AI
Amazon Bedrock Data Automation replaces template-based document extraction
AWS's new generative AI service uses foundation models to intelligently extract and transform unstructured content like PDFs, images, and video without rigid rule-based systems.
The New Stack
Traditional template-based extraction systems are struggling to keep pace with modern business needs. Companies face mounting challenges processing diverse, unstructured data—PDFs, contracts, scanned images, call recordings, and video—across multiple formats that don't fit rigid schemas.
Amazon Bedrock Data Automation (BDA), a new fully managed AWS service, takes a different approach. Rather than relying on brittle rules and templates, it leverages foundation models to intelligently extract, classify, and transform content across documents, images, audio, and video.
How it works#
The service offers two output modes:
- Standard output: Raw, unstructured responses directly from the pipeline for each modality
- Custom output via blueprints: Users define structured, predictable schemas that match their exact business requirements
Blueprints can be auto-generated by inspecting documents or manually configured. The system extracts fields with confidence scores and supports five core operations: classification, extraction, transformation, normalization, and validation. According to the documentation, "Compared with rule-based systems, foundation models achieve better semantic extraction across the board."
Real-world applications#
The technology is built for enterprise workflows. Common use cases include:
- Financial services: Automating bank statements, invoices, and loan applications
- Insurance: Processing claims forms and damage assessments
- HR & Legal: Extracting data from resumes, contracts, and offer letters
- Customer support: Transcribing calls and detecting sentiment
- Compliance: Analyzing video footage and flagging issues
Key advantages#
BDA supports batch processing, versioning for safe testing (development vs. live stages), and provides audit trails with structured field types and validation rules. The multimodal approach means teams can process any combination of document types with a single framework, reducing the complexity of maintaining separate extraction pipelines.
