AWS Partner Network (APN) Blog
Transforming Customer Service with Rapyder’s Generative AI-Powered Call Agent Analyzer
By Kumar Shanu, AI/ML Engineer – Rapyder Cloud Solution
By Rony Roy, AI/ML Partner Solution Architect – AWS
By Heena Khan, Partner Solutions Architect – AWS
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| Rapyder |
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In the ever-evolving landscape of the customer service industry, generative artificial intelligence (AI) is revolutionizing the way businesses operate and provide top-notch support to their customers. Rapyder Cloud Solutions is a leading global cloud consulting partner at the forefront of this transformation, offering end-to-end cloud solutions that are agile, precise, and innovative.
In this post, we delve into Rapyder’s Call Agent Analyzer solution powered by generative AI on Amazon Web Services (AWS). This tool is redefining the way businesses analyze and optimize the performance of their call agents, ensuring an exceptional customer experience.
In addition to the Call Agent Analyzer’s transformative capabilities in the customer service industry, the generative AI-powered solution offers a data-driven approach to quality assurance. By continually monitoring and analyzing customer interactions, it helps businesses to detect trends and patterns that may go unnoticed through manual evaluations.
This allows companies to make informed decisions and implement targeted training and improvement programs for their call agents. By harnessing the power of generative AI, Call Agent Analyzer helps businesses achieve higher levels of customer satisfaction, operational efficiency, and competitiveness in the dynamic customer service landscape.
Rapyder is an AWS Specialization Partner and Managed Service Provider (MSP) that provides AWS cloud consulting, migration services, DevOps enablement, and managed services to enterprises and startups.
Customer Challenges
In today’s competitive business landscape, customer service plays a pivotal role in building and maintaining customer loyalty. Call agents are often at the forefront of these interactions, acting as the bridge between a company and its customers. However, ensuring call agents consistently deliver exceptional service can be a complex undertaking, particularly in a multilingual and diverse market like India.
Here are a few difficulties contact centers face in delivering superior client experiences:
- Multilingual conversations: India’s linguistic diversity, for example, is a unique challenge for businesses. Call centers often oversee conversations in a multitude of regional languages and dialects. Analyzing and understanding these conversations is a significant hurdle.
- Script adherence: Many businesses rely on specific scripts or guidelines for call agents to follow during interactions. Ensuring call agents adhere to these scripts is critical for maintaining consistency in customer service.
- Performance evaluation: Traditional methods of call agent performance evaluation can be time-consuming and subject to human bias. Analyzing a large volume of audio recordings manually is impractical, leading to inefficiencies in identifying areas for improvement.
- Customer satisfaction: The goal of call agent performance analysis is to enhance customer satisfaction. Inconsistent or subpar interactions can lead to dissatisfied customers, impacting a company’s reputation and bottom line.
- Scalability: As businesses grow, so does their call center operations. Scaling up the process of analyzing call agent performance becomes increasingly challenging without the right tools and technologies.
The complexity of these challenges underscores the need for a sophisticated and automated solution that can efficiently process multilingual audio data, evaluate call agent performance, and provide actionable insights for improvement. This is where Rapyder’s Call Agent Analyzer steps in, leveraging generative AI on AWS to revolutionize the call agent performance analysis landscape.
Solution Overview
Rapyder’s Call Agent Analyzer leverages generative AI on AWS to comprehensively address these challenges. Here’s an overview of the solution:
- Efficient audio processing: The solution begins with the upload of audio files, representing conversations between users and call agents. These files may be in various regional Indian languages.
- Language translation: Amazon SageMaker Whisper, a language translation model hosted on Amazon Elastic Compute Cloud (Amazon EC2), converts the conversation audio into English, making it accessible for analysis.
- Call summarization and question analysis: An AWS Lambda function takes over, performing two crucial tasks—it summarizes the call and analyzes whether the agent used specific questions or scripts.
