Whether you are a keen supporter of AI or look at it from a safe distance and the same side-eye John Connor would give it after learning a thing or two about befriending bots back in his days, you will have to agree on one thing: we are witnessing aa digital revolution. Not only that: we are living it firsthand.
Things are moving fast in the world of artificial intelligence, Large Language Models (LLMs) like OpenAI’s GPT series are becoming part of our life in ways we could not even imagine just a few years ago, businesses are embracing them at a steady rate and with a projected market value increasing 5x in the next 5 years, it’s not just something we can’t keep off our radar.
AI is a valid tool to complement the irreplaceable human skills that make up the core of marketing, and if you are looking for smart ways to integrate it in your marketing strategy, you’re in the right place.
Do we have your attention? Great, read on.
To truly unlock the potential of artificial intelligence models, it is essential to present information in a way that they can understand and retrieve efficiently. This is where vector embeddings come into play.
In this blog, we will explore the concept of vector embeddings, explain the tangible benefits of vectorising your website, and give you practical insights to achieve this using tools like Screaming Frog and OpenAI.
First things first: what are vector embeddings?
In very simple terms, vector embeddings are numerical representations of text.
Imagine each piece of content (be it a sentence, paragraph, or webpage) is transformed into a multi-dimensional vector in a mathematical space. Similar pieces of content end up clustered close together, while unrelated content is placed far apart.
For example, if you have a website with articles about “digital marketing” and “gardening,” vector embeddings help a language model understand that queries related to SEO, social media or email marketing are closer in meaning to digital marketing content than gardening tips. Pretty handy, right?
If you are wondering why this matters:
Large Language Models (or LLMs for friends) operate at their absolute best when they can retrieve relevant information quickly.
When you vectorise your website, you’re essentially helping them to be more efficient.
Instead of scanning the entire text each time, they work with these condensed numerical representations, which significantly improves search relevance, content recommendations, and personalised interactions.
Why vectorising your website is important
Vectorising your website is more than a technical exercise: it’s a strategic move that can transform your content strategy in different ways:
Improved Search and Retrieval
Traditional keyword-based searches are limited.
They rely on exact matches, meaning users might miss valuable content if the wording doesn’t align perfectly. With vector embeddings searches are semantic and focus on meaning rather than exact words.
For example, a user searching for “how to boost organic traffic” will retrieve articles on SEO, content marketing, and even technical website optimisation, even if those exact terms aren’t present. Boom, game changer right there.
Just think about how semantic searches can help you to improve user experience and increase engagement: a lot, really.
Enhanced Personalisation
Modern websites thrive on delivering personalised experiences.
By vectorising your content, you can match users with the most relevant resources, whether that’s product pages, blog posts, or FAQs.
LLMs can analyse user queries in real-time and serve responses that are contextually accurate and tailored to the visitor’s intent.
Smarter Chatbots and Virtual Assistants
If your website uses AI-powered chatbots or virtual assistants, embeddings can drastically improve their performance.
Instead of generic responses, your AI can understand context, retrieve precise content, and provide actionable answers, boosting customer satisfaction and reducing support workload.
No more having to type “speak to operator” on repeat until you finally get through to a human out of sheer frustration and willpower AND getting quick answers that make sense? Yes, please.
Data-Driven Insights
Vector embeddings allow you to analyse your content landscape in a way that traditional analytics cannot.
You can identify content gaps, clustering similar topics, and discover emerging trends, all of which inform strategic content creation and marketing campaigns.
We really are living in the future, why not seizing the opportunity to harness these technical advancements for your marketing efforts?
How to vectorise your website
Now that you have taken a peek into the huge benefits of vectorising your website, we know that the next pressing question on your mind is: “can I really do it? And if so, how?”
Again, in very simple terms, vectorising your website involves two key steps: extracting content and converting it into embeddings. Here’s how you can achieve this efficiently.
Step 1: Crawl your website with Screaming Frog
Screaming Frog is a popular SEO tool that allows you to crawl websites and extract content at scale. If you have never used this friendly spider to crawl your website before, here’s a simplified workflow:
- Install Screaming Frog: The free version allows basic crawls, but for larger sites, consider the paid version
- Crawl your website: Enter your website URL and start the crawl. Screaming Frog will collect all pages, including meta titles, headings, and text content
- Export the data: Once the crawl is complete, export the content into a CSV or Excel file. Focus on text-rich elements like body copy, headings, and meta descriptions.
At this stage, you have a structured dataset of all your website’s content, ready to be converted into embeddings.
Step 2: Generate vector embeddings with OpenAI
OpenAI’s APIs allow you to transform text into embeddings efficiently. You can find a lot of in-depth developer documentation on OpenAI developer platform, but here’s a top-level guide to get you started:
- Set up your OpenAI account: you need to create an account and obtain your API key.
- Prepare your text data: remember to clean your text extracted from Screaming Frog. Remove duplicates, boilerplate content, and irrelevant elements.
- Call the embeddings API: OpenAI provides embeddings models (like text-embedding-3-large) that convert text into multi-dimensional vectors. Each text snippet from your site becomes a numerical representation.
- Store the embeddings: now you’ll need to save these vectors in a database or a specialised vector store such as Pinecone, Weaviate, or FAISS. This allows fast retrieval and search.
