Businesses are increasingly turning to intelligent document processing to extract value from sprawling archives of reports, PDFs, web pages, presentations and spreadsheets. Instead of relying on manual review, spreadsheets and basic search or template-based optical character recognition tools, organizations are deploying Artificial Intelligence agents and retrieval-augmented generation techniques to interpret multimodal content, including tables, charts, images and mixed-language text. These systems treat documents more like humans do, recognizing layout, structure and relationships, and then transforming static archives into living knowledge bases that directly feed business intelligence, customer experiences and operational workflows.
Nvidia’s Nemotron open models and GPU-accelerated libraries underpin a full document intelligence stack that spans extraction, embedding, reranking and parsing. Nemotron extraction and OCR models ingest multimodal PDFs and convert text, tables, graphs and images into structured, machine-readable content while preserving layout and semantics. Nemotron embedding models turn passages and visual elements into vector representations tuned for document retrieval, while Nemotron reranking models score candidate passages so the most relevant context is supplied to large language models, improving answer fidelity and reducing hallucinations. Nemotron Parse models then decipher document semantics, providing spatially grounded text and tables that feed downstream Artificial Intelligence agents and workflows, all delivered through Nvidia NIM microservices and foundation models running on Nvidia GPUs.
Real-world deployments illustrate how this architecture is reshaping workflows in multiple industries. Justt.ai uses Nemotron Parse within an Artificial Intelligence-native chargeback management platform that connects to payment providers and merchant systems, automatically assembling dispute evidence from fragmented transaction logs, policies and communications so merchants can recapture revenue lost to illegitimate chargebacks while reducing manual review. Docusign, which handles millions of transactions every day for more than 1.8 million customers and over 1 billion users, is evaluating Nemotron Parse to perform high-fidelity extraction of tables, text and metadata from complex agreement PDFs, turning contract repositories into structured data for search, analysis and Artificial Intelligence-driven workflows. Edison Scientific integrates Nemotron Parse into its PaperQA pipeline for the Kosmos Artificial Intelligence Scientist, decomposing research papers, indexing key concepts and grounding answers in specific passages so scientists can query massive literature corpora more effectively and at lower serving cost. Nvidia positions these components as part of an open, enterprise-ready RAG blueprint, encouraging developers to combine frontier and open source models with routers that automatically select the best model per task, and to experiment with Nemotron RAG and NeMo Retriever via GitHub, Hugging Face and Nvidia’s cloud catalogs.
