In the News

"An advanced conversational AI powered by Kinetica, SQL-GPT allows everyday language, rather than SQL commands, to be used for complex data queries. It offers unmatched speed, accuracy and scalability across 5G and OpenRAN environments, providing real-time data analysis that accelerates decision-making."

"Kinetica provides a distributed, columnar, real-time analytical database offering. It makes heavy use of vector processing and SIMD instructions, and the vendor describes its basis in vector processing as the key to its offering’s achieving query performance at scale."

"As generative solutions mature in the enterprise, the need for a real-time retrieval engine that can power RAG for structured, unstructured, and graph data will be critical. We present an alternative to node2vec, a new and innovative approach for flattening knowledge graph features into a 1-dimensional vector space (embedding model), so popular vector similarity search algos like cosine similarity can be executed on knowledge graph nodes."

"As a member of the Nvidia AI partner ecosystem, Kinetica is excited to be recognized as a first-mover in the AI Application Category, for deploying NIM to power generative AI inference. NIM provides containers to self-host GPU-accelerated inferencing microservices for pretrained and customized AI models across clouds, data centers, and workstations. Upon deployment with a single command, NIM microservices expose industry-standard APIs for simple integration into AI applications, development frameworks, and workflows."

"Kinetica got its start building a GPU-powered database to serve fast SQL queries and visualizations for US government and military clients. But with a pair of announcements at Nvidia’s GTC show last week, the company is showing it’s prepared for the coming wave of generative AI applications, particularly those utilizing retrieval augmented generation (RAG) techniques to tap unique data sources."

" At NVIDIA GTC Kinetica unveiled the industry’s only real-time vector similarity search engine that can ingest vector embeddings 5X faster than the previous market leader, based on the popular VectorDBBench benchmark."

"Kinetica DB Inc., which sells a real-time analytics database for time-series and spatial workloads, took to the stage at Nvidia Corp.’s GTC conference today to unveil a new generative artificial intelligence tool for enterprise customers that delivers big advantages for retrieval-augmented generation workloads."

"Apache Lucene, Datastax, Faiss, Kinetica, Milvus, Redis, and Weaviate are among the vector search providers working with NVIDIA NeMo Retriever microservices to power responsive RAG capabilities for enterprises."

"We are thrilled to announce that Kinetica has now joined the Connect with Confluent Partner program. This collaboration merges the unparalleled speed of Kinetica’s GPU-accelerated database with the data streaming capabilities of Confluent Cloud, delivering insights on high-velocity data streams in mere seconds."

"Kinetica, the real-time database for analytics and generative AI, announced the availability of a Quick Start for deploying natural language to SQL on enterprise data. This Quick Start is for organizations that want to experience ad-hoc data analysis on real-time, structured data using an LLM that accurately and securely converts natural language to SQL and returns quick, conversational answers."

"One of the primary drivers behind the adoption of GPU database architectures is their remarkable speed and efficiency."

"Most of the stuff we see around LLMs today is low-speed data; it’s very static, and it hasn’t been updated. That’s something I think we’re going to see develop over the next 24 months."

"To harness the full potential of vehicle telematics data, a database should effectively capture and integrate both time-series and spatial elements"

"LLMs – the GPT series and others – are the driving force of not just individual-specific tasks but also massive business operations"

"English will replace SQL as the lingua-franca of business analysts"