Documents arrive in every layout a supplier base, a customer base or a regulator can produce. Turning them into structured data that a downstream system will accept without a person retyping it is a well-understood problem right up to the point where the input stops conforming — which it always does.
Designing for the exceptions first
The measure that matters in this kind of system is not how much is automated. It is how much can be trusted without supervision. An automation that handles most cases and quietly mishandles the rest is worse than no automation at all, because the errors surface downstream, weeks later, in a place where they are expensive.
So the exception path is designed before the happy path: confidence thresholds, validation against reference data, and an explicit queue for anything uncertain, so that low-confidence results reach a person rather than a ledger.
Where the engineering effort concentrates
Ingestion and capture; field extraction and classification; a validation and business-rule layer; exception handling with human review where confidence is low; and the hand-off into the systems that consume the result.