Structured and semantic processing of content
SitecoreAI's search module has the capability to process and organize large volumes of content. Through its API or content connectors, it can uniformly structure and semantically enrich content scattered across multiple platforms (such as official websites, blogs, communities, etc.). By leveraging Schema.org markup (e.g., FAQ, HowTo, Product, etc.), it creates AI-friendly "knowledge units."
Building an authoritative knowledge graph
By leveraging SitecoreAI's data management capabilities, it associates product data, customer success stories, whitepapers, certification information, and other content to construct a professional domain knowledge graph centered around the brand. This enables SitecoreAI's search module to gain deeper insights into content entities and their relationships, while providing AI with rich, interconnected contextual information - enhancing authority and the likelihood of being cited.
Intent recognition and query reformulation
SitecoreAI's machine learning models excel at understanding user search intent. This capability can be used to predict and analyze the long-tail questions, comparative queries, and recommendation requests that generative AI users are likely to ask. This enables targeted content generation and optimization to align with the AI's "chain of thought" reasoning process.
Personalization and contextual adaptation
Although generative AI is publicly accessible, its responses may vary based on user context. SitecoreAI's user profiling and contextualization capabilities can help identify the key concerns of different user groups (such as industry sectors and professional identities), guiding the creation of content that is more likely to be cited by AI while adapting to diverse contextual scenarios.
