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Document extraction is one of the easiest enterprise AI business cases to justify: documents per month, times minutes each, times loaded cost. The arithmetic is rarely the hard part. Choosing the approach is.
Where OCR plus rules still wins
If documents come from a small number of known senders in stable formats, a template-and-rules pipeline is cheaper, faster, fully deterministic and trivially auditable. Do not use a language model to solve a problem a regular expression already solves.
Where a model earns its cost
Layout variability is what breaks rules-based systems. Hundreds of vendors with different invoice designs, semantic fields that move, tables that span pages, handwriting, multilingual documents - these are cases where a layout-aware model generalises and a template does not.
The hybrid that usually wins
Deterministic extraction where the format is known, model-based extraction for the long tail, confidence scoring on every field, and a human review queue for anything below threshold. The target is not full automation. It is automating the confident majority and routing the rest to a person - which is also the design that survives an audit.
Measure the right thing
Per-field accuracy, not document accuracy. A document with nineteen correct fields and one wrong total is not 95% useful; it is wrong. Weight your evaluation by what the errors actually cost downstream.
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