Cloud-based GenAI solution for efficient document indexing
Overview
In this case study, we explore how a client sought to improve their document management capabilities by implementing a new indexing system powered by generative AI (GenAI) and built on a modern, cloud-based architecture. The challenge involved enhancing document classification, scalability and security while ensuring efficient key information extraction and metadata generation. By leveraging Amazon Bedrock, Textract and other AWS services, the client successfully developed a state-of-the-art document indexer that resulted in significant productivity gains and cost reductions, thereby transforming their document handling processes.
The Challenge
- The client needed a new document indexing system to enhance their current document management capabilities, specifically by incorporating LLMs for improved document classification
- They required a modern, cloud-based architecture utilizing microservices that would provide scalability, flexibility and robust security
- The application needed to be capable of extracting configurable key information from documents and generating relevant metadata
The Objective
To build a highly scalable cloud-based document indexer system that leverages the capabilities of GenAI through Amazon Bedrock. This system aims to efficiently process and organize vast amounts of document data, enabling users to quickly retrieve and manage information across various formats and sources.
The Solution
- Developed a GenAI application utilizing Amazon Bedrock. This application classifies and validates documents by analyzing their content and structure, using specific prompts to guide GenAI in focusing on relevant sections of the documents
- Employed Amazon Textract to automatically extract metadata from documents by detecting and analyzing text and data within the files. Data processing is handled via AWS Lambda functions to transform the extracted data into structured formats
- Utilized Amazon Bedrock’s LLMs to extract metadata with high accuracy, leveraging prompt engineering to enhance the extraction process
- The extracted metadata is stored and updated in DynamoDB for efficient storage and retrieval
- AWS S3 is used for storing the uploaded documents and the extracted metadata
- Developed a React-based web application that allows users to upload documents and monitor the extraction and processing activities through an interactive monitoring dashboard
The Impact
- Through the successful implementation of this GenAI application, the client benefited from over 35% productivity improvement and substantial cost reduction
- The client achieved scalability, flexibility and secured an enhanced document indexer system on AWS
