Full-text and fuzzy search
Fast keyword search that forgives typos and understands word forms.
Search · Semantic engines
Semantic search development
People search the way they talk. We build search that understands what they mean, puts the right result first and gets better as they use it.
When people can’t find it, they assume you don’t have it
Bad search costs more than it looks. Customers leave when the product they wanted doesn’t show up, and staff waste hours hunting for a document they know exists. Usually the data is fine. The search just matches words instead of meaning.
We fix that with a mix of classic full-text search, vector search on embeddings, and ranking tuned on your real queries. We measure relevance with test sets, not gut feel, so every change can be checked before it goes live.
- Full-text plus vector search
- Tuned on your real queries
- Typo-tolerant and fast
- Relevance measured, not guessed
What we build
Vector search
Embeddings that match meaning, so “cheap flights” finds “budget airfare”.
Hybrid search and ranking
Keyword and vector results blended and re-ranked for the best first page.
Auto-suggest and spell correction
Helpful suggestions as people type, and fixes for the typos they make.
Natural-language queries
Ask a question in plain English and get the answer, not just a list of links.
Relevance testing
Test sets and click data that show whether each change made results better.
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How we work
- 1
Collect real queries
We start from what people actually type, including the searches that return nothing.
- 2
Build a relevance test set
Queries with known good answers, so we can score every change.
- 3
Index and tune
The right mix of full-text, vector and filters for your content, tuned against the test set.
- 4
Launch and learn
Track what people click and what they skip, and keep tuning.
Questions we often get
What is semantic search?
Semantic search finds results by meaning rather than exact words. It turns queries and content into vectors (embeddings), so a search for “laptop won’t charge” can match an article titled “Battery not charging”.
Do we have to replace Elasticsearch or Solr?
No. Both now support vector search, so we can usually add semantic search to the engine you already run.
What is hybrid search?
Hybrid search runs keyword and vector search together and blends the results. It keeps the precision of exact matches, like product codes, and adds the recall of meaning-based matches.
How is this different from a RAG chatbot?
Search returns the right documents or products. RAG goes one step further and has a language model write an answer from them. Good RAG needs good search underneath, so we often build both.
What does a search project cost?
It depends on how much content there is and where it lives. After a free 30-minute call we send a plan and estimate, usually within a week.
Tell us what you’re building
A senior engineer will reply within one business day.