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Conversational Query

A more intuitive and interactive way of analyzing enterprise data
ChatGPT Converts natural language to ad-hoc queries
Kinetica Gives blazing fast responses to ad-hoc queries without the need to pre-engineer data in advance
Users can ask any question of their proprietary data, even complex ones that were not previously known, and receive an answer in seconds.  Together, ChatGPT and Kinetica remove the limits of data exploration and unlock the full potential of an organization’s data.
Kinetica is one of the first database companies to integrate ChatGPT or generative AI features within a database

Get Rapid Responses to Unknown Questions

Kinetica is designed from the ground up to leverage the vectorization capabilities of GPUs and modern CPUs to answer complex queries. Vectorization unleashes significant performance gains – particularly for ad-hoc queries that may result in table scans and multi-way joins that often cripple other databases.

Vectorization in Kinetica »

than Databricks 9.1 LTS (Photon)
Benchmark Suite
than ClickHouse 21
Indepedently Benchmarked
While most analytic databases require data engineering, indexing and tuning to ensure rapid querying, Kinetica delivers similar performance through native vectorization

Ask a Variety of Questions

Kinetica is a multi-genre database enabling you to meet a variety of use-cases on a single system.

Fast Relational

At it's core, Kinetica manages data in a columnar relational structure with tables and columns. It's easy to query data with SQL and connect common visualization tools.


Data can be accessed as a key-value structure to support high concurrency, low latency applications and simplify the operationalization of insights at web scale.


Many geospatial use-cases involve figuring out optimal routing and solving network topology. Graph functionality in Kinetica makes it easy to work with spatial relationships.


Kinetica natively works with points, shapes as WKT, tracks, and labels. There are over 130 geospatial functions that are available through SQL or the REST API.


Kinetica is ideal for time-series and real-time data feeds, and supports windowing to apply aggregate and ranking functions over a period of time, and keep the picture updated as data evolves

Machine Learning

Leverage high-speed model inference and feature generation by running popular ML frameworks containerized in Kinetica.
Kinetica converges multiple modes of analytics such as time series, spatial, graph, and machine learning that broadens the types of questions that can be answered.

Get answers to what is happening 'now'

Kinetica provides the lowest possible latency from the time raw data is created until an answer can be returned to an ad-hoc query This allows users to leverage generative AI to see what's happening in the moment.

Real-time Analytics on Kinetica »

With its ability to convert natural language questions into SQL queries, the addition of ChatGPT to the high-performance, real-time Kinetica database makes it possible for business users to obtain analytical insights on the fly.

Try SQL-GPT, Free on Kinetica Cloud

Kinetica's conversational query is now available. Try it with your own data, or with pre-loaded datasets and easy to follow SQL workbooks that make it easy to get started using generative AI on enterprise data sets.

Kinetica in the Cloud   Kinetica on your Laptop

Experience Conversational Query

ChatGPT plus Kinetica provides an easy and intuitive mechanism for business users (and data analysts) to query and explore data. Kinetica's native vectorized algorithms work with ChatGPT to produce fast results and expressive insights into constantly changing and shifting datasets

While LLMs like ChatGPT can convert natural language to SQL, the speed of response for data analytics questions is still dependent on the underlying data platforms being used.

Conventional analytic databases require extensive data engineering, indexing and tuning to enable fast queries, which means the questions must be known in advance. If the questions are not known in advance, a query may take hours to run or not complete at all.  

In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in radically faster query execution on a smaller compute footprint. Vectorization is made possible by GPUs and the latest advancements in CPUs, which perform simultaneous calculations on multiple data elements, greatly accelerating computation-intensive tasks by allowing them to be processed in parallel across multiple cores or threads.

Kinetica vectorized columnar architecture enables convergence of multiple modes of analytics such as time series, spatial and graph that broadens the types of questions that can easily be answered, such as, "How can we improve the customer experience considering factors such as seasonality, service locations and relationships?"

Kinetica is able to ingest massive amounts of streaming data in real-time to ensure answers represent the most up to date information, such as, “What is the real-time status of our inventory levels and should we reroute active delivery vehicles to reduce the chances of products being out of stock?

There is no additional cost for the ChatGTP integration in Kinetica. Users can experience this feature for free on Kinetica Cloud or Dev Edition.

Kinetica uses the “gpt-3.5-turbo” model

Book a Demo!

The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.

Contact us, and we'll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.