Parse and Hold — Page 3

Confidence scores in document AI often misrepresent extraction accuracy in production.

Vendor accuracy claims hide document-level failures that field-by-field measurement can expose.

Silent failures in intake compound quietly across thousands of documents before anyone notices.

Agentic systems resolve extraction ambiguities themselves before routing documents to humans.

Pre-trained and fine-tuned models fail silently in production document work.

Automated extraction quality makes or breaks three-way matching accuracy.

How to prevent undetected errors from corrupting data downstream.

PDFs store tables as positioned text, not data structures, making extraction fragile.

Multiple classification methods work best together, catching errors that single approaches miss.

Merges, nesting, and missing grids make tables the document AI problem most vendors don't solve.

Sequence matters more than technique in document preprocessing for OCR accuracy.