Forem: Rafał Groń

I Tracked 24 WordPress Plugins Across 5 Review Sites. Here's What Paid Promotion Actually Does.

Rafał Groń — Wed, 15 Apr 2026 14:12:30 +0000

I was about to pay $500 for a sponsored review on one of the big WordPress blogs.

Before I did, I decided to check whether they actually move the needle.

I spent three weeks pulling Wayback Machine snapshots for 24 plugins across 5 WordPress review sites — tracking active install counts before and after each review published.

The results were not what I expected.

Methodology

For each plugin I followed the same process:

Find the exact publication date of the review
Pull a Wayback Machine snapshot of the WordPress.org plugin page from just before the review
Pull snapshots from 2–4 weeks after, then 2–6 months after
Compare install counts

Same process. 24 plugins. 5 blogs.

WP Mayor — 6 Plugins

Paid placements range from $250 to $750. All content is disclosed as paid — that transparency is commendable.

Results: 4 zero impact, 1 possible small bump (~120 installs), 1 no change.

I contacted WP Mayor CEO Mark Zahra before publishing. He responded in detail. His main point was fair: their clients usually sell premium plugins where conversions happen through direct sales, not WordPress.org installs. The free install count isn't a metric they track or promise. I included his full response in the original article.

Barn2 — 2 Plugins

Sponsored content starts at $499. But the most interesting data comes from Barn2's own transparency reports — sponsored content revenue dropped 82% in one year ($4,883 to $894). They've since removed the advertising page from their site.

Results: 1 zero impact, 1 decline.

ThemeIsle — 5 Plugins

They explicitly refuse paid reviews: "We do not interfere with their work and cannot accept sponsored review/link requests."

Results: organic listicle, some plugins showed modest consistent growth.

The only site with any positive signal — and they don't sell reviews.

WPExplorer — 5 Plugins

Results: 3 zero impact, 2 declined.

One declining plugin hadn't been updated in 4 years — so that's a plugin problem, not a review problem.

LearnWoo — 3 Plugins

Operated by WebToffee, a company that also sells its own plugins on the same blog.

Results: 2 zero impact, 1 growing — but growth started well before the review published.

Summary

Blog	Tracked	Zero impact	Possible bump	Declined
WP Mayor	6	4	1	0
Barn2	2	1	0	1
ThemeIsle	5	5	0	0
WPExplorer	5	3	0	2
LearnWoo	3	2	0	0
Total	21	15	1	3

Why Doesn't It Work?

Most WordPress users discover plugins through WordPress.org search or the admin dashboard — not blog articles. WP Mayor has ~16k monthly visitors. WordPress.org gets millions. The path from "reading a review" to "installing" has too many steps and too much friction.

What Does Work?

Based on building Queryra — an AI search plugin for WordPress and WooCommerce:

WordPress.org readme.txt SEO — this is where most installs come from
Organic mentions in curated lists — ThemeIsle's data backs this up
Direct community presence — Reddit, Facebook groups, developer forums

Paid editorial placements don't appear in the data.

Full Research

All individual plugin data, methodology details, and Mark Zahra's full response are in the original article:

👉 AI-powered WordPress search plugin — full research

Queryra vs Relevanssi: Do You Need AI Search or Is Keyword Search Enough?

Rafał Groń — Wed, 08 Apr 2026 11:05:51 +0000

Relevanssi is the default answer when someone asks "how do I fix WordPress search?" And for good reason — 100,000+ active installations, 1.6 million downloads, a genuinely useful free version, and over a decade of active development by Mikko Saari.

Queryra takes a different approach entirely. Instead of making keyword search smarter, it uses AI to understand what people mean — regardless of the words they use.

Full disclosure: I'm the founder of Queryra. I'll be straightforward about where Relevanssi is the better choice.

Two Approaches to the Same Problem

Relevanssi builds a custom index in your WordPress database. Uses TF-IDF weighting to rank results by keyword relevance. Intelligent keyword matching — the same approach Google used before they added AI.

Queryra converts content and queries into vector embeddings — numerical representations of meaning. Finds content whose meaning is closest, regardless of keyword overlap.

Relevanssi asks "do these words match?" Queryra asks "do these meanings match?"

Where Relevanssi Wins

The free version is genuinely good. Not a teaser — a fully functional search plugin. For blogs and small stores, often enough on its own.

100,000+ installs means battle-tested. Every WordPress configuration, theme conflict, plugin compatibility issue — they've seen it.

Runs on your server. Content never leaves your hosting. No external dependency.

PDF indexing. Search inside product manuals, spec sheets, documentation.

Deep customization. Extensive API with dozens of filters and hooks.

Database space is your only cost. No API calls, no per-search fees. Trade-off: index can be 2–3x the size of your wp_posts table.

Multisite support. Search across multiple sites in a network.

Where Queryra Wins

Natural language queries. Customer searches "something warm for winter evenings." Relevanssi needs exact word matches. Queryra understands "cozy fleece blanket" is semantically related.

Intent-aware parsing. "Headphones under $50, not Beats, sort by rating" becomes a semantic search with price filter, brand exclusion, and sort preference — all from plain text.

Zero synonym management. No need to manually map "couch" to "sofa." The AI understands they mean the same thing.

Multilingual without plugins. 50+ languages, no WPML or Polylang needed.

No database bloat. Queryra stores the index in the cloud. Your WordPress database stays clean.

Try before you install. Despite 100,000+ installs, Relevanssi has no public demo. We couldn't find a single site showcasing it. Queryra has live demos you can try right now — or search 3,000+ Wikipedia articles.

