Help visitors find what they mean with semantic search.
Site search often works like a very literal librarian and that's a problem. Here's how to address it.
Imagine a visitor typing "comfy chair for my home office" into your site's search bar. If your search only matches keywords, it comes back empty, because your product is listed as "ergonomic desk seating." The words don't overlap, so the system decides there's nothing there. The visitor leaves. You had exactly what they wanted.
When a reasonable query returns nothing, visitors might conclude the site doesn't have what they need. Even though the right product or content was there, the visitor leaves and a potential opportunity for a sale or enquiry is lost.
Semantic search reduces these false zero-result searches by matching meaning as well as wording. Importantly, it can be added to virtually all existing sites without needing to rebuild the entire platform.
What Traditional Search Actually Does
Standard site search works by matching the words in a query against the words in your content. It's good at finding exact phrases, and modern implementations add useful features like typo correction and stemming (so "running" also matches "run"). But it has no understanding of meaning. "Cheap flights" and "budget airfare" are, to a keyword search engine, two completely different things with nothing in common.
This is fine when your visitors know exactly what to type and type it exactly as it appears in your content. In practice, though, that's a minority of searches. People typically describe things in their own words, use regional terms or community-specific language, ask questions, or explain things abstractly when they're unsure of the proper name for what they're looking for. Traditional keyword search may treat all of those queries as failures to find a match.
You don't need to rebuild your site
If your site's search still works the old way, that's usually a targeted fix rather than a reason to rebuild anything. Take a look at our web design work or get in touch and we'll work out what makes sense for your current setup.
What "Search Like Google" Actually Means
When people say they want their site to search like Google, they usually mean Google's ability to interpret a query. Search Google for "best laptop for video editing" and it doesn't just look for pages containing those five words. It understands that you're asking about processing power, graphics performance, and screen quality, and it surfaces pages that address those things even if the page never uses your exact phrase.
Visitors now expect that experience across websites, apps and internal documentation. The technology behind it is called semantic search, and the mechanism that makes it work is called an embedding.
What the heck is an "Embedding"??
An embedding converts a word, sentence, or page into a set of numbers that represents its meaning rather than its spelling. Think of it as placing every concept on a giant map. "Sofa" and "couch" end up sitting right next to each other on that map, because they mean the same thing, even though they share no letters. "Chair" is nearby. "Volcano" is somewhere entirely different.
When a visitor searches, their query gets converted into a point on that same map, and the system finds the nearest neighbours: the content whose meaning is closest, regardless of how it's worded. In most implementations this runs alongside traditional keyword search rather than replacing it, so you get exact matches when someone types a precise product name or SKU, and meaning-based matches when they describe what they want in their own words. The two approaches cover each other's gaps.
The Tools That Make This Practical
A few years ago, building semantic search into a website required significant custom infrastructure. That's no longer the case. Several platforms now offer this as a standard feature:
- Algolia: probably the most widely used option for business websites. It combines fast, typo-tolerant keyword search with AI-powered semantic ranking, and comes with a polished interface that doesn't require you to train or host your own model. For most sites, it's the lowest-friction path to meaningfully better search.
- Typesense and Meilisearch: open-source alternatives that are quick to set up and increasingly include built-in vector search. Worth considering if you'd rather self-host or keep licensing costs down.
- Elasticsearch and OpenSearch: the enterprise-grade option, now with vector search capabilities, for teams already running their own search infrastructure.
- Pinecone and Weaviate: purpose-built vector databases for more custom, AI-driven search or recommendation experiences. More setup involved, but more flexibility.
- MySQL and MariaDB: worth mentioning because many sites already use one of them. MariaDB 11.7+ supports vector columns, indexes and similarity search. MySQL 9.7 supports the VECTOR type, although its distance functions are currently limited to MySQL HeatWave on OCI and MySQL AI.
For business websites, this isn't an infrastructure project. It's a configuration choice on a hosted service, or in some cases, a feature sitting inside the database you're already using.
What This Means for the Person Doing the Searching
The technical details are worth understanding, but the real argument for semantic search is simpler: visitors notice when search works and they notice more when it doesn't.
A few things that change with meaning-based search:
- People find what they want without knowing the "correct" term. A shopper who wants a couch that splits into two pieces doesn't know it's called a modular sectional. They shouldn't have to.
- Fewer searches end on a zero-results page. A dead-end search result is one of the fastest ways to lose a visitor, because it tells them you don't carry what they're looking for, even when you do.
- Natural language works. People increasingly type full descriptions rather than keyword fragments. "Something to keep my kid busy on a rainy Saturday" is a real search query that keyword search has no idea what to do with.
- Typos and phrasing variations stop being punished. Whether someone types "receive" or "recieve," or uses a regional word for the same product, the system handles it rather than returning nothing.
- Search feels less frustrating. When results reflect what someone meant, they can find what they need without repeatedly rewriting the query.
Sources & Further Reading
The difference between keyword search and semantic search comes down to intent. Keyword search matches wording, while semantic search also considers meaning. That shift is the same one that made Google feel effortless, and it's now available to most websites through tools like Algolia, Typesense, or the vector features built into standard databases.
Visitors never see any of the infrastructure behind it. They just notice that the search bar finally understands them. For a lot of sites, that's a meaningful difference in how many of those visitors actually find what they came for.
If that's not how your site's search feels today, it's usually fixable without rebuilding everything. Our web design work is built with this kind of search performance in mind, or get in touch and we'll walk through what it would take on your current site.