Document AI: turning paperwork into data you can trust

What document AI does

Document AI turns a scan, PDF or photo into structured data: the invoice number, the parcel ID, the date a deed was recorded. It combines three steps: reading the text (OCR), understanding the layout, and extracting the fields you need into a clean format your systems can use.

Why layouts are the hard part

The same information appears in different places on different forms. A county record from one region looks nothing like one from the next. Older scans add noise, stamps and handwriting.

Large language models help a great deal because they can find a field by meaning, not just by position. On their own, though, they can misread or invent a value. Reliable systems pair them with rules and validation:

• Rules for formats you know, such as dates, IDs and amounts.
• Cross-checks, such as totals that must add up or IDs that must exist in your records.
• Confidence scores, with low-confidence fields sent to a person.
• A test set of real documents with known correct values for every layout.

Measure it like software

Treat extraction as code with tests. For each document type, keep a set of examples and the values a careful person would extract. Every change to rules, prompts or models is run against that set before release, so accuracy goes up over time instead of drifting.

This is the work our document intelligence interns learn first: write the extractor, write the tests, prove the result.

Where it pays off

• Property and title research across many county record formats.
• Accounts payable: invoices, purchase orders and delivery notes.
• Onboarding: identity documents, application forms and certificates.
• Healthcare and insurance forms that must be keyed into core systems.