The Same Store, Five Queries

"coffee mug" — Both work perfectly. Keywords match.
"something to keep my drink hot" — Relevanssi might miss it. Queryra returns thermoses and insulated mugs.
"gift for someone who just moved" — Relevanssi finds nothing unless you configured synonyms. Queryra connects it to housewarming gifts.
"modern lamp, not floor standing, under $75" — Relevanssi can't parse negation or price from text. Queryra handles all three.
"lámpara de mesa" (Spanish for "table lamp") — On an English store, Relevanssi returns nothing. Queryra returns table lamps.

The pattern: the more natural the query, the bigger the gap.

Pricing

Relevanssi: Free version with core features. Premium ~$99–129/year (unlimited sites). Permanent ~$299–434 (lifetime). No per-search costs.

Queryra: Free 14-day trial, no credit card. $9.99/month ($120/year). Sandbox Club for unlimited testing.

Relevanssi's free version is hard to beat on budget. For premium features, both cost roughly the same annually — but deliver very different search experiences.

Who Should Use What

Choose Relevanssi if you run a blog or content site with keyword searches. You want a free plugin that dramatically improves default search. You need PDF indexing or multisite. You prefer everything running locally.

Choose Queryra if you run a WooCommerce store where product discovery impacts revenue. Your customers search with natural language. You don't want synonym lists. You need multilingual search. You want AI to handle relevance automatically.

The Honest Take

Relevanssi is the most important search plugin in WordPress history. It proved that default search was broken and gave 100,000+ sites a real fix — many for free. If you run a content site, install Relevanssi. Don't overthink it.

But keyword search has a ceiling. No amount of synonym configuration can bridge the gap between "gift for someone who loves cooking" and a Le Creuset Dutch oven. That's not a Relevanssi limitation — it's a keyword search limitation.

Queryra exists for the stores where that ceiling matters. Where a failed search means a lost sale, not just a frustrated reader.

WordPress plugin: wordpress.org/plugins/queryra-ai-search

Originally published at queryra.com

Queryra vs SearchWP: AI Semantic Search vs the Most Popular Keyword Plugin

Rafał Groń — Wed, 08 Apr 2026 11:04:57 +0000

SearchWP is one of the most respected WordPress search plugins. Trusted by 30,000+ sites, acquired by Awesome Motive in 2021, and backed by over a decade of development since its launch in 2013.

Queryra takes a fundamentally different approach. Instead of making keyword search better, it replaces keyword matching entirely with AI that understands what people mean.

This isn't a case of one being "better" — they solve the same problem with completely different technology.

Full disclosure: I'm the founder of Queryra. I'll be honest about what SearchWP does better.

The Core Difference: Keywords vs Meaning

SearchWP makes keyword search smarter. It indexes custom fields, PDFs, taxonomies, and WooCommerce product data. When a customer searches "blue running shoes," SearchWP finds products containing those words and ranks them by configured weights.

Queryra doesn't match keywords at all. It converts products and queries into vector embeddings — numerical representations of meaning. When a customer searches "something comfortable for morning jogs," Queryra returns running shoes even though no product contains those words.

SearchWP is the best keyword search plugin for WordPress. Queryra is semantic search — a different category entirely.

Where SearchWP Wins

Customization depth. Granular control over ranking — weights for titles, content, slugs, excerpts, custom fields. Multiple search engines for different site sections.

Content indexing breadth. PDF documents, custom database tables, media files, shortcode output, ACF fields, taxonomy terms.

Analytics. Popular terms, top clicks, failed searches — directly in WordPress dashboard.

Ecosystem. Part of the Awesome Motive family. Integrates with WooCommerce, EDD, BigCommerce, Gravity Forms, ACF, and more.

Track record. 30,000+ sites, 10+ years, acquired by the largest WordPress plugin company.

No external dependency. Runs entirely on your server. Data never leaves your hosting.

Where Queryra Wins

Natural language understanding. "Gift for mom under $50, not jewelry" — Queryra understands intent, price constraint, and exclusion. SearchWP needs exact keyword matches plus manual synonyms.

Zero configuration. Install, paste API key, sync. No weight tuning, no synonym lists.

Multilingual out of the box. 50+ languages without WPML or Polylang.

Price for WooCommerce. SearchWP Pro (WooCommerce) = $199/year. Queryra = $120/year ($9.99/month).

Intent-aware query parsing. Two-layer architecture: semantic matching + structured intent extraction (price ranges, brand exclusions, sorting).

Try before you install. SearchWP has no public demo. Queryra has live demos: real WooCommerce store and 3,000+ Wikipedia articles.

Real-World Example

A customer types: "budget laptop for college, nothing too heavy"

SearchWP looks for "budget," "laptop," "college," "heavy." No match unless you've configured synonyms for each term.

Queryra understands "budget" = affordable, "college" = student use, "nothing too heavy" = lightweight. Returns relevant results without configuration.

Pricing

SearchWP: Standard $99/year (no WooCommerce). Pro $199/year (WooCommerce included). All Access $399/year. No free tier. 14-day money-back guarantee.

Queryra: Free 14-day trial, no credit card. $9.99/month with full WooCommerce support. Sandbox Club for unlimited testing.

Who Should Use What

Choose SearchWP if you need PDF indexing, custom database tables, or complex post types. You want full control over ranking. You prefer everything on your server. You have time to configure synonyms and weights.

Choose Queryra if your customers search in natural language. You don't want synonym lists. You need multilingual search. Product discovery directly impacts your revenue. You want to test AI search for free before committing.

