Document Intelligence Pattern

This pattern turns unstructured documents into reviewable, structured data without pretending the model is always right. It is designed for teams that process high volumes of bilingual documents and need extraction they can trust and correct.

Arabic document processing is hard: mixed scripts, varied layouts, and legal nuance. A model that extracts confidently but silently is dangerous in this domain. The constraint is accuracy with accountability — every extracted field must be checkable against the source.

• OCR and layout analysis handle mixed Arabic/Latin scripts before extraction.
• The model proposes structured fields; a reviewer confirms or corrects before data is committed.
• Every extracted field links back to its location in the source document.
• Compliance rules run as explicit checks, separate from the model’s judgment.

• Bilingual OCR and layout analysis
• Model-assisted structured extraction
• Human review and correction queues
• Source-linked, checkable fields
• Rule-based compliance checks

• Python
• OCR
• NLP
• Arabic NLP
• LLM adapters

Extraction and review are separate services behind a typed boundary. Model providers are reached through adapters, so the extraction contract stays stable as models change.

This is a reference architecture. Accuracy depends on document types and configuration; no benchmark numbers are claimed, and this is not a named production system.

• Bilingual documents turned into structured, checkable data
• Extraction that a person can verify against the source
• Compliance logic that stays explicit and auditable