Vector embeddings turn text into numbers that describe its meaning. In SEO, they help compare queries, pages and passages beyond exact keyword matches. You can use them to find related pages, spot content overlap and improve internal links. They don't give you Google's ranking formula. A high similarity score means related text, not a better answer or a new customer.
I'm Brenden House, founder of Searchmaxxed. We do SEO and AI search optimisation. For marketing leaders, the useful question is what this changes on your website, rather than which mathematical model sounds most impressive.
In summary
- Embeddings describe relationships between pieces of text. They can match different wording about the same subject.
- Cosine similarity compares vector direction. It measures similarity within a particular model, not your chance of ranking.
- A useful passage answers a specific question and keeps its conditions beside the answer.
- Use embedding tools to suggest work. Check the intent, product facts and search results before changing pages.
- Measure search visibility, enquiries and pipeline after the work. A tool score proves little.
What are vector embeddings, and why do they matter for SEO?
A vector embedding is a list of numbers that represents features of an input, such as the meaning of a paragraph or query. Software can compare those numbers.
OpenAI's embeddings documentation describes text embeddings as a measure of relatedness. Search and clustering are among its uses. Clustering means grouping similar items, such as customer questions that belong on the same page.
Think about enterprise software. A buyer searches for “stop former employees accessing company systems”, while your product page describes “automated employee offboarding”. Different words, related problem. An embedding model can recognise that relationship without requiring the sentences to share every word.
That's useful for SEO. Your pages can address the way customers describe a problem while explaining the product capabilities that solve it in the terms specialists recognise.
What do dimensions mean?
Dimensions are the numbers in an embedding. Each contributes to its representation, rather than acting as a labelled box for an obvious topic.
OpenAI documents default lengths of 1,536 dimensions for text-embedding-3-small and 3,072 for text-embedding-3-large. They're settings, not writing targets. Adding paragraphs to match a dimension count would be a particularly expensive misunderstanding.
A token is different. Tokens are pieces of text a model processes; a passage can contain many tokens and produce one embedding for retrieval.
How does cosine similarity work?
Cosine similarity measures the angle between vectors, comparing their direction rather than their length. More aligned vectors have higher cosine similarity.
The formula is short: cosine similarity = (A · B) / (||A|| × ||B||).
Read that as the dot product divided by the product of the vector lengths. OpenAI documents this search calculation.
| Mathematical value | What it means geometrically | What it cannot tell you |
|---|---|---|
| 1 | The vectors point in the same direction | Identical wording or factual accuracy |
| 0 | The vectors are perpendicular | A universal boundary between relevant and irrelevant text |
| -1 | The vectors point in opposite directions | Opposite meanings in ordinary language |
The mathematical definition sets these bounds for non-zero vectors, so they describe geometry rather than a calibrated assessment of your website content. They aren't SEO targets.
An embedding tool might return 0.8 for a passage. That doesn't mean an 80% chance of ranking, and multiplying it by 100 doesn't make it a Google score.
Scores depend on the model. They also depend on which text you submit, how you split it and whether you compare documents with documents or queries with passages.
The practical consequence? Compare like with like, then read the text. A passage about access control can be relevant to your buyer while describing a feature your product doesn't offer.
How do vector search, keyword search and hybrid search differ?
Vector search retrieves items whose embeddings resemble the query embedding. Keyword search looks for matching terms, while hybrid search combines lexical and semantic retrieval.
They solve different problems. Your enterprise content needs both the customer explanation and the exact product identifiers that a technical evaluator wants to verify before contacting sales.
| Search approach | Useful for | Example on a software website |
|---|---|---|
| Keyword or lexical search | Exact names, codes and terminology | Finding a particular integration name |
| Vector or semantic search | Related meanings expressed differently | Matching “remove leavers” with “employee offboarding” |
| Hybrid search | Combining exact terms with meaning | Finding offboarding documentation for a named integration |
Dense embeddings distribute learned information across their dimensions. Traditional sparse representations mostly contain zero values, with non-zero entries indicating particular terms or features.
A vector database stores and searches embeddings. Nearest-neighbour search finds nearby candidates within that collection. At scale, systems can use approximate methods to reduce the work needed to find those candidates.
OpenAI's retrieval guide documents hybrid ranking that balances embedding matches and keyword matches. It also documents filtering and ranking controls.
That's a developer product. Its public settings don't reveal how ChatGPT Search ranks the open web, and Google's developer embedding products don't expose Google's organic ranking formula either.
Does Google use embeddings to rank pages?
Google publicly describes systems that understand concepts and relationships between words, including neural matching, BERT and RankBrain. It doesn't publish a universal embedding score for website owners to optimise.
