While the internet existed long before web search came along, its emergence and subsequent advancements have dictated the internet’s popularity and pace of innovation. Search is integral to today’s digital world, Elastic is expanding its frontiers for the enterprise with search AI-powered.

“One of the things that we’ve seen is that search has evolved from lexical search or text-based search into semantic search, which is customers wanting to do natural language question and answering to conversational search,” said Ken Exner (pictured), chief product officer of Elastic. “[They’re] moving towards generative AI applications that use search in order to ground LLMs and use the power of LLMs to build search-powered applications.”

Exner spoke with theCUBE Research’s Dave Vellante for theCUBE’s “Cloud AWS re:Invent Coverage,” during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed search AI, expanding on its potential to eliminate existing bottlenecks and revolutionize the internet. (* Disclosure below.)

Elastic’s implementation of search AI in detail

Elastic began as a search engine more than a decade ago, but its capabilities have expanded significantly. The company has since evolved from traditional lexical search to semantic search, enabling natural language queries and conversational applications, according to Exner.

“At Elastic, we’re thrilled to help customers figure out how to use their private data to power these generative AI applications,” he said. “One of the things that we do is we help ground LLMs, using retrieval augmented generation, on companies’ private data. So, for a lot of companies that already use Elastic, this becomes a very simple thing for them.”

The past two years have been transformative for gen AI adoption, and 2024 has been a year of experimentation, with enterprises exploring how to integrate generative AI into their workflows, according to Exner. Scenarios such as customer service automation and internal search capabilities are laying the groundwork for more expansive use cases.

“What we saw this year in 2024 is companies starting to take some of those ideas and budgets and start building their first generative AI applications, starting with an experiment,” Exner said. “Something internal or something like a customer service application or an internal workplace search application and starting to get their first forays into generative AI. What we expect going forward is to see this move across the enterprise and customers to start finding other use cases.”

The Elastic AI Ecosystem was launched as a response to the perceived complexity of building gen AI applications. It’s a curated set of integrations and tools that harness Elastic’s expansive set of cloud partnerships. By providing prebuilt integrations, Elastic enables developers to quickly deploy RAG applications and semantic search solutions, according to Exner.

“We have spent a lot of time with the three CSPs integrating with their models and their tools,” he said. “We are integrated, for example, into Vertex AI from Google, and we’re integrated into Azure OpenAI studio from Microsoft, and you can use us as a vector database within those toolsets. Similarly, we have been integrating LLMs and other tools into our ecosystem and we’ve been doing all of this work over the last two years.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s “Cloud AWS re:Invent Coverage”:

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

Photo: SiliconANGLE