If you run a WooCommerce store or build WordPress plugins, bad search results are silently killing conversions.

Your customer types “gift for dad who likes gardening” into your store’s search bar.

You sell garden gloves, plant pots, seed kits — exactly what they want.

WordPress search returns: 0 results.

Why? Because none of your products contain the exact words “gift”, “dad”, or “gardening” in the title.

The customer opens ChatGPT. It recommends garden gloves, plant pots, seed kits — with links to Amazon.

You had the products. You lost the sale.

To understand why this keeps happening — and whether AI semantic search actually fixes it — I built a controlled experiment.

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The Experiment: 3,000 Wikipedia Articles, Two Search Engines

I indexed 3,089 Wikipedia articles (movies, music, art, history) and compared two approaches using the same dataset:
• Keyword search — SQL ILIKE on title + description (how WordPress and WooCommerce work today)
• AI semantic search — vector embeddings + cosine similarity (how modern AI search works)

Same data. Same queries. Completely different results.

You can explore the semantic search vs keyword search live demo here:
AI-powered semantic search example

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Test 1: “painting on ceiling of famous chapel in Rome”

Any human knows the answer: the Sistine Chapel ceiling by Michelangelo.

Keyword search results:
1. Ink wash painting (3/5 words matched)
2. Hollywood Sign (1/5 words)
3. Histories (Tacitus) (1/5 words)
4. I Walk the Line (1/5 words)
5. Histoire de ma vie (1/5 words)

The engine matched individual words like “painting” and “of” and returned unrelated content.

AI semantic search results:
1. Sistine Chapel ceiling (58% relevance) ✅
2. The Feast of the Gods (50%)
3. Bacchus and Ariadne (50%)
4. The Tribute Money (Masaccio) (49%)
5. Architectural painting (49%)

The top results are all Renaissance art and Italian church paintings. This is semantic understanding.

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Test 2: “movie about jury deciding if young man is guilty”

Correct answer: 12 Angry Men (1957).

Keyword search results:
1. I Know Why the Caged Bird Sings (3/7 words)
2. Histories (Tacitus) (3/7 words)
3. Hitopadesha (2/7 words)
4. Hollywood Walk of Fame (1/7 words)
5. I Heard It Through the Grapevine (1/7 words)

Not a single courtroom movie.

AI semantic search results:
1. Crime film (47%)
2. 12 Angry Men (1957 film) (43%) ✅
3. The Age of Innocence (41%)
4. Comedy film (39%)
5. The Kid (1921 film) (39%)

The model understands movie, jury, guilty and connects them to the correct film.

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Test 3: “something warm for cold weather”

This is a classic e-commerce search query. Customers describe needs — not product names.

Keyword search results:
1. Monty Python’s Flying Circus (1/4 words)
2. Modernism (music) (1/4 words)
3. Ebenezer Scrooge (1/4 words)
4. Norwegian Wood (novel) (1/4 words)
5. Nazca lines (1/4 words)

AI semantic search results:
1. Some Like It Hot (47%)
2. Winterreise (47%)
3. Snow White (36%)
4. In Cold Blood (36%)
5. If on a winter’s night a traveler (35%)

Every result relates to cold, winter, or temperature. The query intent is understood.

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Why This Matters for WooCommerce Stores

Replace Wikipedia articles with products:

Customer query Keyword search Semantic search
“gift for mom” 0 results Jewelry, candles, kitchen gadgets
“something warm for winter” 0 results Sweaters, jackets, scarves
“comfy work shoes” 0 results Cushioned loafers, office flats
“blue top” Maybe 1 result Navy blouses, azure shirts
“healthy snacks” 0 results Protein bars, nuts, dried fruit

Around 70% of users leave after a failed search. They don’t refine the query. They just leave.

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How Semantic Search Works (Quick Version)

Traditional keyword search:

SELECT * FROM products
WHERE title ILIKE '%gift%'
OR title ILIKE '%mom%'

No exact match → no results.

Semantic search:
1. Converts products into vector embeddings (numerical meaning)
2. Converts the query into a vector
3. Measures similarity using cosine distance
4. Ranks results by meaning, not keywords

“Gift for mom” → understood as a present suitable for a woman → relevant products are returned.

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Semantic Search Demo (Real Data)

You can test this yourself on 3,000+ Wikipedia articles:
AI semantic search for WordPress

Example queries:
• “Composer who wrote a piece of complete silence” → John Cage’s 4′33″
• “Sci‑fi film where alien hides on a spaceship” → Alien
• “Silent vampire movie from the 1920s” → Nosferatu

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Applying This to WooCommerce

I built Queryra — a WordPress plugin that replaces default WooCommerce search with AI semantic search, running only on your products.

It doesn’t search the web. It searches your catalog.

Setup:
1. Install from WordPress.org
2. Generate a free API key (no credit card, no OpenAI account)
3. Sync products
4. Done

What you get:
• Free tier: 100 products, 500 searches/month
• Native WooCommerce indexing (SKUs, categories, attributes, prices)
• Smart ranking for high‑margin products
• No server changes, no GPU, shared hosting works

Plugin page:
https://wordpress.org/plugins/queryra-ai-search/

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For Developers: REST API

Queryra also works outside WordPress via REST API:

curl "https://queryra.com/api/v1/search?key=YOUR_KEY&q=gift+for+girlfriend&limit=5"

Returns your product IDs with relevance scores. Easy to integrate into any stack.

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Tech Stack (for the curious)
• Embeddings: SentenceTransformers (all‑MiniLM‑L6‑v2)
• Vector DB: ChromaDB
• Backend: Python + FastAPI
• Response time: <500 ms for 3,000+ records
• WordPress plugin: PHP (native WP hooks)

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Final Thought

If your WooCommerce search returns 0 results, customers don’t browse — they leave.

Semantic search doesn’t just improve UX. It recovers revenue you’re already paying for with ads and traffic.

If you want to test it on your own store, install the free plugin and see what queries start converting.