Build financial document processing with Pulse AI and Amazon Bedrock

This article demonstrates building a financial document processing pipeline combining Pulse AI's document understanding with Amazon Bedrock's fine-tuning capabilities for accurate extraction of complex financial data.

• Traditional OCR fails on financial documents with complex tables, merged cells, and hierarchical data structures
• Pulse AI extracts structured, semantically-aware data from complex financial documents with high accuracy
• Amazon Bedrock fine-tunes Nova Micro models on extracted data to create domain-specific financial intelligence
• Custom models process new documents with organization-specific understanding, reducing manual review time
• Reference architecture: Pulse extraction → Nova fine-tuning dataset → Bedrock training → custom model deployment
• Fine-tuned model achieved 100% check extraction versus 50% for base Nova Micro model
• Production example: 1,000 complex financial documents processed in under three hours versus multi-day turnaround
• Step-by-step implementation includes EC2 setup, Pulse API integration, dataset conversion, and model deployment
• Cost considerations: EC2 hourly charges, S3 storage, Bedrock fine-tuning, provisioned throughput, and Secrets Manager fees

This solution enables enterprises to build production-ready financial AI applications that understand domain-specific conventions while maintaining performance and cost efficiency.