The Honest Take

SearchWP is the best of traditional search. It rewards configuration investment with precise results.

Queryra is the next generation. It removes keywords entirely and matches by meaning. Less manual control, but no synonym maintenance.

If your customers type "SKU-12345," keyword search works perfectly. If they type "comfortable shoes for standing all day under $80," semantic search wins.

WordPress plugin: wordpress.org/plugins/queryra-ai-search

Originally published at queryra.com

Best AI Search Plugins for WordPress in 2026 (Beyond WooCommerce)

Rafał Groń — Wed, 08 Apr 2026 11:02:52 +0000

Most "best AI search plugin" articles focus on WooCommerce stores. Makes sense — product search has an obvious ROI. But WordPress powers far more than just online stores.

Blogs with thousands of articles. Knowledge bases where customers need to find the right answer fast. Recipe sites, real estate listings, job boards, documentation wikis, membership sites with years of archived content.

On all of these sites, the default WordPress search is equally terrible. And on all of them, AI search can make a real difference.

This guide covers AI search plugins that work for WordPress broadly — not just WooCommerce. We'll look at what each one actually does, what it costs, and whether the AI is real semantic search or just marketing.

Because in 2026, half the plugins calling themselves "AI search" are still keyword search with a chatbot wrapper.

What Actually Makes Search "AI" (And What Doesn't)

Real AI search (semantic search) uses vector embeddings to understand meaning. Your content is converted into numerical representations by a language model. When someone searches, their query is converted the same way, and the system finds content whose meaning is closest — regardless of keyword overlap. A search for "how to fix a leaky faucet" can find an article titled "Repairing kitchen plumbing drips" even though no words match.

Keyword search with AI features is still traditional keyword matching, but with extras like typo correction, synonym suggestions, or an AI chatbot on top. The search itself is still matching words, not meaning.

AI content generation — plugins that use GPT to write content or generate meta descriptions. Nothing to do with search.

Queryra — Semantic Search for Any WordPress Site

AI semantic search that replaces default WordPress search. Uses vector embeddings to match content by meaning, with an intent-aware query parser that handles natural language filters.

Works with any WordPress post type — blog posts, pages, products, custom post types. The REST API works on any platform.

Install the plugin, sync your posts. When a visitor searches "articles about managing remote teams across time zones," Queryra finds relevant posts even if they're titled "Async communication best practices for distributed companies." The meaning matches even though the words don't.

What's good: True semantic search. Intent-aware parser handles complex queries ("posts about X, not Y"). 50+ languages without configuration. 5-minute setup, no API keys from OpenAI needed. REST API for headless WordPress or non-WordPress sites.

What's not: New product (launched January 2026). Requires cloud API connection. No PDF indexing. Free trial is 14 days, then $9.99/month.

Try it: Search 3,000+ Wikipedia articles or search the Queryra blog with AI

WordPress plugin: wordpress.org/plugins/queryra-ai-search

Full disclosure: I built Queryra. I'll be honest about the alternatives below.

AI Search — OpenAI-Powered Semantic Search

Uses OpenAI's text-embedding-3-small model to generate embeddings for your WordPress content. Stores embeddings in your local database and matches queries by vector similarity. Smart 4-tier fallback: semantic → fuzzy → keyword → spell-correct.

What's good: True semantic search. Smart fallback system. Embeddings stored locally. Works with WooCommerce, ACF, and custom post types. Free plugin on WordPress.org.

What's not: Requires your own OpenAI API key. Costs can exceed $1,000/month for large sites. 90 active installs despite 27,000+ downloads — the API key requirement kills activation.

Despite thousands of downloads, we couldn't find a single public-facing website using AI Search in production. The plugin's demo appears to run on a showcase domain rather than a real customer site.

ExpertRec — AI Search as a Service

Cloud-based search service with a WordPress plugin. Uses AI and machine learning for search, autocomplete, voice search, and recommendations.

What's good: AI-powered with NLP and typo correction. Voice search. PDF, DOCX, XLSX search. Multisite support.

What's not: SaaS pricing that scales with traffic. Less WordPress-native feel.

Price: Free plan (100 pages) → paid plans from $9/month

The Keyword Plugins Worth Mentioning

These aren't AI search, but they're what most WordPress sites currently use.

Relevanssi (100,000+ installs, free + premium) — The most popular WordPress search plugin. TF-IDF relevance ranking, fuzzy matching, custom field indexing, PDF search. Best for: blogs and content sites that need better keyword matching without any cost.

SearchWP (30,000+ users, $99–399/year) — Premium keyword search with deep customization, analytics, and WooCommerce integration. Best for: sites that need precise control over search ranking.

Ivory Search (free, 90,000+ installs) — Lightweight plugin with exclusion rules and custom field search. Best for: simple sites that need minor improvements.

FiboSearch (free + premium) — Live AJAX search with product thumbnails. Best for: WooCommerce stores that want better search UX without AI.

One thing worth noting: none of the keyword search plugins above offer a public demo. Despite a combined 230,000+ installations, you can't test any of them before installing. Queryra is the only search plugin in this article with live demos anyone can try.

When Is AI Search Actually Worth It?

You probably need AI search if:

Your site has 500+ posts and visitors struggle to find content
You see "zero results" queries for terms that should match something
Your visitors search with questions and descriptions, not precise keywords
Your content uses varied terminology across articles
You serve a multilingual audience

Keyword search is probably fine if:

Your site has fewer than 100 posts
Visitors search with exact terms they already know
Your budget is zero and Relevanssi's free version covers your needs
Your content is in one language with consistent terminology

Our Recommendation

For blogs on a budget: Relevanssi (free). For knowledge bases: Queryra or ExpertRec. For WooCommerce stores: see our WooCommerce-specific comparison. For multilingual sites: Queryra's 50+ languages out of the box. For developers: Queryra's REST API works outside WordPress entirely.

