Burchett & Co.v1.1.1

Measure Content Alignment with Vector Embeddings

The Burchett & Co. Page Alignment Tool measures the cosine similarity between any webpage and any search query.

Search engines and large language models convert text into numerical representations called vector embeddings. They use these embeddings to measure how closely a page’s content matches a user’s query, not by counting keywords, but by comparing meaning. The mathematical measure they use is cosine similarity: the closer two vectors are in direction, the more semantically aligned the texts are.

This tool lets you see that relationship directly. Enter any webpage and any search query or question you’d ask an AI, and the tool will show you how closely each section of the page aligns with what was asked, scored and ranked from strongest to weakest.

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Enter a URL and a search query below to analyze alignment.

Frequently Asked Questions

What are vector embeddings?

Vector embeddings are numerical representations of text. They convert words, sentences, or entire documents into arrays of numbers (vectors) that capture meaning, not just keywords. Two pieces of text about the same topic will produce similar vectors, even if they use completely different words.

How do vector embeddings work?

A machine learning model reads text and outputs a fixed-length array of numbers, typically several hundred values. Each number represents a dimension of meaning. The model learns these representations from large amounts of text during training, capturing relationships between concepts. The result is that semantically similar text produces vectors that are close together in mathematical space.

Why do vector embeddings matter for digital marketing?

Traditional keyword analysis counts how many times a word appears on a page. Vector embeddings measure whether a page is actually about something, regardless of exact phrasing. A page about "affordable family vacations" and a search for "budget trips with kids" use different words but mean similar things. Embeddings capture that relationship. Keywords miss it.

How do search engines use vector embeddings?

Modern search engines use vector embeddings alongside traditional ranking signals. When someone enters a query, the search engine converts it to a vector and compares it against vectors representing pages in its index. Pages whose vectors are close to the query vector are considered semantically relevant. This allows search engines to return results that match the intent of a query, not just its literal words.

How do large language models use vector embeddings?

Large language models (LLMs) use embeddings as a foundational layer. Every piece of text an LLM processes is first converted into vectors. The model then operates on those vectors to understand context, generate responses, and retrieve relevant information. When an LLM is connected to external data through retrieval-augmented generation (RAG), it uses embedding similarity to find the most relevant documents to inform its answers.

What is cosine similarity?

Cosine similarity is a mathematical measure of how similar two vectors are. It computes the cosine of the angle between them, producing a value between -1 and 1. A score of 1 means the vectors point in the same direction (identical meaning). A score of 0 means they are unrelated. In practice, scores between text embeddings typically range from 0 to 0.8, with scores above 0.7 indicating strong semantic alignment and scores between 0.4 and 0.7 indicating moderate alignment.

What do the alignment scores in this tool mean?

The scores represent how closely a section of a webpage's content aligns with a search query in meaning, measured through vector embeddings. A score above 0.7 indicates strong alignment: the content and the query are about the same thing. Scores between 0.4 and 0.7 indicate moderate alignment: related but not tightly focused. Scores below 0.4 indicate weak alignment: the content is not meaningfully connected to the query.

What is the difference between keyword matching and embedding-based analysis?

Keyword matching checks whether specific words appear on a page and how often. Embedding-based analysis measures whether the overall meaning of the content matches the meaning of a query. A page could use a keyword dozens of times and still score poorly on embedding alignment if the surrounding content is off-topic. Conversely, a page could never use the exact keyword and still score well if the content genuinely addresses the concept behind the query.

Can vector embeddings measure content quality?

Not directly. Embeddings measure semantic similarity, which is about relevance, not quality. A poorly written page about the right topic can still produce a high alignment score. Quality depends on factors embeddings do not capture: depth of information, accuracy, writing clarity, user experience, and expertise. Alignment tells you whether a page is about the right thing. Quality determines whether it says something worth reading.

How is this tool different from an SEO audit tool?

This tool does one thing: it measures the semantic alignment between a single page and a single query using vector embeddings. It does not check keyword density, meta tags, backlinks, page speed, mobile usability, or any of the other signals that SEO audit tools report on. It answers a narrower and more fundamental question: is this page actually about what someone is searching for?