Parsing the 10-K: why financial filings defeat standard PDF pipelines

Financial teams often encounter issues with the accuracy of numbers extracted from 10-K reports, despite using capable language models and retrieval architectures, due to upstream parsing errors. The complexity of SEC annual reports, including their length, cross-referenced structure, and unique financial conventions, poses significant challenges for generic PDF parsing pipelines. Common issues include scale errors, sign handling problems, and misinterpretation of multi-page tables and multi-column layouts, all of which can lead to substantial financial inaccuracies, as exemplified by past costly errors in major companies' reports. Reducto addresses these challenges by employing a vision model to analyze the spatial structure of documents before text extraction, using vision-language models to read content, and implementing an agentic verification layer to ensure accuracy. This approach allows for accurate reassembly of tables, correct handling of scale headers and parenthetical negatives, and provides source citations for extracted data, underscoring the importance of robust parsing in financial data processing.