No matter what you choose, replacing default WordPress search with anything is an improvement. The default search in 2026 is the same basic keyword matching it was in 2006.

Originally published at queryra.com

How to Make Your WooCommerce Store Discoverable by ChatGPT (And Convert That Traffic)

Rafał Groń — Wed, 01 Apr 2026 08:07:32 +0000

Shopify just got native ChatGPT integration. WooCommerce store owners have to set it up manually — but the manual approach actually gives you something Shopify's integration doesn't.

Here's the full setup.

Part 1: Discovery — Tell ChatGPT Your Store Exists

Create an llms.txt file

Place it at yourstore.com/llms.txt. ChatGPT-User bot, PerplexityBot, and other AI crawlers read this file to understand what your site is about.

# YourStore
> [What you sell, in one sentence]

## About
YourStore sells [product category] for [customer type].

## Product Categories
- Category 1: https://yourstore.com/category/cat-1/
- Category 2: https://yourstore.com/category/cat-2/

## Search
AI-powered semantic search available.
Endpoint: https://yourstore.com/?s={query}&post_type=product

## Key Pages
- Shop: https://yourstore.com/shop/
- Contact: https://yourstore.com/contact/

Add schema.org Product markup

For WooCommerce, Rank Math or Yoast handle this. Make sure these fields are populated: name, sku, price, availability, brand.

Consistency matters more than completeness. A product where SKU matches across schema, feed, and title is a stronger entity signal than one with rich but inconsistent markup.

Part 2: Conversion — Fix What Happens When They Arrive

Here's what most guides skip.

You can't put 1,000 products into llms.txt. So when a customer arrives from ChatGPT and searches your store, default WooCommerce search still fails them:

"moisturizer for sensitive skin, under $30" → 0 results
"wireless headphones, not Sony" → 0 results
"gift for someone who loves cooking" → 0 results

ChatGPT brought them to you. Your search lost them.

The fix: semantic search trained on your specific catalog

Queryra is a WooCommerce AI search plugin that replaces default keyword search with vector embeddings. It indexes your catalog in minutes and understands natural language queries, price filters, brand exclusions, and 50+ languages out of the box.

Customer types: "wireless headphones under $60, not Sony"
→ Layer 1: vector search finds semantically relevant headphones
→ Layer 2: intent parser extracts price cap ($60) and brand exclusion (Sony)
→ Result: filtered, ranked, relevant products

No OpenAI API key required — everything runs on Queryra's infrastructure.

The Full Funnel

ChatGPT (discovers your store via llms.txt + schema)
    ↓
Customer arrives with purchase intent
    ↓
Queryra (AI trained only on YOUR catalog)
    ↓
Customer finds the right product
    ↓
Sale

General AI for discovery. Your own AI for conversion.

Resources:

Queryra AI Search Now Works in 50+ Languages — Multilingual WooCommerce Search

Rafał Groń — Thu, 26 Mar 2026 10:35:34 +0000

Default WooCommerce search is broken in English. In Polish, German, or Dutch — it's even worse.

Today I shipped multilingual support for Queryra — AI semantic search for WooCommerce now works in 50+ languages out of the box.

What this means in practice

A Polish customer searching "prezent dla mamy" finds gift products — even without exact keyword matches in the product titles.

A German customer searching "etwas Warmes für den Winter" finds winter clothing.

A Dutch store with Dutch product descriptions works end to end.

No configuration. No language settings. No extra cost.

How it works

We switched to a multilingual sentence transformer model that understands semantic meaning across 50+ languages. The same AI that matches "gift for someone who likes cooking" to relevant products in English now does the same in Polish, German, French, Spanish, Dutch, Serbian, and dozens of others.

Why this matters

Most "AI search" plugins for WooCommerce are English-only. Non-English stores — which represent the majority of WooCommerce installations globally — have been stuck with keyword search that fails on natural language queries in any language.

This update unblocks all of them.

Plugin: wordpress.org/plugins/queryra-ai-search
Demo: woo.queryra.com

How to Properly Test an AI Search Plugin Before Recommending It to a Client

Rafał Groń — Mon, 23 Mar 2026 15:27:14 +0000

You've found an AI search plugin for WooCommerce. The demo looks impressive. But before you recommend it to a client, you need to know it actually works — on their catalog, with their products, for their customers.

Here's how to do that properly.

The wrong way to test AI search

Most developers test like this:

Install plugin
Search for a product by name
It works → recommend to client

The problem: keyword search also handles exact product name queries just fine. You're not testing AI. You're testing autocomplete.

What you actually need to test

AI semantic search earns its place when keyword search fails. So test the failure cases.

Natural language queries

"gift for someone who likes cooking"
"something warm for winter evenings"
"casual outfit for beach wedding"

None of these contain product names. Keyword search returns zero results. Semantic search should find relevant products.

Negations and constraints

"wireless headphones not Apple"
"moisturizer without fragrance"
"laptop under $800 not Lenovo"

This is where most "AI search" plugins fall apart. They do semantic matching but ignore constraints. Test this explicitly.

Misspellings and variations

"moisturiser" vs "moisturizer"
"sneakers" vs "trainers" vs "running shoes"
"couch" vs "sofa"

The zero-result baseline

Before installing anything, run 10 natural language queries on the client's current search. Count how many return zero results. That's your baseline. After installing the AI plugin, run the same queries and compare.

