How do you implement AI-powered document intelligence platforms?

Document intelligence automates extraction, classification, and analysis of business documents. Architecture: document ingestion (multi-format support: PDF, images, DOCX, email), classification (determine document type using a trained classifier or LLM), extraction (pull structured data from unstructured documents), validation (verify extracted data against business rules), and integration (feed extracted data into downstream systems). Classification: train a document classifier on labeled examples (invoice, contract, receipt, form, correspondence). Use a CNN on document images for layout-based classification or a text classifier for content-based classification. Extraction approaches: (1) Template-based: define extraction rules for known document layouts (fixed positions for fields, anchor text for relative positioning). Fast and accurate for standardized documents. (2) Model-based: use pre-trained models (LayoutLMv3, Donut, Azure Document Intelligence, Google Document AI) that understand document layout and extract fields without templates. Works for varied layouts. (3) LLM-based: send document text to an LLM with extraction instructions. Most flexible, handles novel documents, but higher cost and latency. Key features: table extraction (detect table boundaries, parse rows and columns, handle spanning cells), handwriting recognition (for forms with handwritten entries), signature detection (for contract execution verification), and multi-page document handling (track entities across pages). Validation pipeline: type validation (dates, numbers, currencies in correct format), business rule validation (amounts match line items, dates are logical), cross-reference validation (customer ID exists in database, PO number matches records), and confidence-based routing (high confidence auto-processes, low confidence queues for review). Human review interface: display original document alongside extracted data, enable easy correction with click-to-edit, feed corrections back to improve extraction models, and track extraction accuracy trends over time.