Product features, releases, updates, roadmaps, and everything in between AI, automation, and data.
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.
Find out why traditional OCR remains the most reliable and cost-effective solution for the vast majority of document-processing workloads.
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
Make LLM-extracted data accurate and reliable
Use MCP to integrate Unstract with your existing stack
Control and trust, backed by human verification
Make LLM-extracted data accurate and reliable
Architecting document workflows that adapt to dynamic processing needs
