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🔥 Our open-source model Nanonets-OCR2 is trending in the Top 10 on Hugging Face, again. But this isn’t just a leaderboard moment. It’s another step in our mission to make the world more LLM-friendly and to free all the stuck data still trapped in PDFs, scans, and unstructured documents. For years, we’ve been obsessed with one question: 👉 How do we make machines truly understand documents like humans do? That’s why we’re committed to: Releasing best-in-class open-source models that raise the bar for document intelligence. Powering enterprise-grade document processing and agentic workflows that help companies unlock and use their data in ways that were impossible before. Open source isn’t a side project for us, it’s the foundation. Because the future of document understanding will be both open and enterprise-ready. Huge thanks to everyone who’s supported the journey so far. We’re just getting started. 🚀 #OpenSource #AI #LLMs #DocumentAI #HuggingFace #Nanonets

We ran a head-to-head benchmark of AWS Textract against nanonets-ocr-2, our VLM-based doc processing model. Textract breaks reading order on multi-column pages. It skips charts and maps entirely. It struggles with merged cells and nested tables. And it still outputs raw text blocks instead of structured markdown. If your RAG pipeline or agent is producing garbage answers, the problem probably isn't your LLM. It's your parser. Here's what we found across 7 dimensions: - Layout and reading order: Textract gives up on complex pages. We preserve every section in order. - Visual elements: Textract labels them "Figure 1" and moves on. We describe the content. - Complex tables: Merged cells, spanning rows, sub-headers. We handle them. Textract doesn't. - Multilingual: 200+ languages out of the box. No config. - Signatures and stamps: Detected, linked to signees, included in output. - Accuracy on hard docs: +15% better on Docext and OlmOCR-Bench. - LLM-ready output: Clean markdown with doc structure preserved, and confidence scores One model. Structured markdown out. Confidence scores included. Deploys on-prem in a single Docker container. Try it on the documents your current parser can't handle.

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Your procurement cycles aren't slow because of your systems - they're slow because your team is spending 90% time manually handling supplier documents. Our AI Agents free them up to do their best work.

From supplier onboarding to invoice resolution, your procurement processes are still leaking because of manual handling of documents. Nanonets eliminates this.

F100 teams use Nanonets to save ~90% of time in vendor onboarding, RFQ management, PR/PO flows, GRN entry, invoice reconciliation, and reviewing supplier contracts, allowing sourcing cycles to move faster.