Most organizational knowledge is still locked inside complex documents, making it difficult to extract and use the information effectively. Traditional tools often fail when working with real-world PDFs. Tables lose their structure, figures are separated from captions, and multi-column layouts are flattened into unreadable text. These issues create a significant barrier to using AI on real document data.

The open-source project Docling presents a new approach to document ingestion that mirrors human comprehension using open-source deep learning models in a neat Python package. The system extracts structured information through consistent APIs, preserving original document hierarchy while ensuring machine readability.

With support for over ten of the most common file formats and a consistent API, Docling enables production-ready document processing pipelines and provides seamless integration with established frameworks including LangChain and LlamaIndex, as well as multilingual support. Its MIT license and local execution model make it suitable for sensitive enterprise applications.