Azets faced a significant challenge in managing customer inquiries due to their manual support processes. The support team was tasked with responding to a high volume of questions, which often involved repetitive tasks such as copying and pasting answers from previous interactions. This approach proved to be not only time-consuming but also prone to inconsistencies and errors.

The manual handling of inquiries was labour-intensive and lacked scalability. As the volume of customer interactions grew, the inefficiencies of the current system became more apparent. The support team struggled to keep up with the increasing number of inquiries, leading to longer response times and a potential decline in customer satisfaction.

Moreover, the reliance on repetitive manual responses hindered the team’s ability to focus on more complex issues that required human expertise. This issue underscored the need for a more efficient and scalable solution that could handle routine inquiries while freeing up valuable human resources for higher-level support tasks.

Addressing this challenge required a robust technological solution capable of automating the response process while maintaining high accuracy and relevance. By implementing a generative AI-based system, Azets aimed to transform their support process, streamline operations, and enhance overall customer service efficiency.

To address Azets’ challenge of manual and inefficient customer inquiry handling, we implemented an advanced Generative AI (GenAI) solution. This system leverages Retrieval Augmented Generation (RAG) to automate and optimise response generation. By indexing the knowledge base documents in Amazon Kendra, the system can swiftly retrieve relevant information and generate accurate, context-aware responses to customer inquiries.

The solution integrates with Amazon Bedrock, which hosts the Large Language Model (LLM) responsible for interpreting prompts and crafting responses. This approach not only reduces the time spent on repetitive tasks but also improves the consistency and reliability of the information provided to customers. The automated process ensures that responses are generated quickly, allowing the support team to focus on more complex and nuanced issues.

Additionally, the use of AWS Lambda facilitates seamless document uploads and indexing, further streamlining the integration of new information into the system. Amazon S3 is utilised for storing both the documents and static assets, ensuring that all necessary materials are readily accessible and efficiently managed.

Overall, this generative AI based solution transforms Azets’ customer support by providing scalable, accurate, and automated responses. The implementation significantly enhances operational efficiency, reduces manual effort, and improves overall customer satisfaction.