Data Extraction

Turning a document into a set of structured fields is the entire job of Data Extraction tools, and the input is rarely clean. Scanned PDFs, phone photographs, email attachments and long contracts all arrive in the same queue. Processing runs in stages: optical character recognition for scanned and handwritten material, layout analysis that recovers tables and reading order, classification by document type, then extraction of named fields into a defined schema with a confidence score on each value. Validation rules catch totals that do not add up, a review interface routes low-confidence fields to a person, and the approved record is pushed onward to a database or API.

Finance teams process invoices; logistics handles customs paperwork; insurers read claim forms; legal teams pull clauses from agreements; recruiters parse applications. Compare accuracy on your own document mix, table fidelity, handwriting and language coverage, whether a schema can be defined by description or needs labeled samples, throughput, the review queue, and data residency.

Benchmark on your worst documents, not clean samples, and measure accuracy field by field, since one wrong digit invalidates a record. Budget for human review permanently. Watch for merged table cells, misread dates, confident values taken from the wrong page, and drift when a supplier changes layout. Pricing is typically per page, tiered by volume, with reviewer seats or a self-hosted option.