An AI powered extraction platform cut manual effort by 50%
An agent based document extraction platform pulls structured parts data out of thousands of complex PDFs, reaching ~94% accuracy on structured data, with an MVP completed for the supplier.
The Opportunity
For the supplier, critical parts data was locked across roughly 2,700 complex, unstructured PDF documents, mixing text, tables, images, and multi language content, with some files running to 1,500+ pages and 800 MB.
Manual extraction was slow, costly, and error prone, and couldn't scale to support the business's growing data processing needs.
The Transformation
An AI powered document extraction platform was built on AWS using an agent based architecture on Bedrock with Claude 3.5 Sonnet. A classification agent identifies document type, and specialized extraction agents handle text, tables, and images separately.
Data moves through a medallion (bronze, silver, gold) pipeline with human in the loop validation, end to end from document ingestion to database integration.
Multi Agent Extraction Pipeline
A classification agent identifies document type, then specialized extraction agents handle text, tables, and images separately, instead of one generic model trying to parse everything the same way.
Built on Bedrock
The agent based architecture runs on AWS Bedrock using Claude 3.5 Sonnet, giving the platform the reasoning needed to handle mixed formats and multi language content reliably.
Medallion Architecture
Data moves through a bronze, silver, gold pipeline, with human in the loop validation built in to catch what the models get wrong before it reaches the database.
End to End Automated Workflow
The workflow runs automatically from document ingestion to database integration, replacing a process that used to require manual handling at every step.
Reduction in manual data extraction effort
Accuracy achieved on structured, table based data
Documents processed in Phase 1, on an architecture built to scale further
Reduction in operational costs and turnaround time