Government agencies accumulate documents the way rivers accumulate sediment — steadily, across decades, in layers. Policy manuals, procedural guidelines, public notices, administrative orders, compliance reports. Most of it exists as PDFs, Word files, or scanned images. Almost none of it is structured.
This creates two problems. First, it's hard to find: staff search by filename, date, or memory rather than by content. Second, it often isn't accessible: PDFs fail screen readers, can't be translated, and don't meet modern accessibility standards.
Why AI extraction is now viable for government use cases
Three things have converged to make AI extraction practical at the scale government agencies require.
First, large language models are now good enough at extracting structured data from unstructured text that the error rate is manageable with a review step. A year ago, you'd catch too many errors to trust the output. Today, high-confidence extractions are right the vast majority of the time.
Second, per-token costs have dropped far enough that processing a page of text costs a few cents — not dollars. At scale, this makes AI extraction cost-effective compared to manual labor.
Third, the tooling to wire AI extraction into a CMS publishing pipeline now exists. Pith handles the pipeline from upload to CMS push, including the review step that catches errors before they publish.
Defining what to extract
The most important step in any government extraction project is defining the extraction template carefully. The template determines what fields come out and how consistently they map to your CMS schema.
For government documents, common fields include: document type (against your taxonomy), effective date, issuing agency or department, subject or title, document number or reference, and body text as structured rich content.
The body text field is particularly important. If it extracts as a blob of unstructured text, you haven't gained much over a PDF. If it extracts as structured Portable Text — with proper headings, lists, and paragraph breaks — you have genuinely useful web content.
The accessibility case
Section 508 and ADA Title II require that government digital content be accessible to people with disabilities. PDFs are notoriously difficult to make fully accessible — they require manual tagging and ongoing maintenance.
Structured web content published from Pith is accessible by default: semantic HTML, proper heading hierarchy, text that works with screen readers, and content that can be translated by browser tools.
For agencies facing accessibility compliance requirements, moving document content from PDF to structured web is often the most direct path to compliance.