Nemotron Labs: How AI Agents Are Turning Documents Into Real-Time Business Intelligence
Source: NVIDIA AI Blog
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.
What is Intelligent Document Processing?
Intelligent document processing (IDP) is an AI‑powered workflow that automatically reads, understands, and extracts insights from documents. It interprets rich formats—including tables, charts, images, and text—using:
These techniques turn multimodal content into insights that other multi‑agent systems and people can easily use.
Why NVIDIA Nemotron?
With NVIDIA Nemotron open models and GPU‑accelerated libraries, organizations can build AI‑powered document‑intelligence systems for:
- Research
- Financial services
- Legal workflows
- And many other domains
These open models, datasets, and training recipes have achieved strong results on leaderboards such as:
Teams can select the best models for tasks like search and question answering.
How Document Processing Streamlines Business Intelligence
Document‑intelligence systems that can extract meaning from complex layouts, scale to massive file libraries, and pinpoint exactly where an answer originated are invaluable in high‑stakes environments. These systems:
- Understand rich document content – go beyond simple text scraping to capture information from charts, tables, figures, and mixed‑language pages. They treat documents like a human would, recognizing structure, relationships, and context.
- Handle large, shifting data sets – ingest and process massive collections of documents in parallel, keeping knowledge bases continuously up‑to‑date.
- Find exactly what users need – help AI agents locate the most relevant passages, tables, or paragraphs for a query, enabling precise and accurate responses.
- Show the evidence behind answers – provide citations to specific pages or charts, giving teams 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 reshaping how organizations across industries gain insights from their documents.
Justt – AI‑Native Chargeback Management & Dispute Optimization
In financial services, payment disputes generate significant revenue loss and operational complexity for merchants because the required evidence lives in unstructured formats. Transaction logs, customer communications, and policy documents are often fragmented across systems, making dispute handling slow, manual, and costly.
Solution
- AI‑driven platform that automates the full chargeback lifecycle at scale.
- Connects directly to payment service providers and merchant data sources to ingest transaction data, customer interactions, and policies.
- Automatically assembles dispute‑specific evidence that aligns with card‑network and issuer requirements.
Key capabilities
- Dispute optimization (powered by Nemotron Parse) uses predictive analytics to decide which chargebacks to fight or accept and how to craft each response for maximum net recovery.
- Real‑world impact: hospitality operators such as HEI Hotels & Resorts have automated dispute handling across properties, recapturing revenue while preserving guest relationships.
Business outcome
- Merchants recover a significant portion of revenue lost to illegitimate chargebacks.
- Manual review effort is dramatically reduced.
DocuSign – Scaling Agreement Intelligence
DocuSign is the global leader in Intelligent Agreement Management, processing millions of transactions daily for more than 1.8 million customers and 1 billion users.
Challenge
Agreements contain critical information that is often buried in pages of PDFs. To surface that data, DocuSign needed high‑fidelity extraction of tables, text, and metadata from complex documents.
Solution
- Evaluating Nemotron Parse for deeper contract understanding at scale.
- Running on NVIDIA GPUs, the model combines advanced AI with layout detection and OCR.
- Reliably interprets complex tables and reconstructs them with required information, reducing manual corrections.
Impact
- Transforms agreement repositories into structured data that powers contract search, analysis, and AI‑driven workflows.
- Turns agreements into business assets, improving visibility, reducing risk, and enabling faster decision‑making.
Edison Scientific – Research Across Massive Literature Scale
Edison Scientific’s Kosmos AI Scientist helps researchers navigate complex scientific landscapes, synthesize literature, identify connections, and surface evidence.
Problem
Extracting structured information (equations, tables, figures) from large volumes of PDFs is error‑prone for traditional parsers.
Solution
- Integrated NVIDIA Nemotron Parse into the PaperQA2 pipeline.
- Decomposes research papers, indexes key concepts, and grounds responses in specific passages.
Benefits
- Improves throughput and answer quality for scientists.
- Turns a sprawling research corpus into an interactive, queryable knowledge engine that accelerates hypothesis generation and literature review.
- High efficiency enables cost‑effective serving at scale, unlocking the full multimodal pipeline.
All three use cases demonstrate how Nemotron Parse and NVIDIA’s accelerated AI stack turn unstructured documents into actionable, structured intelligence, delivering measurable business value across finance, legal, and scientific domains.
Designing an Intelligent Document‑Processing Application with NVIDIA Technologies
A robust, domain‑specific document‑intelligence pipeline needs components that can extract, embed, rerank, and parse data while keeping it secure and compliant.
| Stage | NVIDIA Solution | What It Does |
|---|---|---|
| Extraction | Nemotron extraction & OCR models | Ingest multimodal PDFs, text, tables, graphs, and images; convert them to structured, machine‑readable content while preserving layout and semantics. |
| Embedding | Nemotron embedding models | Transform passages, entities, and visual elements into vector representations (embeddings) tuned for document retrieval, enabling semantically accurate search. |
| Reranking | Nemotron reranking models | Evaluate candidate passages so the most relevant content is provided as context for large language models (LLMs), improving answer fidelity and reducing hallucinations. |
| Parsing | Nemotron Parse models | Decipher document semantics, extract text and tables with precise spatial grounding and correct reading flow, turning unstructured documents into actionable data. |
These capabilities are delivered as NVIDIA NIM micro‑services and foundation models that run efficiently on NVIDIA GPUs, allowing teams to scale from proof‑of‑concept to production while keeping sensitive data inside their chosen cloud or on‑premise environment.
Why a Mixed‑Model Approach Works
- Frontier + open‑source: Combine cutting‑edge proprietary models with open‑source Nemotron models.
- LLM router: An intelligent router evaluates each request and automatically selects the most suitable model, balancing performance, cost, and efficiency.
By leveraging this architecture, organizations can build document‑intelligence pipelines that are scalable, secure, and cost‑effective, while delivering high‑quality results for downstream LLM‑driven applications.
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.
- Experiment with Nemotron RAG models and the NVIDIA NeMo Retriever open library, available on:
- Try Nemotron Parse on Hugging Face.
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