Inside the Blueprint: Fast Data Extraction and Accurate Retrieval
Enterprises are generating and storing more multimodal data than ever before, yet traditional retrieval systems remain largely text-focused. While they can surface insights from written content, they aren’t extracting critical information embedded in tables, charts, and infographics—often the most information-dense elements of a document.
The AI Blueprint for RAG
In this post, we’ll explore the latest advancements in the AI Blueprint for RAG and dive deep into the core technology under the hood—NVIDIA NeMo Retriever. Discover the latest benchmarks and see how NVIDIA partners are using this blueprint to efficiently extract, index, and query multimodal data and build agentic AI platforms.
Revolutionizing Enterprises and Data Platforms with RAG
Leading NVIDIA partners, including Accenture, Cohesity, DataStax, DDN, Dell, Deloitte, HPE, IBM, NetApp, Nutanix, PureStorage, SAP, Siemens, Teradata, VAST Data, VMware, and WEKA, are already adopting the AI Blueprint for RAG and NeMo Retriever microservices to securely connect custom models to diverse and large data sources enabling their systems and customers to access richer, more relevant information.
Get Started Future-Proofing Your Enterprise with RAG Powered by NVIDIA NeMo Retriever
The AI landscape is evolving rapidly. Enterprises that fail to adopt intelligent retrieval risk falling behind. The NVIDIA AI Blueprint for RAG isn’t just an incremental update—it’s a fundamental shift toward scalable, multimodal, and high-performance retrieval that future-proofs enterprise AI strategies.
Conclusion
The AI Blueprint for RAG is a GPU-accelerated reference example that enables developers to build scalable, context-aware retrieval pipelines tailored to enterprise data. By leveraging the NeMo Retriever microservices, organizations can capture insights from a wide range of enterprise documents, improving both accuracy and throughput.
Frequently Asked Questions
Q: What is the AI Blueprint for RAG?
A: The AI Blueprint for RAG is a GPU-accelerated reference example that enables developers to build scalable, context-aware retrieval pipelines tailored to enterprise data.
Q: What is NeMo Retriever?
A: NeMo Retriever is a microservice that enables fast data extraction and accurate retrieval from multimodal data sources.
Q: How can I get started with the AI Blueprint for RAG?
A: Explore NeMo Retriever microservices on the API catalog to develop enterprise-ready, information retrieval systems that generate context-aware responses from large collections of multimodal data. NeMo Retriever microservices are also now available on AWS SageMaker, Google Cloud Provider GKE, and Azure Marketplace.
Q: Is the AI Blueprint for RAG available for free trial?
A: Yes, the AI Blueprint for RAG is available for a 90-day free trial.