Why you need more than 14 days

Here's what actually happens during a real evaluation:

Days 1–3: Setup and first sync
Days 4–7: Initial testing, some results feel off
Days 8–10: You realize the client's product descriptions are thin. You update them. But you've already used your monthly sync.
Day 14: Trial over. You never tested the improved version.

This is why I added Sandbox Club to Queryra — unlimited syncs (1/hour), no expiration, 200 products, no credit card. For exactly this scenario: developers who need room to iterate before committing.

The checklist

Before recommending any AI search plugin to a client:

[ ] Test 5+ natural language queries on their real catalog
[ ] Test negations ("X without Y", "not brand Z")
[ ] Test misspellings and synonyms
[ ] Measure zero-result rate before and after
[ ] Re-sync after improving product descriptions
[ ] Check response time (should be under 500ms)
[ ] Verify it doesn't break WooCommerce filters and pagination
[ ] Check what happens when the AI service is unavailable (fallback?)

One more thing

Check whether the plugin requires an OpenAI API key. If it does, calculate the real monthly cost for your client's traffic level before recommending it. A plugin that's "free" but costs $300/month in API fees is not free.

Queryra is an AI semantic search plugin for WooCommerce. Sandbox Club gives you the time and syncs to evaluate it properly — queryra.com

I Tested 5 WooCommerce AI Search Plugins So You Don't Have To

Rafał Groń — Tue, 10 Mar 2026 10:37:08 +0000

My WooCommerce store was bleeding customers at the search bar.

Analytics showed 34% of visitors used search — and 41% of those searches returned zero results. People typed "wireless earbuds under 50" and got nothing. They typed "gift for mom who likes cooking" and left.

I spent three weeks testing every AI search plugin I could find. Here's what actually works.

Why "AI Search" Usually Means Nothing

Before the results: a quick reality check.

Most plugins marketing themselves as "AI search" in 2025–2026 are doing one of two things:

Fuzzy keyword matching — better typo tolerance, still keyword-based
ChatGPT API calls — slow (2–5s), expensive, and ChatGPT doesn't know your products

True semantic search — where the engine understands meaning, not just words — is actually rare in the WordPress ecosystem. I tested for this specifically.

My test store: WooCommerce, ~200 products, electronics + accessories.

Test queries I used:

gift for girlfriend (no direct keyword match)
something for gaming setup (broad intent)
wireless not apple (negation)
under $40 for outdoor (price + context)

The 5 Plugins I Tested

1. FiboSearch (Ajax Search for WooCommerce)

Type: Keyword + fuzzy

The most popular WooCommerce search plugin on WordPress.org. Handles autocomplete beautifully, extremely fast.

Results for gift for girlfriend: 0 relevant results. It found products with "gift" in the title — nothing else.

Results for gaming setup accessories: only returned products with the exact word "gaming". My RGB desk mat didn't show up.

Verdict: Best-in-class for keyword search. Not semantic. Great UX, wrong technology for intent-based queries.

2. Searchanise

Type: SaaS keyword search with AI features

Hosted solution, requires external indexing. Nice dashboard, good analytics. They market "AI" features but it's mostly synonym expansion and behavioral ranking.

Results for gift for girlfriend: returned jewelry and accessories — but only because those categories were tagged as "gift" manually.

Cost: starts ~$19/month. Scales with catalog size.

Verdict: Solid upgrade over default WooCommerce search. Smart keyword search, not semantic. Good if you're willing to maintain synonym lists.

3. ElasticPress

Type: Elasticsearch-powered

Requires Elasticsearch hosting (add $15–50/month for infrastructure). Powerful, scalable, used by enterprise WordPress.

Semantic capabilities depend on your Elasticsearch configuration. Out of the box — still keyword-based with better indexing.

Verdict: Overkill for most stores. Enterprise choice, not plug-and-play.

4. Doofinder

Type: Commercial AI search (SaaS)

Probably the most feature-rich option. Behavioral AI that learns from click patterns. But expensive — pricing starts around $99/month.

Better than keyword solutions for natural language queries, but struggles with pure intent queries on new stores (cold start problem — needs behavioral data to learn from).

Verdict: Best commercial option if budget isn't a concern.

5. Queryra

Type: True semantic search (vector embeddings)

This one works differently. Instead of matching keywords, it converts your entire product catalog into vector embeddings — mathematical representations of meaning. When someone searches, their query gets converted too, and the engine finds products by semantic similarity.

products → embeddings → vector index
query → embedding → cosine similarity → ranked results

Results for gift for girlfriend: returned jewelry, perfumes, accessories — all relevant, none containing the word "girlfriend" in their descriptions. ✅

Results for gaming setup accessories: returned RGB lighting, cable management, desk mats, monitor stands — things that belong in a gaming setup even without that exact phrase. ✅

Results for wireless not apple: actually filtered out Apple products. ✅

Results for under $40 outdoor: combined price filtering with semantic context. ✅

Setup: Install the WordPress plugin → enter one API key → click sync. Done. No OpenAI account needed — the AI runs on their backend.

Speed: Sub-500ms on all my tests. Uses pre-computed vector similarity (ChromaDB) — not a live API call per search.

Pricing: Free tier available. Paid plans from $9.99/month.

