CONFIDENTIAL CLIENT
How a technology multinational modernized document data extraction with AI
Modernizing data extraction from IDs, driver's licenses, and medical reports with multimodal GenAI
Smart data lookup across IDs, driver’s licenses, and medical reportsProcessing of multiple document versionswith no retraining neededA serverless architecture**with lower latency and operational flexibility
A global technology and digital transformation services leader worked with BlueMetrics to roll out a new generative AI architecture that modernized its document data extraction process. The company replaced a traditional OCR-based pipeline with a centralized solution using AI models via Amazon Bedrock, able to semantically interpret documents such as IDs, driver’s licenses, and medical reports regardless of layout variation or image quality. The new approach significantly simplified the processing architecture, cut system latency, and improved extraction accuracy, even on low-quality documents.
Overview
Global organizations operating at scale often rely on automated document data extraction to support critical processes for verifying, validating, and integrating information into corporate systems.
That is the context in which a large international technology company identified significant limitations in its current document processing platform. The existing solution was built on traditional OCR tools and depended heavily on rigid document layouts, which required constant technical adjustments and maintenance whenever new document versions appeared.
On top of that, the existing pipeline was made up of multiple sequential steps: classification, extraction, and validation, each run by a different service, which increased architectural complexity and process latency.
Facing a growing need for scalability, flexibility, and lower operational costs, the company decided to move its platform to a generative AI architecture that could interpret documents in context and significantly simplify the processing flow.
Market context
- Growing use of generative AI for document automation
- High layout variability in official documents and medical reports
- Growing need for contextual validation of extracted data, plus pressure for simpler, more scalable, and more resilient architectures
- Demand for solutions that combine automation, semantic interpretation, and cloud scalability
The problem: reliance on traditional OCR and a complex architecture
Despite running on a modern AWS infrastructure, the company’s document extraction pipeline still had important limitations tied to traditional OCR and custom layout-based models.
Whenever a new document version appeared, such as a different format of driver’s license or ID, the team had to collect new data, label examples, and retrain models specific to that layout. This process drove up maintenance costs and reduced the ability to add new document types quickly.
Another important challenge was the quality of the documents being processed. Images with low resolution, shadows, smudges, or handwritten fields often led to extraction errors, requiring manual review and reducing operational efficiency.
On top of that, the processing flow depended on multiple services in sequence: one for classification and another for extraction, which increased latency and created potential points of failure in the pipeline.
As document volume grew and the need for greater flexibility increased, it became clear that the existing model was not sustainable over the long term.
Key challenges
Operational
- Reliance on rigid layouts for data extraction
- A high error rate on low-quality documents
- The need for manual intervention to make corrections
Business
- Difficulty adding new document types quickly
- High costs to maintain and retrain models
- High latency in document processing
Technology
- An architecture built on multiple sequential services
- Limited ability to interpret content semantically
- Reliance on traditional OCR for text extraction
The solution: a smart extraction platform with generative AI
BlueMetrics built a new generative AI architecture that completely replaced the traditional OCR pipeline with a centralized, smarter solution.
The new approach uses generative AI models available in Amazon Bedrock Data Automation to interpret documents in context, automatically identifying and extracting relevant information regardless of where the text sits or how the layout is structured.
The solution was designed to process different document types, such as IDs, driver’s licenses, and medical reports, using specialized prompts for each document category. An AWS Lambda function orchestrates the process, dynamically assembling the right prompt and invoking the Bedrock API to run the analysis.
The flow was simplified into a single call to the AI model, removing the need for separate classification and extraction steps. Documents submitted by users are stored in Amazon S3, processed by the AI model, and have their structured data persisted in JSON format in Amazon DynamoDB.
The entire architecture was built with AWS serverless services, ensuring automatic scaling, high availability, and optimized operational costs.
Key components
Smart data extraction with generative AI via Amazon Bedrock, a Lambda function to orchestrate the processing flow, document storage in Amazon S3, persistence of structured data in Amazon DynamoDB, API Gateway to expose services, and user authentication with Amazon Cognito
Technology differentiators
Extraction based on semantic interpretation of documents, not just text position, processing of multiple layouts with no retraining needed, a simplified and highly scalable serverless architecture, and native integration with the existing AWS ecosystem
Immediate benefits
- Greater accuracy extracting data across varied documents
- A significant reduction in architectural complexity
- Faster processing with lower latency
- Greater flexibility to add new document types
Results:
With the new generative AI solution in place, the company made important gains in accuracy, operational efficiency, and architectural simplicity.
The system reached high accuracy levels extracting structured data, hitting more than 75% accuracy on high-quality documents andabove 50% on low-quality documents**, even in cases with smudges, shadows, or handwritten fields.
On top of that, the new architecture made it possible to support multiple versions of official documents with no model retraining, significantly increasing the system’s flexibility.
Simplifying the pipeline cut total process latency by more than 30%, while also eliminating several points of failure present in the previous architecture.
Operational efficiency:
More than a 30% reduction in total processing latency, a simplified architecture with fewer points of failure, and less need for manual intervention
Data accuracy and intelligence:
Accuracy above 75% on standard documents, efficient extraction even on low-quality documents, and contextual validation of extracted information
Technology progress:
- Complete replacement of traditional OCR with generative AI
- Unified processing via Amazon Bedrock Data Automation
- A highly scalable serverless architecture
Technologies used
AWS services
Amazon Cognito
Amazon API Gateway
AWS Lambda
Amazon DynamoDB
Amazon S3 Amazon
Bedrock Data Automation
Amazon CloudFront
Security
Data encryption in transit and at rest
Policy-based access control
AWS full processing audit
Conclusion:
This project shows how adopting generative AI can profoundly transform traditional document extraction processes.
By replacing an OCR-based pipeline with a smart solution that interprets documents in context, the company was able to simplify its technology architecture, reduce operational costs, and significantly increase system flexibility.
Beyond improving extraction accuracy, the new platform created a technology foundation ready for the future, making it possible to add new document types quickly and evolve toward more advanced automation and analysis applications.
With BlueMetrics support, the company turned a complex operational process into a scalable, smart solution, putting artificial intelligence at the center of its document processing strategy.