Google’s understanding of how users search for information. Semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. These capabilities are underpinned by both Google’s foundation models and a variety of Google Search technologies, including: With the ability to ingest large volumes of documents and support for both unstructured and structured data, apps built with Gen App Builder help customers solve the long-standing headache of finding relevant information across the organization, turning tasks that used to take hours into quick searches or conversational explorations with an app. Gen App Builder’s out-of-box capabilities can remove the need for data chunking, generating embeddings, or managing indexes, hiding complexity behind a straightforward interface that lets developers build apps in minutes with little or no coding and no prior machine learning experience. All of this is available as a fully managed service, so developers can focus on building rather than cloud complexity. Multi-turn conversations are supported so that users can ask follow up questions as they peruse outputs, and customers have control over their data-including the ability to support HIPAA compliance for healthcare use cases. The intersection of generative AI and enterprise dataĮnterprise Search on Gen App Builder lets developers create search engines that help ground outputs in specific data sources for accuracy and relevance, can handle multimodal data such as images, and include controls over how answer summaries are generated.
Let’s look at how Enterprise Search on Gen App Builder helps customers bypass these scale and reliability challenges, so they can start leveraging generative search quickly.
And they generally need the process to be high-performant and scalable out of the box, making the functionality easy to use even if the organization lacks data science and machine learning expertise. They also likely need the ability to ground model outputs in specific data, so that outputs are more relevant and less likely to include mistakes or “ hallucinations.” They generally need safeguards that protect their data, how it is accessed, and how it is used.
These challenges demonstrate that to effectively implement generative search, organizations typically need more than access to powerful foundation models. Even when organizations make these efforts, the resulting solutions still tend to lack feature completeness and reliability, with significant investments of time and resources required to achieve high-quality results. Bespoke, do-it-yourself approaches are generally no easier, with production-grade solutions often requiring complex tasks like integrating embeddings data with foundation models and significant use case data testing. Likewise, many solutions are limited in the data types they can handle, prone to errors, and susceptible to data leakage. For example, building search by breaking long documents into chunks and feeding each segment into an AI assistant typically isn’t scalable and doesn’t effectively provide insights across multiple sources.
To date, most approaches to combining generative AI and search technology have been inadequate for the scale and reliability needed for enterprise use. Customers can combine their internal data with the power of Google’s search technologies and generative foundation models, delivering relevant, personalized search experiences for enterprise applications or consumer-facing websites. In generative AI, these leaders see an opportunity to leverage their data more effectively and deeply, including applying it to conversational apps that can answer complex questions, produce accurate summaries that synthesize many sources, and help people get the information they need, faster.Įnterprise Search on Generative AI App Builder (Gen App Builder) lets organizations create custom chatbots and semantic search applications in as little as a few minutes, with minimal coding needed to get started and enterprise-grade management and security built in.
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Leaders at enterprises know the limits of traditional enterprise search, with queries producing a list of links based on pattern matching, and significant manual investigation required to find the more relevant answers. In our conversations with customers, few generative AI use cases have driven as much enthusiasm as generative search.
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