Artificial intelligence (AI) processing rests on the use of vectorised data. In other words, AI turns real-world information into data that can be used to gain insight, searched for and manipulated.

Vector databases don’t just store your data. They find the most meaningful connections within it, driving insights and decisions at scale. A vector database is just like any other database in that it ...

Even though traditional databases now support vector types, vector-native databases have the edge for AI development. Here’s how to choose. AI is turning the idea of a database on its head.

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

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Did you know that over 80% of the data generated today is unstructured? Traditional databases often fall short in managing this type of data efficiently. That’s where vector databases come into play.

Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...

Generative AI is revolutionizing data and analytics, but its applications demand advanced data management capabilities to handle vast, diverse, and complex datasets that include images, video, audio, ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination" — the generation of ...

This expansion is fueled by the rapid adoption of AI, LLMs, and multimodal applications that require high-performance vector search, scalable indexing, and real-time retrieval. By offering, the ...