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Powered by NVIDIA AI Enterprise , solution enables accurate and fast troubleshooting for time-sensitive operational environments .
Kinetica has launched a generative AI solution for enterprise customers that showcases the next step in the evolution of retrievalaugmented generation ( RAG ).
Generative AI applications utilize RAG to access and integrate up-to-date information from external knowledge bases , ensuring responses go beyond a large language model ’ s ( LLM ) original training data .
To truly understand data , AI needs context about the structure , relationships and meaning of tables and columns in an enterprise ’ s data .
However , the prevalent methods of enriching context ( through vector similarity searches ) are inadequate for quantitative data as they are designed primarily to understand textual content .
Kinetica ’ s solution – powered by the NVIDIA NeMo , part of the NVIDIA AI Enterprise software platform and NVIDIA accelerated computing infrastructure – addresses all of these concerns .
Moreover , most ( if not all ) solutions face a significant amount of lag due to reindexing requirements before new data is available for a similarity search .
As a result , these solutions cannot effectively support use cases that need to interface with real-time operational data .
It is founded on two critical components : low-latency vector search ( leveraging NVIDIA RAPIDS RAFT technology ) and the ability to perform real-time , complex data queries .
This combination enables enterprises to enrich their generative AI applications with domain-specific
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