Google Document AI

Document AI turns paper and PDF documents into structured data. Prebuilt processors read common document types: invoices with their line items, expense receipts, bank statements, government IDs, and forms. An OCR processor extracts text with layout information, and a layout parser prepares documents for use with language models by splitting them into meaningful chunks.

Customization is a strength. A custom extractor, based on Google’s Gemini models, can be set up with a few examples to pull specific fields from a company’s own documents, even if they vary in layout. A workbench lets teams train and evaluate processors, and a human-in-the-loop option sends uncertain results to reviewers. The output integrates with BigQuery, Cloud Storage, and workflow tools, which helps feed data to analytics and AI systems.

Document AI is billed per page by processor type, with a free tier for evaluation. Accuracy depends on scan quality and document variety, and sensitive documents require attention to data governance. Alternatives include Amazon Textract, Azure AI Document Intelligence, ABBYY, and open-source models. Document AI suits organizations on Google Cloud that process large volumes of forms, invoices, and contracts.