Document AI: From PDFs and Invoices to Structured Data

Topics covered

• •Explain what a document AI pipeline does beyond 'reading' a file
• •Tell born-digital PDFs, scans, photos and structured e-invoices apart and know why the difference matters
• •Compare classic OCR, layout-aware parsers and prebuilt models, and vision-language models on accuracy, cost, latency, privacy and language coverage

Topics covered

• •Extract text from born-digital PDFs and know the typical failure modes
• •Prepare scans and photos for OCR and choose between open-source engines
• •Describe honestly what Docling, Marker, Unstructured, Tesseract, PaddleOCR and the major cloud services do, including licence and language limits

Topics covered

• •Design an extraction schema from what downstream systems need, with types, enums, nullable fields and descriptions
• •Use structured outputs or function calling so model responses always match the schema, and know their limits
• •Normalise dates, amounts, currencies and VAT IDs across English, German and Ukrainian documents

Topics covered

• •Explain why schema-valid output can still be wrong and design checks that catch it
• •Write format, arithmetic, master-data and duplicate rules for invoices, including IBAN mod-97 and VAT maths
• •Use confidence scores, grounding and agreement between extractions as routing signals, not as proof

Topics covered

• •Lay out an end-to-end pipeline: intake, classification, extraction, validation, review, export and archive
• •Build the stages in n8n with the right triggers, AI nodes, human-in-the-loop steps and error handling
• •Make the pipeline idempotent so that retries and duplicate emails never create double postings

Topics covered

• •Build a representative labelled test set and labelling conventions
• •Calculate field-level exact match, precision and recall, document-level accuracy and the straight-through-processing rate
• •Interpret small samples honestly and set thresholds that trade review effort against undetected errors