LlamaExtract is a tool designed to transform complex, lengthy documents, like SEC filings and 10-K reports, into structured, actionable data. This structured data extraction aids financial analysis, risk assessment, and investment decision-making by organizing key metrics and financial statements into a format suitable for automated processes. Extracting information from such documents is challenging due to their extensive length and varied structures, but LlamaExtract addresses these issues by using a well-designed schema, which can be created via a Python SDK and Pydantic or a Web UI. The schema captures essential elements, such as filing information and financial highlights, and strategically balances required and optional fields to avoid missing information or hallucinations. It also emphasizes clear field descriptions and a hierarchical organization to maintain context, and includes page tracking for verification purposes. LlamaExtract is available in public beta, offering a comprehensive solution for structured data extraction workflows, allowing users to iterate on schema design and run scalable batch jobs using its Python SDK.