In Enterprise AI, Multilingual Information Retrieval is No Longer Optional
In enterprise AI, understanding and working across multiple languages is no longer optional — it’s essential for meeting the needs of employees, customers and users worldwide.
Multilingual Information Retrieval: A Key Component of AI
Multilingual information retrieval — the ability to search, process and retrieve knowledge across languages — plays a key role in enabling AI to deliver more accurate and globally relevant outputs.
Introducing NeMo Retriever
Enterprises can expand their generative AI efforts into accurate, multilingual systems using NVIDIA NeMo Retriever embedding and reranking NVIDIA NIM microservices, which are now available on the NVIDIA API catalog. These models can understand information across a wide range of languages and formats, such as documents, to deliver accurate, context-aware results at massive scale.
Benefits of NeMo Retriever
With NeMo Retriever, businesses can:
- Extract knowledge from large and diverse datasets for additional context to deliver more accurate responses.
- Seamlessly connect generative AI to enterprise data in most major global languages to expand user audiences.
- Deliver actionable intelligence at greater scale with 35x improved data storage efficiency through new techniques such as long context support and dynamic embedding sizing.
NeMo Retriever: A Game-Changer for Enterprises
New NeMo Retriever microservices reduce storage volume needs by 35x, enabling enterprises to process more information at once and fit large knowledge bases on a single server. This makes AI solutions more accessible, cost-effective and easier to scale across organizations.
Leading Partners Adopt NeMo Retriever
Leading NVIDIA partners like DataStax, Cohesity, Cloudera, Nutanix, SAP, VAST Data and WEKA are already adopting these microservices to help organizations across industries securely connect custom models to diverse and large data sources. By using retrieval-augmented generation (RAG) techniques, NeMo Retriever enables AI systems to access richer, more relevant information and effectively bridge linguistic and contextual divides.
Wikidata Speeds Data Processing from 30 Days to Under Three Days
In partnership with DataStax, Wikimedia has implemented NeMo Retriever to vector-embed the content of Wikipedia, serving billions of users. Vector embedding — or “vectorizing” — is a process that transforms data into a format that AI can process and understand to extract insights and drive intelligent decision-making.
Conclusion
NeMo Retriever helps global enterprises overcome linguistic and contextual barriers and unlock the potential of their data. By deploying robust, AI solutions, businesses can achieve accurate, scalable and high-impact results.
Frequently Asked Questions
Q: What is NeMo Retriever?
A: NeMo Retriever is a new microservice that enables enterprises to extract knowledge from large and diverse datasets for additional context to deliver more accurate responses.
Q: What are the benefits of NeMo Retriever?
A: NeMo Retriever helps enterprises extract knowledge from large and diverse datasets, seamlessly connect generative AI to enterprise data in most major global languages, and deliver actionable intelligence at greater scale with improved data storage efficiency.
Q: Which partners are adopting NeMo Retriever?
A: Leading partners like DataStax, Cohesity, Cloudera, Nutanix, SAP, VAST Data and WEKA are adopting NeMo Retriever to help organizations across industries securely connect custom models to diverse and large data sources.
