Relevance engineering

Relevance engineering is the practice of building the signals that make your brand and content read as relevant and authoritative to search engines and LLMs, so you get surfaced, cited, and recommended. The phrase was first used by the iPullRank team and Michael King. It reframes SEO work around brand and authority instead of keywords and links.

As Kevin puts it, the discipline is moving toward a "new SEO" that sits closer to brand marketing, where success is measured by citations, brand mentions, and influence.

Google and LLMs increasingly judge you on brand demand and authority, which makes engineering relevance the real job. Google evaluates sites heavily on brand search volume and user signals, so differentiated brands with real demand get more room from algorithm updates while undifferentiated ones struggle. On AI surfaces, the goal shifts from earning clicks to getting your data ingested with enough authority that the model recommends you.

Say a mid-market B2B SaaS company has strong rankings but weak brand demand. Its pages get crawled but rarely recommended in AI answers. It moves budget from publishing more articles to earning third-party authority and lifting branded search 25% over 2 quarters, and its citation rate across tracked prompts rises alongside it.

Shift how you measure success: Add citations, brand mentions, and conversion influence next to traffic rather than replacing your old metrics, so leadership can follow the change. Invest in what engines and models reward, brand demand, third-party authority, and differentiated content competitors can't cheaply copy. For agentic and AI surfaces, engineer your data for ingestion with clean feeds and structured product data. Pitch the reframe internally as a move from performance marketing toward brand, which is where richer budgets tend to sit.