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AI & Search

Semantic Search

Portrait of Lukas Horvath, co-founder of Roelu Studio
Lukas HorvathCo-founder

What is Semantic Search?

Semantic search is a search technique that ranks results by meaning rather than keyword overlap. It converts both the query and the indexed content into numerical vectors, then returns whatever sits closest in vector space. Unlike traditional keyword search, which only matches strings, semantic search understands that 'how to make my site faster' and 'page speed optimization' are the same question.

Why it matters

Keyword SEO trained a generation of marketers to write awkward sentences stuffed with exact-match phrases. Semantic search makes that work obsolete. Google's algorithm is now semantic. AI search is entirely semantic. The pages that win are the ones that answer the actual question clearly, in plain language, with structure a model can parse. Writing for synonyms and surrounding concepts beats writing for one keyword. The buyer who searches 'why is my CMS so painful' should land on your headless CMS page, even if those exact words never appear.

How it works

Every page in the index is run through an embedding model that turns the text into a high-dimensional vector — essentially a long list of numbers that represents meaning. When a user searches, their query becomes a vector too, and the engine finds the closest vectors using cosine similarity or a similar math operation. Modern search stacks combine semantic and keyword results in a hybrid setup, because pure semantic sometimes misses on exact product names or part numbers. The output ranks pages by relevance to intent, not by how often the search term appears in the text. Sites that write clearly and structure information by topic show up across a much wider range of queries than they would have under keyword-only matching.

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