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Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the ...

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

Open-source vector database startup Qdrant Solutions GmbH today announced it has raised $50 million in early-stage funding to pave the way for smarter and more reactive artificial intelligence apps.

‘We work with [customers’ file data] to accelerate the process of learning and help avoid hallucination. We are targeting on-prem data. So this is not designed to go search the web or anything. That’s ...

However, when it comes to adding generative AI capabilities to enterprise applications, we usually find that something is missing—the generative AI programs simply don't have the context to interact ...

Latest Graphwise offering bridges the gap between complex enterprise data and functional AI agents, using ontologies reduces inaccurate answers 2X in benchmarks Equally important, the company ...

Organisations should build their own generative artificial intelligence-based (GenAI-based) on retrieval augmented generation (RAG) with open source products such as DeepSeek and Llama. This is ...

Teradata’s partnership with Nvidia will allow developers to fine-tune NeMo Retriever microservices with custom models to build document ingestion and RAG applications. Teradata is adding vector ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...