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Sections
Document Extraction
What document extraction actually does
Most people think of document extraction as 'the system reads the PDF.' That's a quarter of the problem — and the easy quarter.
The hard parts are what comes after reading: understanding what kind of information you're looking at, knowing where the relevant fields are regardless of layout variation, tolerating scan noise, and producing output structured enough to flow into the systems that need it.
The difference between reading and extracting
Reading gives you | Extraction gives you |
"Net 30" somewhere in a PDF | payment_terms: "Net 30" mapped to invoice ID 88421 |
A wall of text from a discharge summary | Diagnosis: ICD-10 J18.9 | Medications: 3 records | Discharge date: 2024-11-03 |
Contract clause text in a scanned PDF | clause_type: "Indemnification" | obligations: [...] | effective_date: 2023-06-01 |
The difference matters because downstream systems — ERPs, EHRs, claims platforms, contract databases — need structured records, not text files.
The five things an extraction pipeline has to do
An extraction pipeline is a sequence of steps, each handling a specific part of the problem. Here's what happens, in order, for every document:
1. Ingest
Accept documents in whatever format they arrive — PDF, TIFF, JPEG, DOCX, email attachment, fax scan, API payload. Normalize to a consistent internal representation. Handle multi-page documents, rotated pages, poor scan quality, and mixed-format batches without requiring pre-processing by the sender.
2. Classify
Identify what type of document this is. An invoice, a consent form, a bill of lading, an NDA. Classification determines which extraction rules and models apply downstream — misclassify and everything after is wrong.
3. Locate
Find the relevant fields within the document. Where is the invoice total? Where is the patient date of birth? Where is the governing law clause? Layouts vary by vendor, annotations appear in unexpected places, tables span multiple pages.
4. Extract
Pull the actual values from located fields. Normalize to consistent formats — dates to ISO 8601, currency to a standard decimal, names to a structured form. Handle abbreviations, shorthand, and domain-specific terminology.
5. Validate and score
Check extracted values against expected formats, business rules, and cross-field consistency. Flag extractions where confidence is low. Surface a human-review queue that contains only documents that actually need attention.
What makes this hard
Document layouts are not standardized.
Every supplier has a different invoice template. Every hospital system has a different discharge summary format. A system trained on your top-ten suppliers fails when supplier eleven sends their first invoice. Production extraction systems handle layout variance by design, not by exception.
Scan quality is variable and unpredictable.
A faxed invoice photographed on a phone and emailed as a JPEG is a different problem from a machine-generated PDF. Real document populations contain all of these. Extraction accuracy on clean PDFs doesn't predict accuracy on degraded scans.
Handwriting breaks most pipelines.
Handwritten annotations on typed forms. Handwritten dates on signed contracts. Standard OCR doesn't handle these well. Production extraction systems need specific handling for handwriting, with accuracy benchmarked separately.
Tables are structurally ambiguous.
A table with merged cells, nested headers, subtotals, and footnotes is not a grid. It's a visual convention that naive extraction systems flatten incorrectly.
The schema you need isn't always explicit.
You need the effective date. The document has a signature date, an execution date, a date in the recitals, and a date of last amendment. Knowing which one you need requires domain knowledge — not just pattern matching.
Documents change.
Suppliers update templates. Regulatory forms get revised. Extraction systems trained on a static snapshot degrade silently as the document population shifts.
What deployment actually looks like
- Discovery against your real documents. We test against a sample of your actual document population, not demo PDFs. We determine extraction accuracy, identify edge cases, and scope the integration surface.
- Prototype deployment. A reference system running on your documents within 2–4 weeks. You see actual field-level accuracy before any significant investment.
- Integration with your existing systems. Plugging extraction output into your ERP, EHR, contract management platform, or data warehouse.
- Production and ongoing operations. Models monitored continuously, retrained as your document population evolves, drift detected before it affects downstream data quality.
For the engineering detail behind each stage, continue to [Architecture →]. For the operational reality, see [Operations →].
