Product features, releases, updates, roadmaps, and everything in between AI, automation, and data.
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.
Explore Unstract, a modern AI-native alternative to Nanonets, offering a prompt-driven, modular platform for multi-service text extraction, human-in-the-loop validation, and seamless deployment via ETL pipelines and APIs.
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
How to pick the right document extraction platform in 2026: Legacy IDP to LLMs