Practical applications of website vectorisation
Vectorising your website opens up a world of opportunities to boost your digital marketing strategy and improve your content marketing performance.
Let’s take a look at some of them:
Semantic search
Traditional search bars often frustrate users by only matching exact keywords.
Vector embeddings enable semantic search, where the search engine understands intent rather than literal words.
Example: A user searches for “best ways to increase site visitors.” Even if your content uses “boost organic traffic,” semantic search bridges the gap, retrieving the most relevant content.
This can help to reduce bounce rates, lengthen dwell times and improve user experience.
Content recommendations
By comparing embeddings, you can recommend content that is genuinely related in meaning, not just by keyword overlap.
This is a powerful tool to include in your content marketing strategy.
Example: A reader exploring “email marketing campaigns” could be guided to related posts on “subject line optimisation,” “A/B testing,” or “personalisation strategies.”
This is a great way to increase engagement, session duration and ultimately lead to more conversions.
Smarter chatbots and virtual assistants
Embeddings help AI chatbots understand context and retrieve precise answers from your knowledge base.
Example: When a customer asks, “How do I reset my password?”, instead of pointing to a generic help page, the bot can deliver step-by-step instructions.
Better customer support, reduced workload for teams and faster resolutions for customers are just some of the benefits of adopting embeddings for chatbots.
Content gap analysis
Clustering embeddings helps you visualise topic coverage across your site, revealing missing areas.
Example: You might find your SEO blog is strong on “on-page SEO” and “link building” but weak on “technical SEO.”
Using embeddings this way can be beneficial to identify opportunities for new content, boosting topical authority.
Personalised user journeys
Embeddings make it possible to adapt recommendations to the intent behind a user’s actions.
Let that sink in for a minute. This is a potential marketing game-changer as more relevant user journeys can drive more conversions.
Example: An e-commerce site can show a user searching “winter coats” options like “insulated parkas” or “thermal jackets.”
Knowledge base and FAQ enhancement
Instead of scrolling through FAQs, embeddings allow users to ask natural language questions and get precise answers, providing faster support and improved self-serve experiences.
Example: A user asks, “Does your tool integrate with Google Analytics?” The system finds and surfaces the relevant section of your docs instantly.
Advanced analytics and insights
Embeddings quantify meaning, making it possible to track patterns and spot emerging topics.
This can be very helpful to stay ahead of trends and align your content strategy with audience demand.
Example: Analysis might reveal a surge in interactions around “AI-driven marketing.”
Best Practices for Vectorisation
To really get the most out of vector embeddings, consider these tips:
- Segment your content: break pages into logical chunks. Paragraphs or sections work better than entire pages
- Update regularly: websites are dynamic. Regularly update embeddings to include new content
- Pre-process your text: remove unnecessary HTML, scripts or boilerplate text to avoid noisy embeddings
- Use a vector database: storing embeddings in specialised databases ensures fast similarity searches and scalability
- Test and measure: test search and recommendation quality, tweak chunk sizes, and refine your workflow as needed
So, in conclusion:
Vector embeddings are changing how we interact with websites and content. In fact, they are making actual waves in that department.
By transforming text into numerical representations, businesses can enable semantic search, personalised recommendations, smarter chatbots, and actionable content insights that can help your marketing strategy.
Tools like Screaming Frog make content extraction straightforward, while OpenAI provides powerful APIs to generate embeddings. By following structured workflows, businesses can not only improve user experience but also gain a competitive edge in content strategy and AI-driven insights.
Embracing vectorisation today means your website isn’t just a collection of pages: it becomes a dynamic, AI-ready resource that anticipates user needs and delivers meaningful, contextually relevant experiences.
Do you need help with vectorising your website?
So, do you need a hand vectorising your website? Here at Logic Digital we love to get involved with all things SEO, and we would be very happy to help you getting your website fully AI-ready and putting your business ahead of the curve.
If you want expert guidance on SEO services tailored to your business, contact us today and discover how we can help you attract the right customers, boost conversions, and smash your targets.
Website vectorisation FAQs
What exactly are vector embeddings in simple terms?
Vector embeddings are numerical representations of text. They allow computers to understand the meaning behind words, not just the words themselves. For example, “boost website traffic” and “increase site visitors” mean the same thing, and embeddings help an AI system recognise that connection.
Why should I vectorise my website content?
Vectorising your website makes your content machine-readable for modern AI tools. This allows you to power semantic search, improve content recommendations, create smarter chatbots, and identify content gaps to enhance user experience and strengthen your digital strategy.
Do I need technical knowledge to vectorise my website?
Some basic technical understanding is useful, but it’s not essential. Tools like Screaming Frog make it easy to extract your site’s content, and services such as OpenAI’s API handle the heavy lifting of creating embeddings. Many vector databases also offer user-friendly interfaces and integrations.
How often should I update my website’s embeddings?
It depends on how often you publish new content or make significant updates. As a rule of thumb, it’s best to re-run your embedding pipeline every time you add substantial new content (such as a blog post, product page, or knowledge base article) to keep your system up to date.
What are the main business benefits of using embeddings?
The key benefits include better search accuracy, higher engagement through recommendations, improved customer support via intelligent chatbots, data-driven content insights, and ultimately stronger customer loyalty. It’s about making your content easier to find, more relevant, and more valuable to your audience.