🔗 queryra.com | WordPress.org plugin | Live demo

Side-by-Side Comparison

gift for girlfriend
FiboSearch ❌ | Searchanise ⚠️ | Doofinder ⚠️ | Queryra ✅

gaming setup accessories
FiboSearch ❌ | Searchanise ⚠️ | Doofinder ✅ | Queryra ✅

wireless not apple
FiboSearch ❌ | Searchanise ❌ | Doofinder ✅ | Queryra ✅

under $40 outdoor
FiboSearch ⚠️ | Searchanise ✅ | Doofinder ✅ | Queryra ✅

Setup complexity
FiboSearch Easy | Searchanise Medium | Doofinder Medium | Queryra Easy

Pricing
FiboSearch Free/paid | Searchanise $19+/mo | Doofinder $99+/mo | Queryra Free/$9.99+

What I Actually Use Now

After testing, Queryra is running on my store. The main reason isn't features — it's the zero-result problem.

Before: 41% of searches returned zero results.
After two weeks with Queryra: under 8%.

That's the metric that matters for a WooCommerce store. Every zero-result page is a customer who left.

The semantic approach also means I don't maintain synonym lists. SearchWP and Searchanise both require manual synonym configuration to handle things like "wireless earbuds" → also returning "bluetooth headphones." With vector embeddings, that relationship is implicit — the model already knows they're semantically similar.

When to Use Each Option

Use FiboSearch if: You want the best UX/autocomplete and customers search with exact product names.

Use Searchanise if: You want a hosted solution with analytics and you're willing to invest time configuring synonyms.

Use Doofinder if: Budget isn't a concern and you have significant traffic to train the behavioral AI on.

Use Queryra if: You want true semantic understanding — customers finding products through intent and natural language — without complex setup or enterprise pricing.

Use ElasticPress if: You're running large-scale with dedicated DevOps resources.

The Bottom Line

The WordPress search plugin market is still catching up to what semantic AI actually means. Most "AI" plugins are keyword search with better UX. True vector-based semantic search is still a relatively small category.

For stores where product discovery is the bottleneck, the difference is measurable in conversion rate.

Zero-result rate dropped from 41% to 8%. That's the test.

Useful links:

Have you tested other WooCommerce AI search plugins? Curious what you found — especially if you've run actual conversion comparisons. Drop a comment below.

Why I Added an LLM Parser on Top of Vector Search (And What It Changed)

Rafał Groń — Mon, 09 Mar 2026 11:27:07 +0000

I thought vector search was enough.

I'd built Queryra — an AI search plugin for WooCommerce and Shopify. Replaced keyword matching with semantic embeddings. Customers could search "something warm for winter" and find sweaters, fleece jackets, blankets. Zero results became rare. It worked.

Then someone searched: "wireless headphones under $80, not Beats"

The vector search returned wireless headphones. Some were $200. Several were Beats. The price cap and brand exclusion were completely invisible to the embedding model.

That's when I realized: vector search was layer one. I was missing layer two.

The Problem With Pure Vector Search

Embeddings are brilliant at one thing: encoding semantic similarity. "Sneakers" lands close to "trainers" and "running shoes" in vector space. "Gift for dad" finds garden tools, BBQ sets, and watches — even without those words in the query.

But a query like "laptop under $1000 for video editing, not Chromebook" contains two fundamentally different types of information:

Semantic intent — what the customer wants (a powerful laptop for video work)
Structural constraints — how to filter results (price cap, category exclusion)

Embeddings handle #1 well. They have no mechanism for #2.

You can't encode "under $1000" as a direction in vector space. "Not Chromebook" isn't a semantic concept — it's an instruction to the search system. Every vector-only implementation has this blind spot, and it gets worse as queries get more specific.

The customers most affected? Highest-intent buyers. The ones ready to purchase right now.

The Solution: LLM Parser as Layer Two

I added a query parser that runs before the vector search. Its job: decompose the query into structured components.

Here's the logic (simplified):

Input: "organic shampoo without sulfates under $25, best rated"

Parser output:
{
  "semantic_query": "organic shampoo",
  "price_max": 25,
  "attribute_exclude": ["sulfates"],
  "sort_by": "rating"
}

Each component then goes to the right system:

semantic_query → vector search (finds semantically relevant products)
price_max → database filter (hard cut at $25)
attribute_exclude → post-filter (removes sulfate-containing products)
sort_by → result reranking (surfaces highest-rated first)

The vector layer finds what the customer means. The parser layer applies what they said.

The Bypass Problem (Latency)

The parser adds ~700–800ms latency. For a simple query like "blue t-shirt", that's pure overhead — embeddings handle it fine alone.

So I added a pre-filter that routes queries before hitting the parser:

def should_parse(query: str) -> bool:
    # Price signals
    if re.search(r'under \$|below \$|\$\d+|budget|cheap|premium', query, re.I):
        return True
    # Exclusion signals  
    if re.search(r'\bnot\b|\bwithout\b|\bno\b|\bexclude\b', query, re.I):
        return True
    # Sorting signals
    if re.search(r'best rated|top rated|newest|cheapest|most popular', query, re.I):
        return True
    # Brand signals (capitalized words that aren't at sentence start)
    if re.search(r'(?<!^)(?<!\. )[A-Z][a-z]+(?:\s[A-Z][a-z]+)*', query):
        return True

    return False  # Simple query — go straight to vector search

Simple queries skip the parser entirely. Complex queries get full intent extraction. The routing is invisible to the user — they just get better results.

What Changed

Before the parser, queries with constraints returned random results within the right category. After:

Query	Before	After
"headphones under $80"	All headphones	Headphones ≤ $80 only
"not from BrandX"	Includes BrandX	BrandX excluded
"best rated coffee maker"	Random order	Sorted by rating
"organic, no sulfates"	All organic shampoos	Sulfate-free filtered

The first row of every table is identical — simple semantic queries work the same. Every other row shows the gap.

