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LLMs and vision-language models depend on clean, structured inputs for accurate results. Discover how OCR text recognition with LLMWhisperer preserves layout and improves document understanding for reliable data extraction.
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.
See Unstract in action with walkthroughs of core features and real extraction workflows.
Managed cloud, on-premise, or open-source. Unstract adapts to your infrastructure needs, so choose what works best for you.
Prompt engineering Interface for Document Extraction
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