A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

Learn why Google’s TurboQuant may mark a major shift in search, from indexing speed to AI-driven relevance and content discovery.

BERLIN & NEW YORK--(BUSINESS WIRE)--Qdrant, the open-source vector search engine built in Rust for production workloads, today announced $50 million in Series B funding led by AVP, with participation ...

Qdrant, a leading provider of high-performance, open-source vector search, is offering a private beta of Qdrant Edge, a lightweight, embedded vector search engine designed for AI systems on devices ...

Google (GOOG)(GOOGL) revealed a set of new algorithms today designed to reduce the amount of memory needed to run large language models and vector search engines. Shares of major memory and storage ...

Qdrant, the open-source vector search engine built in Rust for production workloads, announced it has secured $50 million in Series B funding will enable composable vector search as core ...

Kioxia Corporation today announced the successful demonstration of achieving high-dimensional vector search scaling to 4.8 billion vectors on a single server with its open-source KIOXIA AiSAQ(TM) ...

Wikidata has built the semantic web backbone supporting knowledge cards in popular engines. Now, it's extending this foundation using a vector database to enhance its existing knowledge graph and ...

Information retrieval systems are designed to satisfy a user. To make a user happy with the quality of their recall. It’s important we understand that. Every system and its inputs and outputs are ...