One Unexpected Benefit: Typo + Constraint Combinations

I expected the parser to help with structured queries. I didn't expect it to also fix a secondary problem: typos combined with constraints.

Vector search handles typos well on their own — "moisturiser" finds "moisturizer". But "moisturiser under $20 without pareban" (parabens misspelled) was tricky. The embedding similarity dropped on the misspelled exclusion.

The LLM parser handles both in one pass: corrects the typo, extracts the price constraint, identifies the exclusion. Combined robustness I didn't plan for.

The Tradeoff

This approach has a real cost: the parser uses an LLM API call on complex queries. That's not free. I use gpt-4.1-nano (cheapest option, identical quality to gpt-4o-mini for this use case, ~33% cheaper). With the bypass logic, only a fraction of queries hit the parser — but it's still a cost that scales with traffic.

For a self-hosted open-source setup, you'd replace the LLM call with a local model (Ollama + Mistral 7B works reasonably well for intent extraction). For a SaaS product, you build it into pricing.

Where This Goes Next

The parser currently extracts: price ranges, brand references, attribute filters, exclusions, sorting preferences, and basic negations.

Next on the list: multi-intent queries. "Something for the office and something for the gym" — two separate semantic searches, merged results. Vector search alone can't split the intent. Parser can.

If you're building ecommerce search and hitting the same wall — vector results that ignore everything after the first two meaningful words — this two-layer approach is worth the added complexity.

I wrote a longer non-technical version for store owners here: Why Vector Search Alone Isn't Enough for Ecommerce Stores

Happy to answer questions about the implementation in the comments.

Queryra is AI search for WooCommerce and Shopify. queryra.com

Why 40% of WooCommerce Searches Fail (And the SQL Query That Causes It)

Rafał Groń — Mon, 16 Feb 2026 16:44:44 +0000

Every WooCommerce store runs on the same broken search query:

SELECT * FROM wp_posts
WHERE post_type = 'product'
AND post_status = 'publish'
AND (post_title LIKE '%gift for dad%'
     OR post_content LIKE '%gift for dad%')

That's it. That's the entire search engine behind stores doing $10k–500k/month in revenue.

No synonym matching. No fuzzy search. No understanding of intent. Just LIKE '%keyword%'.

What Actually Happens

When a customer searches "gift for dad who likes gardening", WordPress tries to find those exact words in product titles and descriptions.

Your store might have:

🌿 Professional Garden Tool Set — perfect match by meaning
🌱 Organic Herb Seed Collection — great gift for a gardener
🧤 Leather Garden Gloves - Premium — exactly what they want

But the SQL query returns: 0 results.

Because none of those products contain the phrase "gift for dad who likes gardening".

The customer leaves. Opens ChatGPT. Gets sent to Amazon.

How Big Is This Problem?

Studies show 10–40% of e-commerce searches return zero results. On a store with 100 searches/day, that's 10–40 customers who wanted to buy and couldn't.

Quick math:

30 failed searches × $50 AOV × 20% conversion rate = $300/day lost

That's $9,000/month — from search failures alone.

The 5-Query Diagnostic

You can test your store's search in 2 minutes. Try these five queries:

"gift for [person]" — tests natural language understanding
"[common misspelling]" — tests fuzzy matching
"[describe use case, not product name]" — tests semantic understanding
"[color] + [category]" — tests attribute search
"[synonym for your product]" — tests synonym matching

If 3+ return zero or irrelevant results, your search is costing you sales.

4 Ways to Fix It

Fix 1: Optimize Product Data (Free)

Add synonyms and use cases to your product descriptions manually. Fill in all attributes, tags, categories.

❌ Title: "Garden Tool Set"
✅ Title: "Garden Tool Set — Perfect Gift for Gardeners"
✅ Description: includes "gift", "gardening", "outdoor", "dad", "father"

Pros: Free, immediate improvement.
Cons: Doesn't scale. Won't handle natural language queries you haven't anticipated.

Fix 2: Enhanced Keyword Search (Relevanssi)

Replaces the LIKE query with a TF-IDF algorithm. Adds partial matching, stemming, weighted fields. 100,000+ active installs — battle-tested.

Price: Free / $119/year premium
Setup: 10–15 minutes

Pros: Proven, reliable, huge community.
Cons: Still keyword-based. "Gift for dad" won't find garden tools unless those exact words appear somewhere.

Fix 3: Enterprise Search (Algolia)

External search-as-a-service. Sub-5ms responses, typo tolerance, faceted filtering, analytics. Powers search for Stripe and Twitch.

Price: $50–500+/month
Setup: 2–3 days (requires developer)

Pros: Fastest search on the market. Advanced analytics.
Cons: Expensive. Complex. Overkill for stores under 5,000 products.

Fix 4: AI Semantic Search (Queryra)

Full disclosure — I built this one.

Instead of keyword matching, Queryra converts products and queries into vector embeddings and matches by meaning similarity:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

# Index a product
product_embedding = model.encode(
    "Professional Garden Tool Set - pruning shears, trowel, rake"
)

# Search query
query_embedding = model.encode(
    "gift for dad who likes gardening"
)

# Cosine similarity → 0.82 (high match!)
similarity = cosine_similarity(query_embedding, product_embedding)

"Gift for dad who likes gardening" → finds garden tools, seed kits, plant pots. Without those words appearing in any product title.

