Nemotron Labs turns documents into real-time business intelligence with AI

Nemotron Labs, from NVIDIA, is driving a new era of document intelligence with its open, GPU-accelerated models, enabling enterprises to automatically extract insights from PDFs, spreadsheets, presentations, and web documents. These multilingual and multimodal systems interpret tables, charts, images, and text, converting complex data into information usable by agents and human teams. The technology relies on techniques such as Retrieval-Augmented Generation (RAG) and specialized agents, allowing extracted data to feed workflows in scientific research, financial services, and legal processes. Public benchmarks such as MTEB and ViDoRe V3 demonstrate the effectiveness of Nemotron models for search, question answering, and comprehension of complex documents. Practical cases show direct business impact. Justt.ai automates the financial dispute cycle, connecting transaction data and customer communications to optimize decisions and recover lost revenue. Docusign applies Nemotron Parse to turn contracts into structured data, enabling rapid analysis of obligations and risks, increasing accuracy and reducing human rework. In scientific research, Edison Scientific's Kosmos AI Scientist integrates Nemotron Parse to decompose papers, index concepts, and provide grounded answers, accelerating literature review and hypothesis generation. The ability to process large multimodal volumes efficiently opens the way for scalable pipelines while maintaining compliance and data security. From a technical standpoint, the NVIDIA stack includes multimodal extraction and OCR, semantic embedding, reranking for relevance, and advanced parsing to maintain correct flow and layout. All of this is packaged as NIM microservices, running on GPUs, allowing teams to scale from prototype to production safely and efficiently. The combination of state-of-the-art and open-source models with intelligent task routing ensures robust performance and optimization of computational costs.