Vector databases and embeddings let marketing teams search and group content by meaning rather than exact words. An embedding model turns text into a list of numbers that places similar ideas close together, and a vector database stores those numbers and finds the nearest matches quickly. Workflows built on them save time on content audits, internal linking, site search and support answers, provided the content is chunked sensibly and results are checked.
What are embeddings and vector databases?
An embedding is a numerical representation of text, images or other data produced by a model. Texts about similar topics get similar vectors, so “cheap flights to Rome” and “budget airfare Italy” end up close together even with no words in common. Embedding models are offered by OpenAI, Google, Cohere, Voyage AI and as open models such as those listed on Hugging Face’s MTEB leaderboard.
A vector database stores these vectors with their source text and metadata and answers “what is closest to this?” in milliseconds. Options include managed services such as Pinecone, Weaviate Cloud and Qdrant Cloud, extensions like pgvector for PostgreSQL, and vector search features in tools teams may already use, such as Elasticsearch, OpenSearch, MongoDB Atlas and BigQuery.
Which marketing tasks benefit?
Embeddings help wherever meaning matters more than keywords:
- Finding near-duplicate blog posts that compete for the same search intent.
- Suggesting internal links between related articles.
- Powering site search that understands synonyms and questions.
- Grounding a support or sales chatbot in company documents, known as retrieval-augmented generation.
- Clustering keyword lists or customer feedback into themes.
- Matching products to queries in catalogs with inconsistent naming.
How do you build an embeddings workflow step by step?
The core process is the same for most tasks:
- Collect the content: export posts, pages, products or tickets with IDs, URLs and metadata such as language and category.
- Clean it: strip navigation, footers, scripts and repeated boilerplate that would make everything look similar.
- Chunk it: split long documents into passages of a few hundred words, ideally at headings.
- Embed it: send each chunk to one embedding model and store the vector with its ID and metadata.
- Index it: load vectors into the vector database with metadata filters such as language or content type.
- Query it: embed the question or source text with the same model and retrieve the nearest chunks.
- Review it: check a sample of results by hand and adjust chunking or filters.
Using the same model for documents and queries is essential. Vectors from different models live in different spaces and cannot be compared.
Chunking decides quality
Chunks that are too long blur several topics into one vector; chunks that are too short lose context. Splitting at H2 or H3 headings usually works well for articles, with the page title added to each chunk so the passage keeps its subject. Test two or three sizes on real queries before committing. Overlap of a sentence or two between neighbouring chunks also helps when answers sit at a boundary.
How do you find duplicate content with embeddings?
For a content audit, embed one vector per article, built from the title, introduction and headings rather than the full body, which dilutes the signal. For each article, retrieve its nearest neighbours and flag pairs above a similarity threshold. The right threshold depends on the model and content, so set it by reviewing pairs at several levels.
Flagged pairs then go to an editor, who decides to merge, redirect, differentiate or keep both. Merged pages should redirect with a 301 to the kept version. The embedding step finds candidates in minutes across thousands of posts; the editorial decision still needs a person who knows the search intent behind each page.
How do embeddings help internal linking?
Embed each article and, for every post, retrieve the closest five to ten others with the same language and a published status. Present them to the editor as suggestions with the passage that matched, so anchor text can be chosen naturally. Exclude pages marked noindex and very short pages, which make poor link targets.
Run it again whenever a batch of new content goes live. New posts get links from older related articles, which helps them get discovered and crawled.
What should you watch out for?
A few issues come up repeatedly. Boilerplate text that slipped through cleaning makes unrelated pages look similar. Mixed languages in one index produce odd matches unless filtered by language. Costs grow with re-embedding, so store vectors and only re-embed changed content. And similarity is not truth: a chatbot grounded on retrieved chunks can still misread them, so answers need source links and review.
Privacy applies too. Customer tickets and CRM notes sent to a hosted embedding API leave your systems. Use providers whose data terms fit, or open models hosted in your own environment, for sensitive material.
How Alien Road applies vector databases and embeddings
In a typical engagement we start with a content export from the client’s CMS and a cleaning pass that removes templates and navigation. We choose an embedding model based on languages and data terms, chunk content at headings and load it into a vector store that fits the client’s stack, often pgvector or BigQuery when those already exist.
The first deliverable is usually a duplicate and cannibalisation report, reviewed with the client’s editors, followed by internal link suggestions for each article. Where clients need site search or a support assistant, the same index is reused with retrieval and source links. Reports track merged or redirected pages, links added, organic performance of affected pages and, for assistants, answer accuracy on a test set.
Where to start this week
Export the titles and introductions of your fifty most recent blog posts into a spreadsheet. Embed them with any embedding API or a free open model in a short notebook and list each post’s three nearest neighbours. If several pairs are clearly about the same topic, you have found your first cannibalisation problems and a strong reason to build the full workflow. Start there.
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