Editor’s note: This post is part of the Nemotron Labs blog series, which explores how the latest open models, datasets and training techniques help businesses build specialized AI systems and applications on NVIDIA platforms. Each post highlights practical ways to use an open stack to deliver value in production — from transparent research copilots to scalable AI agents.
Businesses today face the challenge of uncovering valuable insights buried within a wide variety of documents — including reports, presentations, PDFs, web pages and spreadsheets.
Often, teams piece together insights by manually reviewing files, copying data into spreadsheets, building dashboards and using basic search or template-based optical character recognition (OCR) tools that often miss important details in complex media.
Intelligent document processing is an AI-powered workflow that automatically reads, understands and extracts insights from documents. It interprets rich formats inside those documents — including tables, charts, images and text — using AI agents and techniques like retrieval-augmented generation (RAG) to turn the multimodal content into insights that other multi-agent systems and people can easily use.
With NVIDIA Nemotron open models and GPU-accelerated libraries, organizations can build AI-powered document intelligence systems for research, financial services, legal workflows and more.
These open models, datasets and training recipes have powered strong results on leaderboards such as MTEB, MMTEB and ViDoRe V3, benchmarks for evaluating multilingual and multimodal retrieval models. Teams can choose from among the best models for tasks like search and question answering.
How Document Processing Streamlines Business Intelligence
Document intelligence systems that can pull meaning from complex layouts, scale to huge file libraries and show exactly where an answer came from are incredibly useful in high-stakes environments. These systems:
- Understand rich document content, moving beyond simple text scraping to capture information from charts, tables, figures and mixed-language pages and treating documents as a human would by recognizing structure, relationships and context.
- Handle large quantities of shifting data, ingesting and processing massive collections of documents in parallel, and keeping knowledge bases continuously up to date.
- Find exactly what users need, helping AI agents pinpoint the most relevant passages, tables or paragraphs to a query so they can respond with precision and accuracy.
- Show the evidence behind answers by providing citations to specific pages or charts so teams can gain transparency and auditability, which is critical in regulated industries.
The result is a shift from static document archives to living knowledge systems that directly power business intelligence, customer experiences and operational workflows.
Document Intelligence at Work
Intelligent document processing systems built on NVIDIA Nemotron RAG models, Nemotron Parse and accelerated computing are already reshaping how organizations across industries gain insights from their documents.
Justt: AI-Native Chargeback Management and Dispute Optimization
In financial services, payment disputes create significant revenue loss and operational complexity for merchants, largely because the evidence needed to handle them lives in unstructured formats. Transaction logs, customer communications and policy documents are often fragmented across systems and difficult to process at scale, making dispute handling slow, manual and costly.
Justt.ai provides an AI-driven platform that automates the full chargeback lifecycle at scale. The platform connects directly to payment service providers and merchant data sources to ingest transaction data, customer interactions and policies, then automatically assembles dispute-specific evidence that aligns with card network and issuer requirements.
The platform’s AI-powered dispute optimization, powered by Nemotron Parse, applies predictive analytics to determine which chargebacks to fight or accept, and how to optimize each response for maximum net recovery. Leading hospitality operators like HEI Hotels & Resorts use the platform to automate dispute handling across their properties, recapturing revenue while maintaining guest relationships.
By pairing document-centric intelligence with decision automation, merchants can recapture a significant portion of revenue lost to illegitimate chargebacks while reducing manual review effort.
Docusign: Scaling Agreement Intelligence
Docusign is the global leader in Intelligent Agreement Management, handling millions of transactions every day for more than 1.8 million customers and over 1 billion users.
Agreements are the foundation of every business, but the critical information they contain are often buried inside pages of documents. To surface the information, Docusign needed high-fidelity extraction of tables, text and metadata from complex documents like PDFs so organizations could understand and act on obligations, risks and opportunities faster.
Docusign is evaluating Nemotron Parse for deeper contract understanding at scale. Running on NVIDIA GPUs, the model combines advanced AI with layout detection and OCR. The system can reliably interpret complex tables and reconstruct tables with required information. This reduces the need for manual corrections and helps ensure that even the most complex contracts are processed with the speed and accuracy their customers expect.
With this foundation, Docusign will transform agreement repositories into structured data that powers contract search, analysis and AI-driven workflows — turning agreements into business assets that help organizations and their teams improve visibility, reduce risk and make faster decisions.
Edison Scientific: Research Across Massive Literature Scale
Edison Scientific’s Kosmos AI Scientist helps researchers navigate complex scientific landscapes to synthesize literature, identify connections and surface evidence.
Edison needed a way to rapidly and accurately extract structured information from large volumes of PDFs, including equations, tables and figures that traditional information parsing methods often mishandle.
By integrating the NVIDIA Nemotron Parse model into its PaperQA2 pipeline, Edison can decompose research papers, index key concepts and ground responses in specific passages, improving both throughput and answer quality for scientists. This approach turns a sprawling research corpus into an interactive, queryable knowledge engine that accelerates hypothesis generation and literature review.
The high efficiency of Nemotron Parse enables cost-efficient serving at scale, allowing Edison’s team to unlock the whole multimodal pipeline.
Designing an Intelligent Document Processing Application With NVIDIA Technologies
A robust, domain-specific document intelligence pipeline requires technologies that can handle data extraction, embedding and reranking, while keeping the data secure and compliant with regulations.
- Extraction: Nemotron extraction and OCR models rapidly ingest multimodal PDFs, text, tables, graphs and images to convert them into structured, machine-readable content while preserving layout and semantics.
- Embedding: Nemotron embedding models convert passages, entities and visual elements into vector representations tuned for document retrieval, enabling semantically accurate search.
- Reranking: Nemotron reranking models evaluate candidate passages to ensure the most relevant content is surfaced as context for large language models (LLMs), improving answer fidelity and reducing hallucinations.
- Parsing: Nemotron Parse models decipher document semantics to extract text and tables with precise spatial grounding and correct reading flow. Overcoming layout variability, they turn unstructured documents into actionable data that enhances the accuracy of LLMs and agentic workflows.
These capabilities are packaged as NVIDIA NIM microservices and foundation models that run efficiently on NVIDIA GPUs, allowing teams to scale from proof of concept to production while keeping sensitive data within their chosen cloud or data center environment.
The most effective AI systems use a mix of frontier models and open source models like NVIDIA Nemotron, with an LLM router analyzing each task and automatically selecting the model best suited for it. This approach keeps performance strong while managing computing costs and improving efficiency.
Get Started With NVIDIA Nemotron
Access a step-by-step tutorial on how to build a document processing pipeline with RAG capabilities. Explore how Nemotron RAG can power specialized agents tailored for different industries.
Plus, experiment with Nemotron RAG models and the NVIDIA NeMo Retriever open library, available on GitHub and Hugging Face, as well as Nemotron Parse on Hugging Face.
Join the community of developers building with the NVIDIA Blueprint for Enterprise RAG — trusted by a dozen industry-leading AI Data Platform providers and available now on build.nvidia.com, GitHub and the NGC catalog.
Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA AI news, joining the community and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.
