Semantic Search

Classic search matched query strings to document strings, rewarding exact keywords. Semantic search encodes both query and documents as embeddings and retrieves by vector similarity, so "tools to see if AI mentions my company" finds a page titled "brand monitoring for ChatGPT" without sharing a single keyword.

Production systems are usually hybrid, combining semantic similarity with keyword signals and authority, then applying reranking for final ordering. But the semantic layer decides the candidate pool: if your content is not conceptually near the query, nothing downstream can save it.

AI engines lean on semantic retrieval because conversational queries are long, varied, and rarely match anyone's target keywords. One buying intent fragments into hundreds of phrasings, and semantic search collapses them onto the same content, if that content expresses the intent clearly.

The practical shift: stop optimizing pages for a keyword and start optimizing them for an intent. A page that genuinely resolves "how do I choose X for situation Y" gets retrieved across every paraphrase of that situation.

Cover intents exhaustively with specific sections: use cases, comparisons, constraints, prices, alternatives, each as a self-contained passage. Write in the language of the problem, not just your product's vocabulary, so buyer phrasings land near your vectors. Topical breadth across a cluster of pages widens your semantic surface area.

Validate against reality: track real conversational prompts in your category and see which engines retrieve and cite you. Geonimo's daily prompt tracking across engines shows where semantic retrieval finds your content and where competitors occupy the intent instead.