The Unstract Blog

Robotic Process Automation (RPA) is great at what it was built for: repetitive, rule-based work. And yet, a lot of RPA programs hit the same wall once they move beyond a pilot: documents. RPA with OCR requires a document understanding layer, not just a text extraction engine. Learn more.

Learn how intelligent chunking and retrieval strategies improve LLM accuracy on long documents, and how Unstract implements them in practice.

Markdown-based OCR falls short for LLM-driven structured data extraction. This article compares it with LLMWhisperer, a layout-preserving OCR built for LLM pre-processing, highlighting how retaining spatial structure and confidence scores enables more accurate downstream extraction.

Unstract moves from hardcoded workflows to adaptive, data-driven pipelines. Learn how post-processing webhooks, custom data variables, and prompt chaining enable flexible, future-ready document automation.

Learn how to replace manual document processing with a controlled inbox-to-database workflow that improves accuracy, predictability and trust in downstream data.

Learn how LLMWhisperer and Unstract handle document management end-to-end. LLMWhisperer acts as a next-generation OCR and document parsing engine, preserving layout, understanding checkboxes and handwriting, and extracting high-fidelity data from all major formats, while Unstract applies LLMs for enterprise-grade classification, splitting, parsing, and automated workflows.