How do you implement automated document processing workflows?

Document processing automation follows an extraction-validation-action pipeline. Ingestion: accept documents via email attachment, file upload, API, or watched folder; classify document type using a lightweight CNN or rule-based classifier. Extraction: use OCR (Tesseract, AWS Textract, Google Document AI) for scanned documents, parse structured formats (PDF with embedded text, DOCX, XLSX) directly, and apply NER models or regex patterns for field extraction (dates, amounts, names, addresses). Validation: cross-reference extracted data against master data (customer database, product catalog), apply business rules (amount within expected range, dates are valid), and flag anomalies for human review with confidence scores. Action: route validated data to downstream systems (ERP, CRM, accounting), trigger approval workflows for exceptions, and archive processed documents with extracted metadata. Implementation: use Apache Tika for format detection, implement a template library for known document layouts, train custom extraction models for domain-specific documents, and maintain a human review queue with a feedback loop to improve extraction accuracy over time. Metrics: track extraction accuracy, straight-through processing rate, and time savings versus manual processing.