Vast Data announced yesterday the launch of its “VAST InsightEngine” solutions in collaboration with global company Nvidia. This is the first solution of its kind, capable of securely ingesting, processing, and retrieving all enterprise data (including files, structured data, tables, and data streams) in real-time.
The new solution resulting from the Vast Data and Nvidia partnership aims to accelerate business insights by aggregating real-time data streams, integrating them, and retrieving them, making enterprise data readily available for AI-driven decision-making. Commenting on the new collaboration, Jeff Denuorth, co-founder of Vast Data, stated, “Thanks to the unique architecture of the Vast Data platform, combined with NVIDIA NIM services, we make it simple for enterprises to extract insights from their data in real-time. By unifying all elements for AI retrieval processes at the foundation of enterprise data, the ‘VAST InsightEngine’ solution with Nvidia is the first of its kind in the sector, providing a comprehensive view of all structured and unstructured enterprise data to enable advanced AI-supported decision-making.”
On the other hand, Justin Puitano, Vice President of Enterprise Computing and AI at Nvidia, said,“Integrating Nvidia NIM into VAST InsightEngine with Nvidia helps enterprises more securely and efficiently access data at any scale to quickly convert it into actionable insights.”
The new VAST InsightEngine solution is expected to significantly advance real-time AI capabilities and can automate many processes that previously required lengthy data analysis times. The solution is scheduled for release later in 2025.
While hundreds of companies are focusing on training large language models, tens of thousands will work on implementing real-time data retrieval, creating new infrastructure demands that can classify and search both structured and unstructured datasets, along with new semantic methods, such as vector and knowledge graphs, with a need for speed, scalability, simplicity, and security.
