Connect your data to AI with comprehensive reference workflows that accelerate AI application development and deployment, featuring NVIDIA NIM and NeMo building blocks for RAG, AI agents, digital humans, and more.
Connect AI applications to multimodal enterprise data with a scalable retrieval augmented generation (RAG) pipeline built on highly performant, industry-leading NIM microservices, for faster PDF data extraction and more accurate information retrieval.
Create intelligent virtual assistants for customer service across every industry
Ingest massive volumes of live or archived videos and extract insights for summarization and interactive Q&A
Create intelligent, interactive avatars for customer service across industries
Create AI agents that reason, plan, reflect and refine to produce high-quality reports based on source materials of your choice.
Automate research, and generate blogs with AI Agents using LlamaIndex and Llama3.3-70B NIM LLM.
Document your github repositories with AI Agents using CrewAI and Llama3.3 70B NIM.
Improve information retrieval accuracy with world-class NVIDIA NeMo Retriever models for reranking retrieved enterprise data to improve answer relevancy.
Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
Multilingual text reranking model.
Leverage retrieval-augmented generation to ground large language models in your proprietary data.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Cutting-edge vision-language model exceling in retrieving text and metadata from images.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Connect AI agents to enterprise data with world-class NVIDIA NeMo Retriever and community models for multilingual/cross-lingual text question-answering.
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
English text embedding model for question-answering retrieval.
Optimized community model for text embedding.
