The article delves into the technical challenges of extracting tabular data from documents, explaining that it's far more complex than standard text OCR and requires three core coordinated phases: detection, structure recognition, and data extraction.
LlamaIndex's blog post highlights that 90% of enterprise data is unstructured, and modern AI stacks (NLP, NER, LLM) can convert these documents into queryable structured information, unlocking significant business value.
Single-pass extraction fails silently on complex documents, while deep extraction uses an iterative, agent-driven verification loop to achieve near-perfect accuracy, making it essential for production workflows.