Document Parsing & Structuring

Turns a document into data. Ingests a file of unknown quality and format, recovers its text and layout, and emits structured fields a downstream system can act on. The decisive shift from classic OCR is that a vision-capable model handles layouts it has never seen without a per-format template, which is what makes long-tail document types economically viable at all.

Document types are varied or unpredictable; volumes are high enough that manual keying is the bottleneck; the downstream consumer needs fields rather than text.

A handful of stable formats from cooperative senders — a configured parser is cheaper, faster and deterministic. Also poor where the document is the legal artefact and any extraction error carries liability, unless every field is confirmed.

Extracted values must be traceable to their location in the source, and confidence must be exposed rather than hidden. Where extraction feeds a financial or legal record, a human confirms before it is acted on.

Silent misextraction. A field read from the wrong position produces a plausible value that passes every downstream validation, which is far more damaging than a failure to extract at all.

A shared services team receives supplier invoices as PDFs from several hundred suppliers, each with its own layout. Extraction pulls invoice number, date, net, tax and line items into the payables system; anything below a confidence threshold routes to a person. The saving is the keying, not the judgement.