Azure AI Document Intelligence (formerly Form Recognizer) extracts text, tables and key-value pairs from unstructured documents and transforms them into structured data. It uses pre-trained deep learning models to interpret layouts and semantics, and custom models can be trained through a no-code interface for specialized formats. In some cases, however, power users may require a custom fine-tuning interface instead.
One of our teams reported that ADI significantly reduced manual data entry, improved data accuracy and accelerated reporting, leading to faster data-driven decisions. Like Amazon Textract and Google Document AI, it provides enterprise-grade document processing with strong layout understanding. An emerging open-source alternative is IBM’s Docling, which offers a more flexible, code-centric approach to structured data extraction. Compared to traditional OCR tools, ADI captures not just text but also structure and relationships, making it easy to integrate into downstream data pipelines. That said, we’ve observed occasional latency when embedding it into synchronous user workflows, so we recommend using it primarily for asynchronous processing.
