Implement Information Extraction Solutions
Document Intelligence model families
Choose the model by document variability, not by document name. Prebuilt models (invoice, receipt, ID document, health insurance card, bank statement, contract) return a defined field schema with confidence scores. Layout returns text, tables, selection marks, and paragraph structure. Read is pure OCR. Custom template models require labeled samples and excel when every document shares one fixed layout. Custom neural models generalize across varied layouts and handwriting but need more data and cost more to train. A composed model bundles several custom models behind one model ID; a classifier routes mixed document types before extraction, and split handles multi-document files.
Building a searchable knowledge base
For retrieval over documents, the Azure AI Search pattern is: indexer pulls from blob storage → skillset enriches (OCR, language detection, entity extraction, chunking, embedding) → fields land in the index → queries use hybrid + semantic ranking. Optionally persist enrichments in a knowledge store. Integrated vectorization lets the indexer chunk and embed without custom code.
Traps
Picking custom template for handwritten or shifting layouts; forgetting that extraction confidence below threshold should route to human review; assuming a single custom model handles a container full of mixed document types - that needs a classifier. Field bounding boxes and confidence values matter in exam scenarios about validation.
Rule of thumb
Ask "does the layout change between documents?" No → template. Yes → neural. Mixed types in one location → classifier first, extraction second. Need answers with page citations over a corpus → Azure AI Search with chunking and semantic ranker.