Case Study
Solving the Challenge of Large-Scale Document Workflows
Client Profile
Overview
Meet Our Hero
This global software provider helps enterprises manage and access their content at scale. As customer needs grew, the organization struggled with orchestrating a multi-stage process for handling massive volumes of documents.
The lack of automation slowed operations, while limited resilience created risks around performance and customer experience. The company needed a cloud-native solution that could reliably process diverse file types, make documents searchable, and deliver results at scale.
The Challenge
Challenge 01
Needed to handle diverse file types in document processing
Challenge 02
Multi-stage workflows were complex and hard to manage
Challenge 03
Scalability challenges limited the ability to handle large volumes
Challenge 04
State management was difficult in a decoupled architecture
Challenge 05
End-user experience suffered due to delays and inefficiencies
The Goal
- Reliably orchestrate an OCR pipeline on AWS
- Handle massive volumes of documents with automation
- Make documents searchable to increase usability
- Ensure resilience with fault-tolerant workflows
- Enhance the end-user experience
The Solution
Step 01 | Event-Driven Architecture
- Built the pipeline around AWS services for orchestration and resilience
- Used SQS queues, S3 buckets, and DynamoDB counters to manage state
Step 02 | Multi-Stage Processing
- Applied Apache Tika for text extraction
- Leveraged Tesseract for optical character recognition (OCR)
- Added a text overlay service to enrich documents
- Assembled files through a dedicated service to complete the pipeline
Step 03 | Automation
and Resilience
- Deployed services on Amazon ECS for container orchestration
- Implemented error handling and DLQs to ensure fault tolerance
- Used IAM and AppConfig for secure, configurable operations
Outcomes & Impact
Searchable documents, increasing the platform’s product value
Massive scalability, handling large volumes of files efficiently
High resilience, with DLQs and automated recovery in place
Improved efficiency, streamlining multi-stage document processing
Future-ready architecture, providing a foundation for advanced document intelligence features
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