News room

AI Leadership Insights: Real-Time Analytics

Generative AI plays two parts in the real-time space. One is acting as a co-pilot for users, where it unlocks a whole new level of efficiency and insight for a much broader set of users. The second part of that is it’s going to change the compute requirements of the analytic ecosystem...
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Reengineering Data Engineering for GenAI

GenAI blew up the pressure on data engineers, making it "untenable" for them to build pipelines for a growing number of queries. That's when Kinetica saw a significant opportunity and seized it.
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The GenAI Data Developer Experience: Performance Optimization

Kinetica leverages a concept called retrieval-augmented generation (RAG) to interpret a natural language request whose subjects, verbs and objects can be mapped to symbols in a tabular, time-series, or geospatial database.
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Mahindra Formula E Racing Selects Kinetica for Lightning-Fast Insights

In the fiercely competitive world of international motorsport, where every millisecond counts, Kinetica has been chosen by ...
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The GenAI Data Developer Experience: Performance Optimization

When you pair large language models with database engines geared for this new world of servers, the need for performance pre-optimization vanishes.
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Reengineering Data Engineering for GenAI

Kinetica did more than position its database for GenAI data engineering. It also integrated a ChatGPT-like interface to its vector database, natively handling queries in natural language and converting them to SQL on the fly.
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AI model training rekindles interest in on-premises infrastructure

The folks we’re talking to are fixated on having complete control,
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Where in the World? The Reality of Geospatial Data

Join host Eric Kavanagh as he delves into the fascinating world of geospatial data. Eric is joined by two esteemed experts in the field: Eugene Burke, a data strategy leader, and Nima Negahban, the CEO of Kinetica.
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Kinetica Integrates with Native LLM for Accessible, Secure, and Optimized SQL Queries

Public LLMs—such as Open AI’s GPT-3.5—introduce a variety of privacy and security concerns for enterprises dealing with highly sensitive data. Kinetica’s native LLM, dubbed SQL-GPT, is engineered to keep data within the customer’s environment and network perimeter
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Kinetica Boosts Analytical Database With Native LLM

Kinetica continues to expand the artificial intelligence capabilities of its high-performance analytics database, unveiling a new native large language model this week that the company says allows users to perform rapid, ad-hoc data analysis on real-time, structured data using natural language.
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Kinetica’s Native Large Language Model Enables Organizations To Run SQL-GPT With Enhanced Privacy

In addition to being more secure, Kinetica’s native LLM is tailored to its syntax and industry data definitions such as telecommunications, financial services, automotive, logistics and others, creating more reliable and accurate SQL generation
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Generative AI to Make Launch SQL Queries Simpler

The need to optimize the processing of those queries requires a database that was designed from the ground up to make the most efficient use of costly graphical processor units (GPUs)
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Kinetica Launches Native LLM for Language-to-SQL on Enterprise Data

The U.S. Air Force has been working with Kinetica, leveraging advanced analytics on sensor data to quickly identify and respond to potential threats, helping to keep the skies safe and secure for all users of the national airspace system.
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Kinetica offers its own LLM for SQL queries, citing security, privacy concerns

Kinetica's self-developed large language model, designed to enable SQL queries from natural language prompts in its relational database, targets security-sensitive customers including US government defense organizations.
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Location Data Profusion Driving Cutting-Edge Innovation

This unique feature ensures that users can seamlessly interact with geospatial data, making complex spatial data queries more accessible and efficient.
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