Relevance engineering · Engineering page relevance
Relevance engineering is a discipline popularized by Mike King (iPullRank) that treats search visibility as an engineering problem: measuring a page’s relevance to a query, notably with embeddings and cosine similarity. It combines information retrieval, AI, content, UX and digital PR.
Standalone definition · 41 words
Where does the term come from?
Mike King, founder of iPullRank, put it forward at SEO Week and in his agency’s AI Search Manual, presented as the framework for visibility in conversational search. His press release states that Search Engine Land named him AI Search Marketer of the Year 2025.
How does it work?
Topics, pages and queries are represented as vectors, and relevance is measured by their closeness. A central vector is computed for each topic, then a score for each page, and these scores guide what to write, what to cut, and how to group and link pages. DEJAN describes the same logic and ties it to AI visibility.
What are the limits?
It is a measurement framework, not a published ranking rule: a given engine’s embeddings are not accessible. Google also reminds that its systems understand relevance without exact word matches, which argues for substance over surface optimization.
Frequently asked questions
Does relevance engineering replace SEO?
It extends SEO with information retrieval methods and vector measurement. It does not set aside crawling, indexing or authority.
Do you need specific tools?
An embedding model and a similarity calculation are enough for a first audit. Tools such as iPullRank’s Qforia help simulate query fan-out.
Related terms
Further reading
- Profound vs Otterly vs Ahrefs Brand Radar: query fan-out comparison
- Search Central Live Deep Dive Barcelona 2026: what Google said over the 3 days
- ← Back to the GEO glossary (114 terms)