Jina AI redefining semantic, multimodal search – News

As artificial intelligence accelerates across every industry, Jina AI is emerging as one of the few platforms capable of redefining how organizations actually find, interpret and act on their data.

Jina AI is moving search far beyond keyword matching, pushing it into a semantic, multimodal, long-context world that mirrors how people think. The result is faster, more intuitive access to sprawling unstructured data — all powered by an open-source, highly scalable architecture built for the next wave of data-intelligent applications, according to Han Xiao (pictured), vice president of AI at Elasticsearch B.V.

“Jina AI was founded in 2020,” Xiao said. “Our only goal is to build a world-class search model. We call them the search foundation models. That particularly includes the embeddings, rerankers and the small language models that people can use to build better search systems, high quality search systems and high relevance search systems. Over the last five years, we have been extensively working on building the world-class model, making sure that they work on multilingual, multimodal data.”

Xiao spoke with Rob Strechay at AWS re:Invent, during an exclusive broadcast on theCUBE, News Media’s livestreaming studio. They discussed how Jina AI is transforming search and data retrieval through semantic and multimodal intelligence. (* Disclosure below.)

Elastic Inference Service and Jina AI redefining intelligent search

The rapid evolution of AI is transforming how developers build, search and extract value from data. By integrating Elastic Inference Service with Jina AI, Elastic is advancing into a new era of intelligent, scalable and developer-friendly search — shifting from keywords to semantic, multimodal and AI-native retrieval, according to Xiao.

“Elastic Inference Service is right now the default inference service behind all the Elasticsearch system,” he said. “Developers can have [a] very handy experience and access all the top models from Jina AI, but also ELSER will stay open. What we want to provide is the best developer experience for all the business to make sure that they have the immediate access every time there’s a new embedding model, new reranker model, new small language model that can be used to build the search system.”

Elasticsearch stands out as a globally trusted platform for production-ready search and analytics by unifying high-performance indexing, real-time analytics, large-scale scalability and enterprise reliability. Its support for embedding models and vector databases enables semantic and multimodal search across text and images, significantly improving the accuracy and relevance of data retrieval, Xiao pointed out.

“One of the observations that we look at is when people try to build a very high quality search system, they typically need a lot of building blocks and they also need an orchestration layer, which basically connects all the dots together,” he said. “Elastic, and in particular the Elasticsearch, is one of the most downloaded and usable frameworks for developers and businesses to build production-ready system, search systems. We want to be the computational layer behind this search system.”

Here’s the complete video interview, part of News’s and theCUBE’s coverage of AWS re:Invent: 

(* Disclosure: Elastic sponsored this segment of theCUBE. Neither Elastic nor other sponsors have editorial control over content on theCUBE or News.)

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