Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation

Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER). NER identifies entities from predefined categories, such as persons and organizations. Although various AI services and solutions support NER, this approach is limited to text documents and only supports a fixed set of entities. Furthermore, classic NER models can’t handle other data types such as numeric scores (such as sentiment) or free-form text (such as summary). Generative AI unlocks these possibilities without costly data annotation or model training, enabling more comprehensive intelligent document processing (IDP).

AWS recently announced the general availability of Amazon Bedrock Data Automation, a feature of Amazon Bedrock that automates the generation of valuable insights from unstructured ...