Intelligent Document Processing

Law firms handle a large volume of case files, reports, contracts, and compliance documents that require manual review to correspond efficiently with clients. While traditional automation tools can be used, they often fail to interpret unstructured text such as legal clauses or handwritten notes.

Legal teams using intelligent document processing to improve their document workflows can better streamline their case preparation and reduce costly oversight. With advanced techniques like retrieval-augmented generation (RAG), teams can automatically extract relevance—such as parties, dates, references, and clauses—from extensive files and PDFs in seconds. For organizations that straddle both legal and financial domains—such as compliance teams or regulatory divisions—AI-driven document intelligence ensures consistent interpretation and reporting across industries. Through the integration of AI and machine learning, teams can coordinate large-scale document reviews efficiently using intelligent document processing (IDP).

NVIDIA provides resources for law firms and legal departments looking to enhance their document intelligence capabilities with generative AI. Legal teams can use RAG to build AI agents that interact with case files, contracts, or compliance documents—allowing attorneys to instantly query specific clauses, summarize discovery material, or locate precedent within large document repositories.

By leveraging NVIDIA’s AI platform, organizations can move beyond simple chatbots to deploy intelligent agents capable of reasoning through complex information. These systems streamline document review, accelerate due diligence, and enhance knowledge management across litigation, regulatory, and corporate practices. For legal professionals collaborating with financial institutions or managing multi-industry compliance, these agents also support contract analysis and regulatory reporting, connecting insights across both legal and financial data.

Optimal Inference for Generative AI Workloads

NVIDIA NIM™, part of NVIDIA AI Enterprise, is a collection of inference microservices accelerating generative AI deployment at scale. NIM now delivers optimized support for NVIDIA Nemotron™ reasoning models—built for advanced reasoning, agentic workflows, and efficient tool use—alongside visual language models like Nemotron Parse for high-throughput document parsing and structured extraction. By integrating community and NVIDIA foundation models, as well as custom AI from the NVIDIA API Catalog, NIM ensures high-throughput, low-latency inferencing via NVIDIA Triton Inference Server™ and NVIDIA® TensorRT™-LLM, empowering organizations to confidently deploy robust AI with advanced reasoning and document understanding both on premises and in the cloud.  

Accelerate Data Curation

NVIDIA NeMo™ Curator is a scalable, GPU-accelerated data-curation microservice that prepares high-quality datasets for pretraining and customizing generative AI models. With it, financial institutions can train and fine-tune LLMs on financial documents. NeMo Curator streamlines data-curation tasks such as data download, text extraction, reformatting, cleaning, quality filtering, and exact/fuzzy deduplication to help reduce the burden of combing through unstructured data sources. Document-level deduplication ensures that LLMs are trained on unique documents, which can greatly reduce pretraining costs. 

Generate Synthetic Data for Responsible AI Training

NVIDIA NeMo Data Designer and NVIDIA NeMo Safe Synthesizer are complementary synthetic data generation tools that transform how organizations use data for AI development. NeMo Data Designer generates high-quality, domain-specific synthetic data from scratch, accelerating model training while removing privacy risks and data collection bottlenecks. NeMo Safe Synthesizer creates privacy-safe synthetic versions of sensitive data, unlocking insights from otherwise unshareable sources. Together, they enable responsible, scalable data use and power intelligent document processing systems that securely extract, classify, and understand information from complex documents.

Real-Time Information Retrieval

Nemotron RAG is a collection of industry-leading extraction, embedding, and reranking models that enable semantic search of enterprise data to deliver highly accurate responses using retrieval augmentation. Developers can use these GPU-accelerated microservices for specific tasks, such as searching for relevant pieces of information within internal data to answer business questions, increasing accuracy and reducing hallucinations.

Machine learning models can recognize and extract critical information from a wide variety of legal documents, including contracts, discovery responses, NDAs, and court filings. With generative AI, law firms can summarize depositions, highlight key arguments, or surface precedent across thousands of pages in minutes—saving hours of manual review.

For legal research, the most valuable insights often lie buried in unstructured data from case law archives, legal journals, or regulatory updates. Generative AI allows legal professionals to query and interact with this information directly—chatting with PDFs or searching across document repositories—to uncover relevant precedents or compliance risks faster than ever before.

Firms are deploying deep learning models to streamline repetitive tasks such as contract review or due diligence, automatically extracting clauses, obligations, and term dates for client transactions or litigation preparation. In areas where law intersects with finance, such as mergers and acquisitions or compliance audits, these same models accelerate verification processes and minimize risk exposure. With natural language understanding (NLU), legal teams can interpret client inquiries, case updates, and legal correspondence accurately, making document management both scalable and intelligent.

Legal organizations looking to deploy generative AI models for intelligent document processing can use the NVIDIA blueprint to quickly build RAG-powered chatbots for legal research, case analysis, and contract review.

Here are the five steps to get going:

1. Start with industry-leading retrieval and reasoning: Retrieve data with the best-in-class accuracy for text and multimodal question answering using Nemotron RAG models and Nemotron reasoning models.

2. Prototype with state-of-the-art generative AI models: Leading foundation models include Meta Llama 3, Google Gemma 7B, Mixtral 8x7B, retrieval models, and NVIDIA’s Nemotron-3 8B family, optimized for the highest performance per cost.

3. Customize foundation models: Tune and test the models with proprietary data using NVIDIA NeMo, an end-to-end platform for developing custom generative AI, anywhere. 

4. The cloud-first way to get the best of NVIDIA AI: NVIDIA DGX™ Cloud is an AI platform for enterprise developers, optimized for the demands of generative AI.

5. Deploy and scale: Run applications anywhere, cloud, data center, or edge, by deploying with NVIDIA NIM, part of NVIDIA AI Enterprise—the production-grade, secure, end-to-end software platform that includes generative AI reference applications and enterprise support.