Here’s how to use vector search within your own site for internal SEO

Most website search functions still work with exact keywords: you only get results that literally match your query. These days, people just search differently. In fact, many people ask questions as if they were just asking a friend.

Vector search helps you do that. Vector search helps you do that. This technique converts words into numerical representations (vectors), allowing the system to understand which words belong together in terms of meaning.

I go on to tell you what vector search is, why it’s important for internal SEO and how to apply it to your own site.

What is vector search?

Vector search is a search technique that converts text into number strings. The system does not look at individual words, but at what someone means by their query. This allows the system to show good search results even if the search term does not appear verbatim in the text.

As an example, a search on “best tools for content planning” may yield pages that only list “AI tooling for editorial processes.

Vector search uses models such as Sentence Transformers or OpenAI Embeddings. Frameworks such as FAISS, Weaviate or Vespa are often used to store and search these vectors. This allows you to extend search results with contextual answers, for example through a Q&A feature.

Je herkent de context en intentie binnen het zoekgedrag van de gebruiker op deze manier sneller. (1)

Why vector search is important for internal SEO

Good internal search features are important if you want your users to easily find their way around your site. You also need to remember engagement and conversion. Vector search reinforces that role by delivering better search results for queries with terms that are not so obvious. Vector search understands this better than classic keyword matching.

This semantic coverage allows users and search engines to better understand how your content is related. This improves internal navigation as well as indirectly gives search engines stronger signals about the content and structure of your site.

With good semantic coverage, you not only help visitors but also Google to understand the logic of your site. You actually show: this belongs together. That makes your entire domain stronger.

Met vector search zie je snel waar je gebruikers naar zoeken en waar gaten zitten in je contentdekking. Daar moet je dus nog meer over schrijven. De data die deze vorm van zoeken oplevert, is ook nog eens direct bruikbaar voor het verbeteren van je content en site-architectuur. (2)

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Integration on your own site

To implement vector search on your website, you need to choose good frontend and backend integrations.

This is a basic approach:

• Make sure all content pages are properly incorporated into your site. This can be done through a model such as text-embedding-ada-002 or an open source alternative
• Create a search engine that uses cosine similarity or nearest-neighbor matching
• Integrate search functionality into your UX, such as in a search bar or as suggestions on 404s and category pages

For a simple solution, use tools like Weaviate, Typesense or Algolia with semantic search extensions.

Long-term SEO benefits

In practice, I see that vector search often leads to longer sessions, more interaction and better distribution of your traffic across your pages. There is also better content distribution across the domain (also for older pages) and more relevant signals for search engines based on user interaction

In addition, give users an experience that matches modern search intentions, as they are used to from AI-driven platforms.

Fortunately, more and more companies are doing this. Increasingly, I see companies using vector search within their own search or content platform to be found better. I’ll give you an example of how this works well:

Say you have hundreds of blog articles and knowledge base pages on various topics. Then traditional search logic based on exact words doesn’t always work well. Vector search makes it possible to search by meaning.

A visitor searching “How do I improve my internal link structure?” by vector search will get results about content clusters or entities, even if those words are not literally in the text.

I personally find that visitors click through more and stay longer if they actually find what they are looking for. People find what they are looking for faster, stay longer reading and click through more often. This is exactly the behavior that search engines recognize as a sign of quality.

Summary

Vector search is more than a technical upgrade to your search function. It is a strategic tool for your own SEO, allowing you to easily see what your users want and are looking for. By properly guiding users through your site, your content is much more usable. You then have valuable contact with visitors because you have a better idea of what they are looking for. Even pages that would otherwise not be picked up by the search engines are now found and visited by your target audience.

Vector search and internal SEO in practice

Still have questions about how vector search works and what it means for internal SEO? Below I answer the most common questions.

Is vector search difficult to implement?

You already lay the foundation of vector search with existing embedding models and libraries like FAISS or Weaviate. For complex environments, a custom setup is often better.

What is the difference between vector search and classic search functions?

Classic search engines evaluate exact words, while vector search recognizes meaning. As a result, relevant results without an exact keyword are also found.

Does vector search also affect my external SEO?

Indirectly, vector search can help with external SEO because users find what they are looking for better and therefore stay on your site longer. In time, those signals boost visibility in search engines.