Its ranking systems guide makes these distinctions:
| Google system | Google's explanation in plain English |
|---|---|
| Neural matching | Matches representations of concepts in queries and pages |
| BERT | Understands how combinations of words express meaning and intent |
| RankBrain | Relates words to concepts, including matches without every exact query word |
| Passage ranking | Uses individual sections to understand a page's relevance |
The guide describes other systems too. Links and reliable information still matter. An embedding API cannot reproduce that whole set of decisions merely because its provider also operates a search engine.
For Searchmaxxed, the work still includes crawl access, indexing, page structure and accurate content. An inaccessible page stays inaccessible.
Google's AI features guidance says existing SEO fundamentals remain relevant for AI Overviews and AI Mode. Supporting pages need indexing and snippet eligibility.
No special embedding submission appears in those requirements. Keep the basics working.
What is passage retrieval, and how should it change your writing?
Passage retrieval selects a relevant section from a larger document. Write sections that answer a question without relying on distant paragraphs to explain the product, conditions or exceptions.
A chunk is a section a retrieval system processes separately. Systems can split documents by length or structure; your website doesn't control every system's boundaries.
OpenAI's vector stores automatically chunk, embed and index uploaded files. That illustrates the mechanism. Google's passage ranking has a different public description: it helps Google understand a page's relevance through individual sections.
Avoid treating them as identical systems.
A worked example: enterprise access-management software
Consider a hypothetical SaaS company. Its buyer needs to know whether managers can remove employee access without getting administrator permissions across the entire platform.
A broad paragraph about “secure, flexible enterprise access” leaves that question unanswered. More synonyms won't fix it.
| Page element | Unhelpful version | More useful version, if the product supports it |
|---|---|---|
| Heading | Flexible access | Can managers remove access without becoming administrators? |
| Answer | Our platform simplifies enterprise permissions. | Managers can revoke access for their assigned team without receiving organisation-wide administrator rights. |
| Condition | Explained elsewhere | An administrator must assign the delegated access role first. |
| Evidence | General security claim | Link to the role-permissions table and revocation documentation. |
| Limitation | Omitted | State which integrations need a separate revocation step. |
The improvement comes from the answer. It tells procurement and security teams what happens, which permission they need and where the documentation supports the claim before they book a demonstration.
That's the page work Searchmaxxed provides through on-page SEO: product facts, direct answers, relevant supporting pages and internal links. The hypothetical feature needs product-owner verification before publication.
Keep exceptions nearby. If a retrieval system extracts only the permission claim, a limitation several sections later might never reach the person evaluating your software for their organisation.
How can embeddings improve site structure and internal links?
Embeddings can suggest related pages and reveal groups of overlapping content. Treat those suggestions as candidates, then decide whether the pages serve the same task or different ones.
Related doesn't mean interchangeable. The access-management company still needs a product page, an offboarding guide and technical documentation, even when an embedding tool places them beside each other.
| Embedding analysis | Useful SEO action | Check before acting |
|---|---|---|
| Query-to-page matching | Find a potential landing page for a search | Does the page answer the intent and offer the right next step? |
| Page clustering | Organise related products, guides and documentation | Do the categories make sense to customers? |
| Internal-link suggestions | Connect an explanation to supporting detail | Does the link help at this exact point in the text? |
| Content overlap | Inspect pages that might compete for the same searches | Do Search Console and live results support consolidation? |
| Gap analysis | Find a question with no adequate answer | Is the answer absent, or merely hard to find? |
| Redirect candidates | Find possible destinations during a migration | Does the destination preserve the old page's purpose? |
For our example, the offboarding guide should link to the product capability where it explains removing access. The product page should link to permissions documentation where evaluators need the implementation detail.
Every page has a job. That principle matters more than forcing all pages towards a single average embedding, especially when an enterprise website serves several products and several audiences.
Our guide to topical authority explains how related pages can support a subject without repeating the same answer. Searchmaxxed's site-structure work follows those distinct page roles.
Can embedding scores tell you whether AI will cite your page?
Embedding scores can indicate semantic relatedness within a chosen model. They cannot establish whether an AI answer will select your page as a source.
We have a useful distinction from our own research. In our Australian agency-question panel on 2 October 2026:
We put 300 Australian agency-buyer questions to ChatGPT three times each, with search on. It pulled 17,870 search results into those 900 answers and kept 4,389 as sources. Being found is the easy part. About three in four results it reads never make the answer.
That was a retrieval-and-citation observation, not an embedding experiment. We didn't measure cosine scores in that panel or isolate their effect on source selection.
Measure those stages separately. A page can enter a system's research and still contribute nothing visible to the final answer, even when its subject matches the question.
How modern models choose sources examines that gap. For your software company, track whether answers cite the permissions documentation and whether the explanation of your product stays accurate.
