On-page relevance

On-page SEO

What each page says, who it speaks to, and how it connects to the rest of the site. Technical work makes you accessible; on-page work makes you relevant to the actual question.

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What's included

Intent & page mapping

Every page gets one clear intent and a set of questions it answers. Without this you get cannibalization: two pages compete for the same term and neither wins.

Heading structure & passages

H1-H3 that describe the actual content, in self-contained blocks. A RAG system extracts passages, not pages — good structure decides which fragment can be cited.

Title & meta description

Titles that win the click in the SERP and describe the content honestly. They stay relevant in AI Overviews too, where the title is often the only brand text shown.

Internal linking

Hub-and-spoke architecture with descriptive anchor text — distributes authority and gives passage-level context for AI retrieval.

Why it matters

Technical work answers "can the bot read this page." On-page answers "does this page deserve to be cited for this question" — two different problems with different solutions.

The most common on-page problem I find isn't thin content, it's cannibalization: several pages saying the same thing and splitting their signals.

Frequently asked questions

About On-page SEO

Technical SEO handles infrastructure: whether bots can access, render, and index the site (crawling, rendering, speed, schema). On-page SEO handles each page's content: which search intent it covers, how the text is structured, its title/meta, how it's linked internally. Technical makes you accessible; on-page makes you relevant. You need both — a technically perfect site whose pages answer nothing stays uncited.

Cannibalization happens when two or more of your pages target the same search intent. Engines don't know which to show, so they split the signals between them and neither ranks well. The fix: map each page to a distinct intent, then consolidate (301 or canonical) the redundant pages into the strongest one.

RAG systems in AI engines don't extract whole pages, they extract passages. A descriptive H2 followed by a self-contained 2-4 sentence answer is a unit the model can lift and cite directly. A wall of unstructured text forces the model to guess where the answer starts and ends — and it usually picks another source instead.

Google rewrites them often, but not always — and when it keeps yours, that text decides the click. Meta descriptions are also used verbatim by other surfaces: share previews, aggregators, some AI interfaces. The cost of writing one well is small; the cost of letting an engine pick an unflattering fragment is a lost click.

Let's see where you stand.

A 15-minute discovery call. No pitch — I'll tell you directly if and how I can help with On-page SEO.