sub, build utterances, transcribe and perform sentiment analysis, and feed utterances to Dialogflow for intent detection and fulfillment. Additionally, we will explore using Large Language Models (LLMs) for generative responses to assist agents.

Architecture Overview

Key Components

GCP Services:

• Google Kubernetes Engine (GKE): Manages containerized applications.
• Cloud Pub/Sub: Handles real-time messaging and event processing.
• Speech-to-Text API: Transcribes audio streams to text.
• BigQuery: Stores and analyzes call metadata and transcriptions.
• Dialogflow CX: Manages conversational interactions and agent assistance.
• Vertex AI: Builds and deploys custom machine learning models.
• Cloud Storage: Stores audio recordings and transcriptions.

Telephony Systems:

• Genesys
• Twilio
• Avaya
• Cisco
• Five9

Architectural Diagram

An architectural diagram illustrating the integration of real-time call audio data into a GenAI system on GCP would look like this:

Detailed Steps

Step 1: Setting Up Telephony System Integration

Example: Genesys Audio Hook

Genesys Audio Hook:

• Configure Genesys Cloud to stream audio data to GCP.
• Use the Genesys Streaming API to capture real-time audio and call metadata.
• Ensure proper authentication and secure connections between Genesys and GCP.

Step 2: Building a Scalable Audio Capture WebSocket Solution on GKE

1. Deploy WebSocket Server:

• Use GKE to deploy a WebSocket server that captures real-time audio streams from telephony systems.
• Configure the WebSocket server to handle multiple simultaneous connections, ensuring scalability and reliability.

2. Identify Speaker and Agent Channels:

• Implement logic to distinguish between customer and agent audio streams.
• Use metadata such as call IDs, timestamps, and participant identifiers to track and separate channels.

Step 3: Pushing PCM Audio Data to Pub/Sub

1. PCM Audio Data Capture:

• Capture raw PCM audio data from the WebSocket server.
• Ensure the audio data is properly formatted and encoded for transmission.

2. Publish to Pub/Sub:

• Publish the PCM audio data to Cloud Pub/Sub for further processing.
• Include relevant call metadata in the message payload to facilitate downstream processing.

Step 4: Building Utterances and Transcribing Audio

1. Audio Segmentation:

• Segment the audio stream into individual utterances based on silence detection or predefined time intervals.
• Maintain synchronization with call metadata to ensure accurate tracking.

2. Transcription:

• Use the Speech-to-Text API to transcribe the segmented audio utterances.
• Store the transcriptions in BigQuery along with associated metadata.

Step 5: Performing Sentiment Analysis

1. Sentiment Analysis:

• Apply sentiment analysis models to the transcribed text to determine the emotional tone of each utterance.
• Store sentiment scores in BigQuery for further analysis and reporting.

Step 6: Feeding Utterances to Dialogflow for Intent Detection and Fulfillment

1. Intent Detection:

• Send transcribed utterances to Dialogflow CX for intent detection.
• Use Dialogflow to identify user intents and trigger appropriate actions or responses.

2. Fulfillment:

• Configure Dialogflow to handle fulfillment tasks, such as retrieving information from databases or invoking external APIs.

Step 7: Using LLM for Generative Responses for Agent Assist

1. Generative AI Models:

• Deploy LLMs using Vertex AI to generate contextually relevant responses for agents.
• Integrate LLMs with Dialogflow CX to provide real-time suggestions and assistance during calls.

2. Agent Assist:

• Implement a real-time dashboard or interface for agents to view AI-generated responses and suggestions.
• Continuously refine and improve the AI models based on feedback and performance metrics.

Table of Audio Hook Integrations

ProviderIntegration MethodFeaturesGenesysStreaming APIReal-time audio capture, call metadata, transcriptionTwilioTwilio Media Streams, TwiML, and WebSocketsReal-time audio streams, call control, and metadataAvayaAvaya Breeze, Avaya Media ServerReal-time audio streaming, call routing, and media processingCiscoCisco MediaSense, Webex Contact CenterReal-time audio capture, media recording, and analytics | | Five9 | Five9 WebRTC Integration, Five9 API | Real-time communication, call data, and integration with AI services |

Conclusion

Integrating real-time call audio data with GenAI on GCP provides a powerful solution for enhancing contact center operations. By leveraging GCP services such as GKE, Cloud Pub/Sub, Speech-to-Text API, BigQuery, Dialogflow CX, Vertex AI, and Cloud Storage, contact centers can achieve real-time analytics, improved agent assistance, and superior customer service.

This architecture provides a scalable and efficient approach to capturing, processing, and analyzing call audio data in real-time. By implementing such a system, contact centers can stay ahead of the competition, delivering exceptional customer experiences and driving operational efficiency through advanced AI and real-time data processing capabilities.

Overall, this integration not only improves the performance and efficiency of contact centers but also enhances the quality of customer interactions, leading to higher customer satisfaction and loyalty.

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