RAG means retrieval-augmented generation: a system retrieves material and supplies it to a model to help produce an answer. Retrieval and generation are different stages.
Getting into the candidate set gives a source the opportunity to contribute, but the system still has to choose material for its answer.
How should you evaluate an embedding SEO tool?
Evaluate an embedding tool against decisions your team can verify, such as useful internal-link suggestions or correct matches between queries and pages. Start with known examples.
For the SaaS company, include a genuine offboarding match and a related page that addresses the wrong product. Add a passage that omits an important exception.
Then inspect the recommendations. A tool that cannot separate those cases needs more scrutiny before it shapes a website-wide rewrite or consolidation project.
Ask the vendor these questions:
- Which embedding model and version produce the score?
- Does the tool compare titles, main content, whole pages or individual passages?
- Does it strip navigation and other repeated website text?
- What happens when content exceeds the model's input limit?
- Does it distinguish query-to-document retrieval from text-to-text similarity?
- Can you inspect the exact text behind each suggestion?
- What evidence connects its recommended threshold to your site's actual results?
The task distinction matters. Google's embedding documentation separates RETRIEVAL_QUERY and RETRIEVAL_DOCUMENT from SEMANTIC_SIMILARITY.
Google explicitly says the semantic-similarity task isn't intended for retrieval, a distinction worth checking before accepting a tool's query-to-page recommendations as evidence. A tool can perform correct maths on the wrong representation and still give you a convincing dashboard.
Keep the model and extraction settings fixed when comparing revisions. Otherwise, a score change might reflect a different measurement rather than an improvement to the page itself.
Then measure the released work. Searchmaxxed includes Search Console and GA4 checks alongside search and AI visibility measurement, so page changes connect to enquiries rather than stopping at tool scores.
Is Searchmaxxed right for your SEO and AI search work?
Searchmaxxed fits teams that want someone to build and improve the pages behind search visibility. We cover technical SEO, content, site structure and AI search optimisation.
Our AI search optimisation service connects that work with GEO, AEO and LLM SEO. We want customers to find your company through Google and AI answers, then reach a page that explains why your product fits their situation.
You keep the assets. Our managed packages include client ownership of the website, repository, content and data, with direct access to the people doing the work.
| Plan | Relevant scope |
|---|---|
| Growth System | New pages or substantial rebuilds, internal links, technical work and AI visibility tracking |
| Agentic Search Engineering | Adds comparison, industry and integration page families, plus digital PR and qualified link campaigns |
| Custom & Enterprise | Scope can include multiple markets, dedicated engineering, custom reporting and original first-party research |
The fit is practical.
| Searchmaxxed is a good fit if | Look elsewhere if |
|---|---|
| Your enterprise, B2B SaaS, venture-backed, manufacturing or public-sector team wants SEO and AI search implementation | You want strategy advice with no changes to your site |
| You want us to improve product pages, technical SEO and internal links | You only need embeddings for an internal search application |
For an internal search application, your engineering team can evaluate the developer APIs directly.
For the access-management example, better search work means publishing the supported permissions answer and connecting it to the right product and documentation pages. The score can wait.
Frequently asked questions
What exactly are embeddings?
Embeddings are numerical representations of inputs such as text. They let software compare relationships between those inputs, including phrases that discuss the same subject with different words.
What is semantic SEO, and how does it work?
Semantic SEO makes a page's meaning and relationships clear through accurate explanations, related topics and useful links. It goes beyond repeating keywords. For enterprise software, that means connecting a customer's problem to the product feature and documentation that answer it.
Does ChatGPT use embeddings?
OpenAI documents embedding models and vector-store retrieval for developers. Those are different product surfaces. An API similarity result alone cannot tell you which website ChatGPT will cite.
What is a good cosine similarity score for SEO?
There is no universal SEO threshold. Judge scores within the same model and configuration against examples your team has checked for relevance, then verify whether the proposed edits help users.
Should every passage have the same word count?
No fixed word count fits every answer. Keep each answer with its conditions and supporting facts, and split sections when the question changes rather than to satisfy a chunk-size target.
Do I need a vector database to rank in Google?
Google's published requirements don't call for a vector database on your website. A database serves other purposes. Crawlable pages, indexing and useful content remain the website priorities.
Should I remove every page with low semantic similarity?
No. A specialist integration page can differ from the rest of your website while answering an important customer question. Inspect the page before removing it.
What should you do next?
Start with a product page and a customer question that your sales team keeps answering because the website still doesn't explain it properly. Check the explanation, its evidence and the links before commissioning a rewrite around an embedding score.
If you want Searchmaxxed to handle the SEO and AI search work, bring that page to the conversation. Let's talk pricing.