Chatbots do not select content based on isolated words. Instead, they transform meanings and concepts into mathematical vectors. This allows them to perform rapid comparison and information retrieval based on meaning, not just keywords.
To get cited, your content must be mathematically aligned with the user's intent. It is no longer about matching exact keywords. It is about minimizing the semantic distance between the user's query and your text.
Major AI platforms use state-of-the-art models to transform text into vectors. We process your content to vectorize it with maximum precision, calculating the exact semantic relevance between your text and a specific prompt.
We provide a semantic score from 0 to 1, where 0 is unrelated and 1 is identical. Higher semantic proximity significantly increases the likelihood that a chatbot will recognize the connection and link the prompt to your text.
Not purely by score. While high proximity is the foundation, the model's final selection is also influenced by other factors such as domain trust, technical health, and the semantic scope of the topic
Talk to an expertWe analyze all your content at scale and suggest new versions with higher semantic proximity.
