The WordPress plugin directory is full of "AI search" plugins. Most don't use AI in the technical sense. They use keyword matching, sometimes with synonym lookup or an optional ChatGPT API call layered on top.
For developers evaluating these plugins for client projects or personal stores, the marketing-vs-reality gap matters. Knowing what's under the hood determines whether the plugin will handle the queries your users will actually type.
Here's how to verify any "AI search" plugin's technical claims yourself.
The architectures behind "AI search"
There are three main approaches:
1. TF-IDF (keyword matching with statistics)
Term Frequency × Inverse Document Frequency. Classic information retrieval since the 1970s. Words from the query are matched against indexed product fields (title, description, tags). Each match is weighted by how often the word appears in the product vs the catalog overall. Synonyms and fuzzy matching can be layered on top.
Strengths: fast, deterministic, no LLM calls, low cost.
Limitations: requires keyword overlap between query and product. Does not understand intent. "Pull-out wall mount" cannot match "Pull-Down Full Motion TV Mount" unless explicit synonyms are configured.
2. Vector embeddings (semantic search)
Each product is converted to a mathematical representation (vector) using an embedding model. The vector captures meaning, not just words. Queries are embedded the same way. Similarity is measured by cosine distance between query vector and product vectors.
Strengths: handles paraphrasing, intent, multiple languages, natural language queries.
Limitations: requires embedding infrastructure (Pinecone, Supabase pgvector). Embedding cost per product at indexing. Query embedding cost per search.
3. LLM-augmented keyword search
Traditional keyword search with an LLM (usually ChatGPT or similar) called to enhance the query or rerank results. The base architecture is keyword. The LLM provides query expansion or natural language understanding as a layer.
Strengths: better than pure keyword, simpler than full vector setup.
Limitations: still bottlenecked by keyword matching at the retrieval stage. LLM costs per query. Latency from API calls.
How to test which architecture a plugin uses
Run these four queries against any "AI search" plugin demo:
Test 1: Out-of-vocabulary intent
Search a product attribute that does not appear in any product title. For a TV mount store: "pull out from wall" when products are titled "Pull-Down Full Motion."
Expected behavior:
- Vector embedding semantic search: returns relevant products
- TF-IDF with synonyms: returns relevant products IF synonyms are configured for this exact case
- Pure keyword: returns nothing
Test 2: Multilingual without configuration
Search the same product category in a different language than your catalog. For an English catalog: "kurtka zimowa bez futra" (Polish: winter jacket without fur).
Expected behavior:
- Keyword search without translation plugin: returns nothing
- Keyword search with translation plugin: depends on per-language synonym config
Test 3: Misspellings
Search a product type with a deliberate typo: "moisturiser" (UK) when catalog uses "moisturizer" (US), or "anty aging" instead of "anti aging."
Expected behavior:
- Vector embeddings: typically handles minor spelling variations
- TF-IDF with fuzzy matching: handles some variations based on edit distance configuration
- Pure keyword exact-match: returns nothing
Test 4: Abstract intent
Search a problem description rather than a product. For a skincare store: "dry skin on my elbows" or "something for tired-looking eyes."
Expected behavior:
- Vector embeddings: bridges intent to product (e.g., body moisturizer for elbows)
- Keyword search: returns nothing unless products literally contain those phrases
If only Test 4 returns no results, the plugin is keyword-based regardless of marketing language. If Tests 1 through 3 also fail, it is purely keyword matching with no synonym configuration.
Results from testing 5 popular WordPress AI search plugins
I ran these tests against the major "AI search" plugins in the directory last weekend. Verified through public documentation and free tier setup.
FiboSearch
Architecture: keyword matching with fuzzy logic and synonyms. Optional ChatGPT integration for query enhancement.
Test results: handles Test 3 (misspellings) via fuzzy matching. Fails Test 1, Test 2, Test 4 without explicit synonym configuration. The "AI" in marketing refers to the optional ChatGPT layer.
Verdict: TF-IDF with optional LLM enhancement.
SearchWP
Architecture: keyword indexing with weighted engine configuration. AI features (semantic, image, suggest) are paid add-ons calling OpenAI for query refinement.
Test results: similar to FiboSearch. Base retrieval is keyword. Paid AI add-ons improve query understanding but do not change the underlying architecture.
Verdict: TF-IDF with paid LLM augmentation.
Relevanssi
Architecture: full-text search with custom weighting, ranking control, and synonym support.
Test results: handles Test 3 if fuzzy matching is configured. Fails Tests 1, 2, 4 in default setup.
Verdict: TF-IDF. Documentation explicitly does not claim AI or semantic search. Honest about being keyword.
AI Vector Search (Semantic)
Architecture: dual mode. Default Lite Mode uses TF-IDF with synonyms and stopwords. Real vector embeddings activate only with paid Self-Hosted Supabase mode plus your own OpenAI API key.
Test results in default Lite Mode: same as FiboSearch (TF-IDF). Test results in paid vector mode: full semantic capability.
Verdict: depends on configuration. Default install behaves as TF-IDF. Marketing claims describe the paid configuration.
Queryra
Architecture (disclosure: my product): vector embeddings. Each product indexed with the engine. Customer queries parsed by an LLM that extracts semantic intent plus structured filters (price range, brand, exclude terms). The query vector is then matched against product vectors.
Test results: passes Tests 1, 2, 3, 4 in default free tier. No additional configuration required.
Verdict: vector embedding semantic search with intent understanding. Default install delivers what marketing describes.
What this means for plugin selection
For developers picking an "AI search" plugin for a client project:
If the use case is small catalogs with predictable customer queries, TF-IDF plugins (FiboSearch, SearchWP, Relevanssi) work well and have lower runtime costs.
If customers describe needs in natural language, search across languages, or use terminology that differs from catalog vocabulary, true vector embedding semantic search becomes the requirement. The marketing-vs-reality check is critical here. A plugin claiming "AI" but using TF-IDF will not bridge intent to product the way customers expect.
The four-query methodology above lets you verify any plugin's actual architecture in 15 minutes. Worth doing before committing to a paid tier or recommending to a client.
Conclusion
"AI search" is marketing language that covers multiple technical architectures. TF-IDF with optional LLM layers is the most common. True vector embedding semantic search is the rarest.
For WordPress in 2026, the field of plugins delivering true semantic search out of the box, without requiring paid add-ons or external infrastructure, is small.
Full methodology, FAQ, and side-by-side test results:
https://queryra.com/blog/which-wordpress-ai-search-plugins-actually-use-semantic-search
Try a live demo (no signup): woo.queryra.com
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