Building a Case Management Assistant Using Amazon Bedrock and Amazon Kendra

The Challenge

Case management in industries like insurance, healthcare, legal services, and customer support often involves processing vast amounts of unstructured data. Case workers or agents are tasked with retrieving relevant documents, understanding historical context, responding to inquiries, and keeping track of all interactions. This manual process can be slow, error-prone, and overwhelming as the number of cases increases.

The complexity arises from multiple data sources—documents, correspondence, audio transcripts, and case histories—that must be quickly processed and understood. These challenges can lead to delays in case resolution, poor customer experience, and misinformed decisions. Additionally, retrieving the right documents and summarizing key details from a massive repository requires a level of automation that goes beyond simple keyword searches.

The Solution

A case management assistant, powered by AWS services like Amazon Kendra and Amazon Bedrock, solves these problems by automating document retrieval, summarization, and information processing. This solution allows case workers to spend less time searching for information and more time focusing on decision-making and client interactions. By integrating tools like Amazon Textract, Amazon Transcribe, and Amazon DynamoDB, the assistant processes various inputs to provide concise summaries, suggest probable questions, and deliver relevant documents in real-time. This enhances both the accuracy and speed of case handling, allowing organizations to scale their operations without compromising on service quality.

Key Components of the Case Management Assistant

Lambda Functions for the Assistant API: AWS API Gateway and Lambda functions coordinate the overall workflow, handling requests and communicating with other AWS services. These serverless functions offer scalability and eliminate the need to manage servers, making it easier to adjust resources as the case volume grows.
DynamoDB for Case History Storage: Amazon DynamoDB stores case history data, providing fast, scalable access to historical records. This allows the assistant to retrieve the necessary background information for each case query without delays.
Amazon Textract for Document Reading: Amazon Textract extracts key information from scanned documents or uploaded files like PDFs and images, providing up-to-date case details and correspondence that can be used in further processing.
Amazon Transcribe for Audio to Text Conversion: Amazon Transcribe converts client calls or IVR recordings into text, making this data accessible to the assistant for analyzing queries and adding them to the case history.
RAG (Retrieval-Augmented Generation) Engine: The RAG engine combines data from case history, correspondence, and audio inquiries to generate a summary, timeline, and possible follow-up questions. This engine uses retrieval-based techniques to pull the most relevant information before summarizing it.
Amazon Bedrock for Summarization and Insights: Amazon Bedrock agents and AWS native models like Titan, integrated into the RAG engine, enhances the assistant’s ability to engineer prompt, summarize cases, suggest timelines, preserves history and formulate probable next steps. Its advanced natural language processing capabilities make it a critical component for generating accurate and useful case summaries.
Amazon Kendra for Document Retrieval: Amazon Kendra acts as the search engine, retrieving relevant documents based on the case details and context provided by the RAG engine. It helps case managers quickly find policies, contracts, or previous cases that may be pertinent to the query.

Architecture Overview

The architecture revolves around the seamless communication between the various AWS services mentioned above, with AWS Lambda acting as the orchestrator. Below is an outline of how the services interact:

The user (case manager or system) triggers a query through the Assistant API, which is handled by a AWS Lambda function.
The API retrieves the case history from Amazon DynamoDB.
Any recent correspondence is read using Amazon Textract.
Audio files (if any) are converted to text using Amazon Transcribe.
These inputs are sent to the RAG engine, which uses Amazon Bedrock Titan for summarization, timeline generation, and question formulation.
Relevant documents are fetched via Amazon Kendra and delivered to the case manager.

Benefits of This Solution

Efficiency: Automates time-consuming tasks like document reading, audio transcription, and information summarization, allowing case managers to focus on decision-making.
Accuracy: Provides concise and relevant information, reducing the risk of human error.
Scalability: Leverages serverless components and scalable services like AWS Lambda and Amazon DynamoDB, ensuring the solution can handle increasing workloads.
Cost-Effective: By using pay-as-you-go AWS services, the solution remains cost-efficient for businesses of all sizes.

Enhancing and Maturing the Solution

As organizations grow, their needs may extend beyond the basic case management assistant. Here are some alternative/advanced options to consider as you mature the solution:

RAG Engine with Amazon Bedrock Knowledge Bases: As case complexity increases, using Amazon Bedrock Knowledge Bases to manage domain-specific queries can enhance the assistant’s ability to provide context-aware insights. Knowledge bases help the assistant offer more refined, expert-level responses by leveraging industry-specific data.
Custom RAG Engine with Amazon OpenSearch: For organizations requiring greater control over how documents are indexed and searched, integrating Amazon OpenSearch with the RAG engine provides the flexibility to customize vector embeddings and control the document retrieval process. OpenSearch allows for tailored search algorithms, making it ideal for scenarios where Amazon Kendra’s managed solution may not provide enough customization.
Enhanced NLP Models for Specialized Domains: Custom-trained natural language models can be deployed via Amazon SageMaker and Bedrock to handle highly specialized queries in fields like healthcare, law, or finance. As the case management needs become more complex, these domain-specific models can provide deeper insights and more tailored summaries.
Leverage Amazon Q Business for Enhanced Efficiency: Amazon Q Business can be integrated to automate routine tasks such as report generation, drafting and data summarization, freeing up valuable time for the case managers. Its lightweight app creation capabilities can be utilized to develop custom apps tailored to specific case management workflows, further steamling operations and improving overall efficency.

Conclusion

The case management assistant built using Amazon Kendra, Amazon Bedrock Titan, and other services is a powerful tool for improving the efficiency and accuracy of case resolution. By automating document retrieval, case summarization, and data extraction, this assistant reduces manual effort, enabling case workers to focus on higher-value tasks. Additionally, the solution’s scalable architecture allows businesses to handle increased case volumes without sacrificing quality. As organizations grow, the assistant can be further enhanced with solutions like Amazon OpenSearch and Amazon Bedrock Knowledge Bases, ensuring that it remains flexible and customizable to meet evolving needs.

Contributors:

Jayasankar Chakravarthy, Specialist Leader, Deloitte LLP
Loveleen Narang, Specialist Master, Deloitte LLP
Aparna Mudireddy, Specialist Master, Deloitte LLP
Ajith Joseph, Specialist Master, Deloitte LLP

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Disclaimer: Please note that AWS technology is constantly evolving, and new features may be available since the release of this blog post. It's recommended to review the latest documentation to determine the most suitable solutions for your specific needs. This blog is a reference guide only. Additionally, ensure that the proposed solutions comply with your organization's security and compliance requirements, as some services may be relatively new and may not be fully compliant with all industry standards.

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