Imagine you're looking for information about a customer refund.
You search for refund issue
, but the document you're looking for is actually titled completed returns.
A traditional search engine only looks for matching words, so there's a good chance it won't find what you need. If the search terms don't match exactly, relevant results can easily be buried or missed altogether.
Semantic search changes that.
Instead of matching keywords, it understands the meaning behind a search. It recognises that "refund issue" and "completed returns" are closely related concepts, making it far more likely to return the right result the first time.
Search That Understands People
This technology isn't limited to customer support articles.
Imagine a business with hundreds of internal APIs or documents:
- An endpoint is called
Update customer profile information
- Another is called
Calculate shipping rates
With traditional search, users need to know the exact wording.
With semantic search, they can simply search for:
- Change Client Details
- Delivery Costs
and still find exactly what they're looking for.
It even works with broader concepts. A search for "money flows" could return APIs relating to payments, transactions and invoicing, despite those words never appearing in the query.
Try it for yourself
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Does It Work?
Modern semantic search is built on two core components:
An embedding model, which converts text into numerical representations that capture its meaning.
A vector database, which stores those representations and finds the most semantically similar results.
These numerical representations, known as embeddings, allow a system to measure how closely two pieces of text are related, even when they share very few words.
It's More Accessible Than Ever
Until recently, this type of search was often associated with complex AI projects and significant infrastructure costs.
Today, that's no longer the case.
Managed platforms can automatically provision vector databases and generate embeddings for you, making implementation straightforward. However, for many small and medium-sized businesses, always-on managed services can become an unnecessary ongoing expense.
A lightweight approach can be far more cost-effective.
In one of our recent demos, we combined PostgreSQL with the pgvector extension as the vector database and OpenAI's `text-embedding-3-small` model to generate embeddings. Because the embedding model is pay-as-you-go, businesses only pay for what they use rather than maintaining expensive dedicated infrastructure.
Why It Matters
Your employees and customers don't think in keywords they think in ideas.
Semantic search bridges the gap between the language people naturally use and the way information is stored, helping them find answers faster and reducing frustration.
Whether you're searching support documentation, product catalogues, internal knowledge bases or API libraries, every query is ranked by meaning rather than simple word matching, surfacing the most relevant results first.
The result is a search experience that feels more intuitive, saves time, and helps people find exactly what they came for even when they don't know the right words.
