Vector SEO: How Embeddings Are Changing Discovery

Vector SEO is shorthand for optimising pages for retrieval by meaning rather than by exact keywords. Search systems convert text into embeddings, lists of numbers that place similar ideas close together, and fetch whatever sits nearest the question. You cannot tune a vector directly. What you can do is write clearly, cover a topic properly and structure pages so each section stands alone.

Quick links

• What is vector SEO?
• How do embeddings change what ranking means?
• Is vector SEO a real discipline or a rebrand?
• What does query fan-out have to do with it?
• What should you change on your site?
• How do you test whether a page reads clearly to a machine?
• FAQ

What is vector SEO?

Vector SEO is the practice of writing and structuring content so that systems matching on meaning, not just on words, retrieve it for the right questions. The name is informal. Nobody at Google publishes a “vector SEO” guideline, and I would be suspicious of anyone selling it as a product.

The underlying machinery is real, though, and worth understanding. Google’s own cloud documentation defines embeddings as high-dimensional numerical vectors that represent a piece of text, and vector search as a technique for comparing similar objects using them. The closer two items sit in that space, the more semantically similar they are.

In plain terms:

1. A model reads a passage and turns it into a long list of numbers.
2. The question gets the same treatment.
3. The system returns the passages whose numbers sit nearest the question’s.

That is the whole trick. “Cheap flights to Lisbon” and “budget airfare Portugal capital” share almost no words, and land in roughly the same neighbourhood.

How do embeddings change what ranking means?

They shift ranking from “does this page contain the phrase” towards “is this page about the thing being asked”. Keywords still matter as a signal, but they stop being the gate.

Classic keyword matching rewarded a page for repeating the right terms in the right places. Embedding-based retrieval has no interest in your keyword density. A page can use none of the searcher’s words and still be the closest match, and a page stuffed with them can sit miles away if what it says is vague.

Google says as much in its guide to optimising for generative AI features: AI systems can understand synonyms and general meanings of what someone is seeking, and it advises that you do not need to worry about missing long-tail variations. That is a polite way of saying the old exact-match habit has a shrinking return.

Two consequences follow, and both matter more than the maths:

• Vague pages lose. A page that gestures at a topic without saying anything specific produces a blurry embedding. It sits in the middle of nowhere and is nearest to no question in particular.
• Mixed pages lose. A page that covers four unrelated services averages out into something that matches none of them well.

Is vector SEO a real discipline or a rebrand?

Mostly a rebrand of good practice, with one genuinely new idea underneath. Be sceptical of anything that promises you can engineer your vectors.

The sceptical case is fair. You do not know which embedding model a given engine uses. Different engines use different ones, and they change. Any tactic that depends on the shape of a specific model’s output is guesswork that will age badly. Google’s guide also lists rewriting content specifically for AI systems and splitting it into small “chunks” as myths to ignore.

The new idea is simpler: retrieval now works on passages and meaning, so the quality of each section matters as much as the page’s overall authority. That is a change in emphasis, not a new religion. If you have read our piece on entity SEO, the two fit together. Entities tell a system who you are. Embeddings tell it what a given passage is about.

We take a plain line on this at Aeonix. Write for the reader, make every section answer something, and the machines mostly sort themselves out. The rest is hygiene.

What does query fan-out have to do with it?

Query fan-out is why topical depth matters. Google says AI Overviews and AI Mode may issue multiple related searches across subtopics to build one answer, which means a single question can pull in several sub-questions you never saw typed.

Take a buyer asking an AI assistant which CMS suits a ten-person marketing team. Behind that one prompt, the system may go looking for pricing models, editor experience, migration effort and how well each option handles search. Each of those is a separate retrieval, and each can surface a different source.

Your page does not need to rank for the headline question. It needs to be the closest passage for one of the sub-questions. A site with thin pages on the main topic and nothing on the surrounding questions has one chance. A site that covers the neighbourhood properly has several.

That is the practical meaning of topical depth: not more words, but more of the adjacent questions answered, each on its own clearly headed section. If you want the wider picture of how this plays out in Google’s newer interface, we cover it in our guide to optimising for LLM-driven search.

What should you change on your site?

Fix clarity first, then coverage, then structure. None of it is exotic, and most of it improves the page for human readers too.

Say one thing per section

Each H2 should answer one question, and the first sentence under it should answer it directly. This is the same answer-first habit we use on every post here. A passage that opens with the answer embeds cleanly and reads cleanly. A passage that opens with three sentences of throat-clearing does neither.

Use the words your buyers use, and the ones they do not

Embeddings handle synonyms well, so do not contort your copy around a single phrase. Do the opposite. Write the way a customer would describe their problem, and include the plain-English version alongside the industry term. “Headless CMS (a content system that is separate from your front end)” does more work than the term alone.

Cover the neighbouring questions

List the questions a buyer asks before, during and after the one you are targeting. Cost, risk, timescale, alternatives, what goes wrong. Then check which ones your site answers. Gaps here are where fan-out queries slip past you.

Do this with real pages, not a pile of near-duplicates. Google’s guide is explicit that mass-producing pages for query variations breaks its scaled content abuse policy. Depth means one good page per genuine question, not forty thin ones.

Be specific

Numbers, names, timescales, places, constraints. “We rebuilt the site in six weeks” has a sharper meaning than “we deliver quickly”. Specific passages embed distinctly, which is what you want when a system is looking for the nearest match to a specific question.

Keep the structure plain

Clear headings, short paragraphs, descriptive link text, content present in the HTML rather than injected late by scripts. Structured data still helps with rich results, but Google is clear that it is not a requirement for its AI features, and that there are no additional requirements to appear in AI Overviews or AI Mode.

Which, if you think about it, is the good news. There is no secret list. The pages that do well are the ones that were worth reading anyway.

How do you test whether a page reads clearly to a machine?

You cannot see the embedding, but you can test the behaviour it produces. A few cheap checks catch most problems.

1. Summarise the page in one sentence without looking at it. If you cannot, the page is probably doing too many jobs.
2. Read only the headings and first sentences. Do they tell the full story? If a stranger could not tell what each section answers, neither can a retrieval system.
3. Ask an AI assistant the sub-questions. Not the headline query, the neighbouring ones. See which sources get named and whether you appear anywhere.
4. Check Search Console. Google points site owners to the generative AI performance report there to see how content appears in AI features.
5. Look for sections that wander. A paragraph that starts on pricing and ends on team culture is two passages stuck together.

None of this needs a specialist tool. It needs an hour and a willingness to find out that a few of your favourite pages say less than you thought. Our AI search audit checklist runs through the technical side, and our work on AI website optimisation covers what to do once you have the list.

Vector SEO is a useful name for something sensible. Write clearly, answer the surrounding questions, and keep sections honest about what they cover. Treat anyone offering to optimise your embeddings directly with the same warmth you would give a man selling magic beans.