In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.
Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon Web Services available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Amazon Bedrock offers a serverless experience so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure.
With Amazon Bedrock and other AWS services, you can build a generative AI-based email support solution to streamline email management, enhancing overall customer satisfaction and operational efficiency.
Challenges of knowledge management
Email serves as a crucial communication tool for businesses, but traditional processing methods such as manual processing often fall short when handling the volume of incoming messages. This can lead to inefficiencies, delays, and errors, diminishing customer satisfaction.
Key challenges include the need for ongoing training for support staff, difficulties in managing and retrieving scattered information, and maintaining consistency across different agents’ responses.
Organizations possess extensive repositories of digital documents and data that may remain underutilized due to their unstructured and dispersed nature. Additionally, although specific APIs and applications exist to handle customer service tasks, they often function in silos and lack integration.
The benefits of AI-powered solutions
To address these challenges, businesses are adopting generative AI to automate and refine email response processes. AI integration accelerates response times and increases the accuracy and relevance of communications, enhancing customer satisfaction. By using AI-driven solutions, organizations can overcome the limitations of manual email processing, streamlining operations and improving the overall customer experience.
A robust AI-driven email support agent must have the following capabilities:
- Comprehensively access and apply knowledge – Extract and use information from various file formats and data stores across the organization to inform customer interactions. Seamlessly integrate with APIs – Interact with existing business APIs to perform real-time actions such as transaction processing or customer data updates directly through email. Incorporate continuous awareness – Continually integrate new data, such as updated documents or revised policies, allowing the AI to recognize and use the latest information without retraining. Uphold security and compliance standards – Adhere to required data security protocols and compliance mandates specific to the industry to protect sensitive customer information and maintain trust. Implement governance mechanisms to make sure AI-generated responses align with brand standards and regulatory requirements, preventing non-relevant communications.
Solution overview
This section outlines the architecture designed for an email support system using generative AI. The following diagram illustrates the integration of various components crucial for improving the handling of customer emails.
High Level System Design
The solution consists of the following components:
- Email service – This component manages incoming and outgoing customer emails, serving as the primary interface for email communications. AI-powered email processing engine – Central to the solution, this engine uses AI to analyze and process emails. It interacts with databases and APIs, extracting necessary information and determining appropriate responses to provide timely and accurate customer service. Information repository – This repository holds essential documents and data that support customer service processes. The AI engine accesses this resource to pull relevant information needed to effectively address customer inquiries. Business applications – This component performs specific actions identified from email requests, such as processing transactions or updating customer records, enabling prompt and precise fulfillment of customer needs. Non-functional requirements (NFRs) – This includes the following:
- Security – Protects data and secures processing across interactions to maintain customer trust. Monitoring – Monitors system performance and user activity to maintain operational reliability and efficiency. Performance – Provides high efficiency and speed in email responses to sustain customer satisfaction. Brand protection – Maintains the quality and consistency of customer interactions, protecting the company’s reputation.
The following diagram provides a detailed view of the architecture to enhance email support using generative AI. This system integrates various AWS services and custom components to automate the processing and handling of customer emails efficiently and effectively.
The workflow includes the following steps:
- Amazon WorkMail manages incoming and outgoing customer emails. When a customer sends an email, WorkMail receives it and invokes the next component in the workflow. An email handler AWS Lambda function is invoked by WorkMail upon the receipt of an email, and acts as the intermediary that receives requests and passes it to the appropriate agent. These AI agents process the email content, apply decision-making logic, and draft email responses based on the customer’s inquiry and relevant data accessed.
- Guardrails make sure the interactions conform to predefined standards and policies to maintain consistency and accuracy. The system indexes documents and files stored in Amazon Simple Storage Service (Amazon S3) using Amazon OpenSearch Service for quick retrieval. These indexed documents provide a comprehensive knowledge base that the AI agents consult to inform their responses. Business APIs are invoked by AI agents when specific transactions or updates need to be run in response to a customer’s request. The APIs make sure actions taken are appropriate and accurate according to the processed instructions.
Deploy the solution
To evaluate this solution, we have provided sample code that allows users to make a restaurant reservation through email and ask other questions about the restaurant, such as menu offerings. Refer to the GitHub repository for deployment instructions.
The high-level deployment steps are as follows:
- Install the required prerequisites, including the AWS Command Line Interface (AWS CLI), Node.js, and AWS Cloud Development Kit (AWS CDK), then clone the repository and install the necessary NPM packages. Deploy the AWS CDK project to provision the required resources in your AWS account. Follow the post-deployment steps in the GitHub repository’s README file to configure an email support account to receive emails in WorkMail to invoke the Lambda function upon email receipt.
When the deployment is successful (which may take 7–10 minutes to complete), you can start testing the solution.
Test the solution
This solution uses Amazon Bedrock to automate restaurant table reservations and menu inquiries as an example; however, a similar approach can be adapted for various industries and workflows. Traditionally, customers email restaurants for these services, requiring staff to respond manually. By automating these processes, the solution streamlines operations, reduces manual effort, and enhances user experience by delivering real-time responses.
You can send an email to the support email address to test the generative AI system’s ability to process requests, make reservations, and provide menu information while adhering to the guardrails.
- On the WorkMail console, navigate to the organization gaesas-stk-org-<random id>. Choose Users in the navigation pane, and navigate to the support user. Locate the email address for this user.
Send an email requesting information from the automated support account using your preferred email application. The following image shows a conversation between the customer and the automated support agent.
Clean up
To clean up resources, run the following command from the project’s folder:
Conclusion
In this post, we examined how you can integrate AWS services to build a generative AI-based email support solution. By using WorkMail for handling email traffic, Lambda for the processing logic, and Amazon SES for dispatching responses, the system efficiently manages and responds to customer emails. Additionally, Amazon Bedrock agents, supplemented by guardrails and supported by an OpenSearch Service powered information repository, make sure responses are accurate and compliant with regulatory standards. This cohesive use of AWS services not only streamlines email management but also makes sure each customer interaction is handled with precision, enhancing overall customer satisfaction and operational efficiency.
You can adapt and extend the business logic and processes demonstrated in this solution to suit specific organizational needs. Developers can modify the Lambda functions, update the knowledge bases, and adjust the agent behavior to align with unique business requirements. This flexibility empowers you to tailor the solution, providing a seamless integration with your existing systems and workflows.
About the Authors
Manu Mishra is a Senior Solutions Architect at AWS with over 16 years of experience in the software industry, specializing in artificial intelligence, data and analytics, and security. His expertise spans strategic oversight and hands-on technical leadership, where he reviews and guides the work of both internal and external customers. Manu collaborates with AWS customers to shape technical strategies that drive impactful business outcomes, providing alignment between technology and organizational goals.
AK Soni is a Senior Technical Account Manager with AWS Enterprise Support, where he empowers enterprise customers to achieve their business goals by offering proactive guidance on implementing innovative cloud and AI/ML-based solutions aligned with industry best practices. With over 19 years of experience in enterprise application architecture and development, he uses his expertise in generative AI technologies to enhance business operations and overcome existing technological limitations. As a part of the AI/ML community at AWS, AK guides customers in designing generative AI solutions and trains AI/ML enthusiastic AWS employees to gain membership in the AWS generative AI community, providing valuable insights and recommendations to harness the power of generative AI.
