Insights

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.

This happens constantly on sites with traditional search. It's not a niche problem or a minor inconvenience. A visitor who searches for something and gets no results is, in most cases, a visitor who's gone. They don't assume you miscategorized it. They assume you don't carry it.

The fix is a different approach to how search understands a query, and it's now accessible to almost any website without significant infrastructure work.

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, that's a minority of searches. People describe things in their own words, use regional terms, ask questions, or don't know the proper name for what they're looking for. Keyword search treats all of that as a failure to find a match.

What "Search Like Google" Actually Means

When people say they want their site to search like Google, they're not asking for Google's scale. They're asking for Google's understanding. 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.

That experience is what visitors now expect everywhere, not just on Google, but on your site, your app, your internal documentation. The technology behind it is called semantic search, and the mechanism that makes it work is called an embedding.

What an Embedding Is, Without the Jargon

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 separately because a lot of sites are already running on one of these. Recent releases (MySQL 9+ and MariaDB 11.7+) now include a native vector type and built-in similarity search, so basic embedding-based search is potentially available without adopting any new infrastructure at all.

For most 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.
• Zero-results pages drop significantly. 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.
• Visitors trust the site more. When search works the way people expect it to, they don't think about it at all. They just feel like the site understands them. That tends to mean they browse longer and convert more often.