How AI PDF Analysis Works Behind the Scenes

Stage 1: Getting to the text

A PDF is a description of where marks go on a page, not a document with a structure. Many PDFs carry an embedded text layer, which is the easy case. Scans and photos carry only pixels, so the characters have to be recognised from the image. This first stage sets the ceiling for everything that follows: no later stage can recover text that was never read correctly.

Stage 2: Recovering layout

Text without layout is a jumble. Reading order has to be reconstructed for multi-column pages, headers and footers separated from body content, and table cells identified by their positions rather than by any markup, because the PDF contains none. Tables that continue across a page break have to be stitched back together with their headers, which is the hardest single problem in the pipeline.

Stage 3: Working out what the document is

An invoice, a contract and a research paper need different things extracted. Type detection happens before extraction so the model knows which fields to look for — supplier and VAT number for an invoice, parties and notice period for a contract, methodology and sample size for a paper.

Stage 4: Structured extraction against a schema

This is where the quality difference between tools lives. Rather than asking a model to describe the document freely, the extraction is constrained to a strict schema: named fields, typed values, a defined shape. A constrained response cannot wander, and a field the document does not contain comes back empty instead of invented. Free-form generation is where hallucinated values come from; a schema is the main defence against them.

• Detected document type
• Summary and key points
• Labelled key values with their field names
• Tables as rows and columns, not prose
• Empty where the document is silent

Stage 5: Validation, storage and logging

The structured result is validated against the schema before it is stored, so a malformed response fails loudly rather than landing in your data. The original file, the extracted data and the generated report are each stored privately, and every run is logged with its duration and outcome — which is what lets you trace any figure back to the file and the run that produced it.

What this predicts

Two things follow directly. First, image quality matters more than model quality for scans, because stage one caps everything downstream. Second, documents with unusual layouts — dense multi-column forms, heavily nested tables — are where to check the output most carefully, because stage two is doing the most guessing.