Creating an organized system from cluttered folders.

The extraction pipeline handles PDFs (digital and scanned), images (JPEG, PNG, TIFF), Microsoft Office documents, email attachments, and structured forms. Azure Document Intelligence provides pre-built models for invoices, receipts, ID documents, tax forms, and health insurance cards. For specialized document types unique to your organization, we train custom extraction models on your sample documents during the Discovery Sprint.

Accuracy depends on document quality and complexity. Pre-built models for standard document types (invoices, receipts) achieve high extraction accuracy out of the box. Custom models trained on your specific documents improve with each correction cycle. Every extracted field carries a confidence score, and the human-in-the-loop review queue routes low-confidence extractions to your team for verification — so errors are caught before they reach downstream systems.

The pipeline outputs structured data via REST API, webhook, or direct database writes — formatted to match your target system schema. We build connectors for ERP systems, CRM platforms, data warehouses, document management systems, and workflow engines. The integration layer handles field mapping, data validation, and error handling so extracted data arrives clean and ready to use.

Every document gets a processing confidence score. Documents below the confidence threshold are routed to a human review queue with the partial extraction pre-filled — your team corrects rather than re-enters from scratch. Failed documents are logged with the failure reason, and patterns in failures feed back into model improvement. The system degrades gracefully, never silently drops a document.

Yes, with expectations set appropriately. Azure Document Intelligence handles printed text on poor-quality scans well. Handwriting recognition works for structured fields (dates, amounts, short text) with reasonable accuracy; free-form handwritten paragraphs are harder. During the Discovery Sprint, we test your actual document samples and report extraction accuracy by document type and quality level — so you know exactly what to expect before committing to a production build.

Standard OCR converts images to text. It does not understand document structure, extract specific fields, classify document types, or route documents to workflows. Intelligent document processing adds layout analysis (understanding tables, headers, sections), field extraction (pulling specific data points into structured output), classification (knowing what type of document it is), and validation (confidence scoring and error detection). The result is structured data, not raw text.