AI-Powered Enterprise Search: Revolutionizing Data Insights and Business Operations

AI-powered enterprise search: Transforming data insights - SiliconANGLE

Glean's approach to smarter systems: AI, inferencing and enterprise data As businesses grapple with the complexities of artificial intelligence and modern data environments, AI-powered enterprise search has become essential for finding, understanding and acting on critical knowledge. This advanced technology is transforming how organizations manage information by making search processes more intuitive, efficient and capable of meeting evolving organizational demands, according to Arvind Jain, chief executive officer of Glean Technologies Inc. "AI is going to be a core capability of every product that you buy in the future. I mean, it's as simple as that. We will expect every product that we buy to be smart," Jain said. Jain spoke with theCUBE Research's John Furrier for theCUBE's "Cloud AWS re:Invent Coverage," during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. Jain shared insights into how AI and inferencing are reshaping enterprise search and enabling organizations to build smarter systems with deeper data connections. Modern enterprise search extends far beyond merely retrieving data. Instead, it integrates inferencing capabilities with enterprise systems to provide actionable insights. Glean has built a platform that connects enterprise data in a secure, meaningful way, creating opportunities for smarter applications, Jain explained. "We didn't actually set out to build an AI application. We were first solving the problem of people can't find anything in their work lives. We built a search product and we were able to use inferencing as a core part of our overall product technology," he said. "That has allowed us to build a much better search and question-and-answering product ... we're [now] able to answer their questions using all of their enterprise knowledge." These advancements leverage AI to simplify complex workflows, transforming traditional search into a tool for intelligent data discovery and problem-solving. By combining secure data connections with AI capabilities, Glean enables enterprises to improve efficiency while maintaining control over their knowledge ecosystem. The platform's AI-powered enterprise search function ensures that users can retrieve not just data, but meaningful answers. "You'll run Glean within your AWS instance within your VPC, and what you're doing is you're connecting Glean with all of your enterprise data and knowledge," Jain said. "Now you have this horizontal AI data layer where all of their enterprise data has been brought together." Looking ahead, AI-powered solutions are not just enhancing enterprise search but are also democratizing application development. These tools empower both technical and non-technical users to create sophisticated applications with ease using natural language, according to Jain. "You have to think about it in the new world; you're making everybody a developer ... AI is doing one really good thing, which is it is giving the power to a business user who doesn't know how to code. You're allowing them to build complex applications, do complex data analysis by just expressing those things in English." he added. Here's the complete video interview, part of SiliconANGLE's and theCUBE's "Cloud AWS re:Invent Coverage":

Elastic supercharges enterprise operations with AI-powered search - SiliconANGLE

AI-powered search: Elasticsearch's vision for the future of enterprise innovation 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, and Elasticsearch B.V. is expanding its frontiers for the enterprise with AI-powered search. "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 Elasticsearch. "[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 AI-powered search, expanding on its potential to eliminate existing bottlenecks and revolutionize the internet. (* Disclosure below.) 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":

Elastic's AI-powered data search: Unlocking business insights - SiliconANGLE

Elasticsearch's AI vision: Unlocking business value from unstructured data Advances in artificial intelligence are reshaping how enterprises leverage their data, with AI-powered data search positioning Elasticsearch B.V. as a front-runner in this movement. By enabling organizations to unlock insights from unstructured data through its innovative AI-powered search and security solutions, Elasticsearch is raising the bar for efficiency and scalability in enterprise applications, according to Ash Kulkarni (pictured), chief executive officer of Elasticsearch. "Large language models are enabling that value extraction," he said. "We are seeing customers go from traditional search -- which was textual, lexical search -- to semantic search. That's the first step, right? Then, people are going from there to now saying, 'Now that I can ask these questions, can I turn this into a conversational application so I can automate some of the queries and so on?' This is the business version of ChatGPT." Kulkarni spoke with theCUBE Research's John Furrier for theCUBE's "Cloud AWS re:Invent coverage," during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how Elasticsearch leverages AI-powered data search to transform enterprise data management, from semantic search to advanced automation in business processes and cybersecurity. Elastic's serverless vector database is a game changer for enterprises managing unstructured data, offering unprecedented scalability and efficiency, according to Kulkarni. The database integrates with Amazon Web Services Inc., making AI-powered data search and semantic capabilities accessible to a broader range of users. This shift empowers businesses to derive actionable insights without needing extensive technical expertise. "[For] the semantic search, the great part about it is in the past, when you searched for information, you had to be precise in your questions; otherwise, you wouldn't get matches," Kulkarni said. "Now you can search for the concept, you can search contextually, and you can get amazing answers. And that's how human beings respond. That's effectively semantic search. Now the machine can do that, which is so powerful." Elastic's advancements extend beyond semantic search to retrieval-augmented generation and agentic workflows. These tools enable automation of complex business processes, reducing the need for manual effort and increasing productivity, according to Kulkarni. "Now I can automate things that in the past involved human beings reading through documents and forwarding the analysis onto the next person in the chain," he said. "That journey is happening today, and I expect that people are going to go through that curve, going from search to semantic, to RAG [and] then agentic workflows." Elastic has also demonstrated a commitment to privacy, governance and scalability through innovations such as better binary quantization. This technique reduces memory requirements for vector embeddings by 32 times, making AI solutions more cost-effective and efficient, according to Kulkarni. "Even today, the cost of inferencing is pretty high," he said. "We are constantly making our vector database more and more efficient. Just two weeks ago, we released a capability called better binary quantization. [It] represents an entire vector embedding in a single bit without compromising on the accuracy of the results that you can get. It's a very advanced algorithm [and] we are the first in the industry to be out with it." In the security domain, Elastic's AI-driven tools, powered by AI-powered data search, are bridging gaps in cybersecurity expertise. By automating complex threat detection and analysis, Elastic equips new security professionals with capabilities that would otherwise take years to develop, Kulkarni explained. "[We've applied] that to the other domains that we play in, observability and security," he said. "In security, we applied those same concepts to create a functionality called Attack Discovery, and that basically gives a security operations center analyst ... instead of just dealing with alerts, it turns all of those alerts, correlates them and shows you the actual attacks that are going on in their environment." Here's the complete video interview, part of SiliconANGLE's and theCUBE's "Cloud AWS re:Invent coverage":