Automating the extraction of tabular ex-dividend dates, corporate actuals, and credit risk files using private local model architectures.
Financial firms process unstructured data containing balance sheets, credit filings, and regulatory declarations. Manually indexing these files results in transaction bottlenecks and compliance risks. This local blueprint details the pipeline used to capture and structure tabular financial records safely on private database servers.
Complex tables and accounting grids are extracted from PDF files using local OCR engines that maintain table coordinates and hierarchy, converting them into structured markdown files.
Specialized parameter models extract key values—such as ex-dividend dates, operating margins, PO numbers, and revenue figures—without sending data to public clouds.
The extracted JSON objects are validated against ledger constraints and pushed directly to ERP systems (SAP/Oracle) via private API connectors.