These slides are from a presentation given at the International Search Summit Barcelona 2025. Vector embeddings are gaining traction across various applications and play a crucial role in Google's algorithm. While they are widely known for their impact on semantic search, they also offer powerful opportunities for automating SEO tasks. By leveraging cosine similarity, a metric that measures differences between embeddings, you can optimize internal linking and streamline redirect mapping with precision. Vector embeddings are not limited to one language, making it easier to cross international borders. Beyond that, embeddings can be stored in a vector database, which can be integrated with an LLM. This setup helps limit hallucinations, allows you to inject your own data effortlessly, and keeps you ahead of the curve as models evolve. By taking advantage of these opportunities, you can better predict Google's behavior and take your SEO automation to the next level for international SEO.