Price: $9.99/month (14-day free trial, no credit card)
Setup: 5 minutes, no OpenAI key needed

How to Choose

It depends on your main problem:

Customers search with natural language → semantic search (Queryra)
Typos and partial matches → keyword search (Relevanssi, SearchWP)
Enterprise scale + analytics → Algolia
Search UI looks bad → YITH Ajax Search

Most stores start with one, measure the improvement, then add complexity if needed.

Try It Yourself

🔍 Live demo on a real WooCommerce store — try searching "looking older than my age" on a skincare store
📝 Full guide with all 4 fixes compared
📦 WordPress plugin on WordPress.org
💻 GitHub source code

What search solution are you using on your WordPress / WooCommerce sites? Have you run into the same zero-results problem? Drop a comment 👇

I Asked 5 AI Assistants to Recommend My Product — None of Them Knew It Existed

Rafał Groń — Thu, 05 Feb 2026 11:04:52 +0000

I launched a product on WordPress.org a week ago. Out of curiosity, I asked ChatGPT:

"What are the best AI-powered search plugins for WooCommerce in 2025?"

It had no idea my plugin exists. Neither did Claude, Gemini, or Perplexity.

That got me thinking — if AI assistants can't find your product, do you even exist in 2025?

The Experiment

I asked the same question to five different AI assistants:

ChatGPT (GPT-4o)
Claude
Gemini
Perplexity
Copilot

Results: All five recommended the same 3-4 plugins that have been around for years. None mentioned anything launched in the past 6 months.

This isn't just my problem. Try it yourself — ask any AI assistant about your product, your competitor's new feature, or any recent WordPress plugin. Chances are, it draws a blank.

Why AI Assistants Don't Know Your Product

Unlike Google, which crawls and indexes pages constantly, AI models have a knowledge cutoff. But that's only part of the story.

Even AI assistants with web search (like Perplexity or ChatGPT with browsing) struggle to find new products because:

No backlinks yet — new products rank low in search results that AI pulls from
No structured data — AI can't parse your product if it doesn't understand what it's looking at
Blocked crawlers — many sites accidentally block AI bots in robots.txt
No AI-readable documentation — your marketing copy isn't optimized for how AI processes information

What I Did About It

For my plugin (Queryra — an AI search plugin for WooCommerce), I tested several techniques to improve AI discoverability:

1. llms.txt Files

A new convention gaining traction — plain text files that describe your product in a way AI models can easily parse:

/llms.txt          # Quick summary
/llms-full.txt     # Complete documentation

Think of it as robots.txt but for AI understanding, not crawling.

2. Schema JSON-LD Markup

Structured data that tells AI exactly what your product is:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Queryra",
  "applicationCategory": "WordPress Plugin",
  "operatingSystem": "WordPress/WooCommerce"
}

3. AI Crawler Permissions

Explicitly allowing AI bots to crawl your site:

User-agent: GPTBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: ClaudeBot
Allow: /

Many WordPress sites block these by default without realizing it.

4. Comparison Pages

A /compare page with structured competitor analysis gives AI the context it needs to position your product relative to alternatives.

What Actually Worked?

It's too early for definitive data, but here's what I've noticed after one week:

Perplexity (which does live web search) started surfacing Queryra in results faster than others
Schema markup alone isn't enough — AI needs text content around it
llms.txt is promising but adoption is still early
Comparison pages seem to give AI the most useful context for recommendations

The honest answer: none of these are a silver bullet yet. AI discoverability is where SEO was in 2005 — nobody really knows the rules, but the people who figure it out early will have a massive advantage.

Key Takeaways for Developers

If you're launching anything in the WordPress/WooCommerce ecosystem (or anywhere, really):

Check your robots.txt — make sure you're not accidentally blocking GPTBot, ClaudeBot, and other AI crawlers
Add structured data — Schema.org markup helps AI understand what your product actually is
Create an llms.txt file — it's 10 minutes of work and the standard is growing
Write comparison content — AI loves structured "X vs Y" context
Don't rely on WordPress.org alone — being listed in the plugin directory doesn't mean AI knows about you

Try This Right Now

Ask ChatGPT or Claude:

"What are the best AI search plugins for WooCommerce?"

Then search for the same thing in the WordPress plugin directory. Compare the results. The gap is staggering.

That gap is an opportunity — for your products too.

I'm building Queryra — a semantic search plugin for WooCommerce. Free in the WordPress plugin directory (search "Queryra" in Plugins → Add New).

Has anyone else experimented with making their products visible to AI assistants? What worked for you?

Your Store Needs a "Local ChatGPT" - Not the One That Knows Everything

Rafał Groń — Mon, 02 Feb 2026 16:02:48 +0000

ChatGPT is amazing. But it has one big problem for e-commerce:

It knows TOO MUCH.

Customer asks: "What's a good gift for mom?"

ChatGPT answers: "Here are some ideas from Amazon, Etsy, Target..."

Your store had the perfect necklace. Sale lost.

The "Local ChatGPT" Concept

What if you had ChatGPT that ONLY knew your products?

Same AI understanding of intent
Same natural language processing
But trained ONLY on YOUR 500 products
No API key needed
No OpenAI account

Customer searches "gift for mom" → finds YOUR jewelry, YOUR perfumes, YOUR scarves.

Why No API Key?

Most AI plugins require:

Create OpenAI account
Add credit card
Generate API key
Paste into plugin
Pay per search

99% of store owners won't do this.

Queryra includes everything - one API key from us, no external accounts.

Try It

Free plugin: Queryra AI Search on WordPress.org

Live demo: Try semantic search on Wikipedia

Pricing: See all plans

What search problems do you face in your store?