Define the record you actually need
Do not start by asking a model to “understand this document”. Start with the fields your receiving process needs. For an invoice, those may include the supplier reference, invoice date, currency, line items and total. For an intake form, they may be contact details, requested service and supporting evidence.
Our document-processing service separates intake, extraction, validation and review. Each stage has a different job. A system can read a value correctly while the document itself is wrong. Keep that distinction visible, particularly when information will affect accounting records, customer accounts or contractual decisions.
Distinguish OCR from interpretation
Optical character recognition, or OCR, converts visible text in an image into machine-readable text. It can struggle with skewed scans, faint print, handwriting and crowded layouts. A digitally generated PDF may already contain usable text, so rendering every page into an image can add unnecessary work.
Extraction goes further: it assigns meaning to the text and maps it to fields. Microsoft Azure AI Document Intelligence, Amazon Textract and Google Cloud Document AI provide document-analysis approaches worth comparing for the required document types. Check language support, regional processing, field evidence and charging rules in their documentation. Product names alone do not tell you which approach fits your collection.
Compare templates with flexible extraction
Template-based rules can be effective when documents follow a stable layout. They are easier to reason about but can break when a supplier moves a field or changes a table. A model-based approach may handle more variation, while introducing uncertainty that needs explicit validation.
Use a representative collection to compare them. Include different suppliers, scan qualities, multi-page tables and documents with missing fields. Keep some documents out of development so that evaluation is not simply recognition of examples already used to tune the system. Measure the fields that matter to the process rather than awarding the whole page a single reassuring score.
Preserve uncertainty instead of guessing
A schema defines the shape of the output: field names, data types and allowed values. Require the extraction step to follow it, then validate the result outside the model. Missing information should stay missing. A plausible purchase-order reference invented to complete the schema is worse than an empty field that triggers review.
Store the original value alongside any normalised value. Dates such as 03/04 can be ambiguous without context. Currency symbols may not uniquely identify a currency. Retain page references or bounding boxes where the extraction tool supplies them, so a reviewer can inspect the evidence without searching the entire file.
Apply business checks separately
Compare supplier identifiers with an approved source. Check totals using deterministic arithmetic and account for the rounding rules your process uses. Detect possible duplicates using relevant document identifiers rather than file names alone. Treat a credit note differently from an invoice, even when their layouts look similar.
Extraction must not become automatic approval. A document that names a new bank account still requires your established verification process. Likewise, identifying a tax field does not establish its legal treatment. Accounting and tax decisions remain with appropriately qualified people. The system should provide evidence and clear exceptions, not replace those responsibilities.
Design the review screen around corrections
Show the document and the extracted fields together. Highlight unresolved values and explain which rule failed. Let reviewers correct a field without retyping the whole record. Record the change and the approval decision while limiting unnecessary copies of personal information.
Agree what happens to unsupported files, unreadable pages and mixed document bundles. Quarantine suspicious uploads and validate file types before processing. Define retention for originals, extracted records and diagnostic logs separately. A document may be needed for business records long after a temporary processing copy should have been deleted.
Connect only after the record is ready
An agreed delivery scope can include an intake route, extraction schema, validation rules, a review queue and a controlled export. Establish acceptance criteria by field and document type. The person responsible for the destination system should approve the mapping before write access is enabled.
If the main problem is what happens after extraction, continue into workflow automation. If you want staff to ask questions across an approved document library, explore knowledge assistants. These are related capabilities, but they need different checks. Reading a page, approving a transaction and answering a policy question are not the same task.