Pulse AI + Amazon Bedrock cuts financial doc processing from days to hours

Financial institutions process thousands of complex documents daily—balance sheets, income statements, SEC filings, and audit materials. Traditional OCR tools fail on intricate table structures with merged cells, multi-column layouts, and context-dependent data. A single OCR error can cascade through interconnected calculations, causing systematic analytical errors and costly manual corrections. The problem is not just accuracy but semantic understanding: traditional approaches treat documents as images, missing structural relationships and contextual nuances.

Pulse AI and Amazon Bedrock together solve this with an intelligent pipeline. Pulse extracts structured, semantically-aware data from complex financial documents, handling hierarchical tables and multi-column layouts. Amazon Bedrock then fine-tunes Amazon Nova Micro models on that high-quality data, creating domain-specific intelligence tailored to an organization's financial conventions. Custom models process new documents with organization-specific understanding, reducing manual review from days to hours. In one deployment, a batch of about 1,000 complex documents that previously required multi-day turnaround was processed in under three hours, producing structured, auditable outputs ready for downstream analytics. The system integrates within a customer's VPC or via SaaS, and the workflow includes document ingestion, extraction, conversion to fine-tuning format, storage in S3, and supervised fine-tuning using Nova Micro—a cost-efficient model for text-based extraction. Enterprises like Samsung, Cloudera, and Fortune 500 financial institutions already use this approach to achieve enterprise-grade accuracy at scale.