- Structured data output: The results, including call summaries and question analysis, are structured into JSON files tagged to the respective call agent.
- Data storage: The structured JSON data is stored in an Amazon Simple Storage Service (Amazon S3) bucket, creating a repository for analyzed data that offers insights into call agent performance.
Solution Architecture
The solution’s architecture utilizes AWS components and services for indexing and retrieval flow.
Figure 1 – Solution architecture diagram.
Data Flow and Processing
This combines the power of AWS services and Rapyder’s expertise in generative AI to revolutionize call agent performance analysis, offering scalability, accuracy, and data privacy in one comprehensive solution.
Figure 2 – Data flow diagram.
To initiate the process, you can manually upload an audio file to Amazon S3, or you can automate by integrating it with your internal system. This audio will be converted into text so it can be processed further, as described below:
- Audio upload trigger: The process commences when an audio file is uploaded to an Amazon S3 bucket. This trigger activates an AWS Lambda function to initiate the analysis process.
- Transcription with Whisper model: Amazon SageMaker Whisper, a potent language model, comes into play. It’s hosted on a SageMaker inference endpoint and is responsible for transcribing the audio file into text. This transformation makes the conversation content accessible for analysis.
- AWS Lambda for LLM analysis: Subsequently, the transcribed text is forwarded to another Lambda function equipped with large language models (LLMs) from Amazon Bedrock, including the powerful Claude 2 model. This function serves a dual purpose:
- Summarization: Claude 2 excels in generating concise and informative summaries of the call content. Its capabilities in natural language understanding (NLU) enable it to distil the essence of lengthy conversations efficiently.
- Q/A similarity: Leveraging Claude 2’s advanced question-answering (Q/A) capabilities, it assesses whether specific scripted questions were posed during the call. The model’s precision in identifying scripted interactions enhances the accuracy of the analysis.
- Fine-tuning and prompting techniques: To ensure the accuracy and relevance of Bedrock-generated responses, Rapyder’s generative AI solution utilizes Claude 2, as prompting techniques guide the model to generate responses based on a predefined set of script questions provided to the call agent. This approach minimizes the risk of inaccurate analysis and provides a granular level of insights.
Benefits of Amazon Bedrock
Amazon Bedrock offers a multitude of advantages that significantly enhance the Call Agent Analyzer’s capabilities:
- Claude v2 model: Leveraging the powerful Claude v2 model, Amazon Bedrock significantly enhances the accuracy and depth of call analysis within the Call Agent Analyzer. Claude v2’s NLU capabilities streamline the process of summarization and scripted question identification.
- Privacy and security: Amazon Bedrock allows the private customization of models with fine-tuning techniques while maintaining robust data privacy and security standards. This ensures sensitive customer interactions are kept confidential and protected.
- Streamlined development: By providing a unified API for various foundation models, Bedrock simplifies the integration of AI capabilities into the Call Agent Analyzer. This streamlining accelerates the development cycle and ensures efficient deployment.
- Serverless architecture: The serverless architecture of Bedrock eliminates the requirement for managing infrastructure, offering a more agile and cost-effective deployment of generative AI capabilities. This seamless integration with familiar AWS services optimizes operational efficiency.
Conclusion
In the fiercely competitive realm of customer service, businesses must continually seek innovative solutions to distinguish themselves. Through a powerful collaboration with AWS, Rapyder’s Call Agent Analyzer empowers businesses to optimize agent performance and elevate the customer experience.
Through the constructive interaction of Amazon Bedrock and the Claude v2 generative AI model, Call Agent Analyzer benefits from improved accuracy, streamlined development, and enhanced data privacy, empowering businesses to optimize call agent performance with confidence and efficiency.
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Rapyder Cloud Solution – AWS Partner Spotlight
Rapyder is an AWS Specialization Partner and MSP that provides AWS cloud consulting, migration services, DevOps enablement, and managed services to enterprises and startups.