Search by meaning, across your own content
Keyword search fails when the user's words don't match yours. Semantic (vector) search understands intent, so a question phrased in plain language finds the right document, product or answer even without an exact term match.
We build it over your content — your docs, product catalog, knowledge base, past tickets — so results come from your material, not the open web. It's the foundation under modern site search, internal knowledge tools and AI assistants that need to cite your sources.
- Natural-language queries matched on meaning, not exact words
- Search across docs, catalogs, knowledge bases and tickets
- Synonyms and paraphrases handled without manual keyword lists
- Foundation for retrieval-augmented AI assistants over your data
- Filters and metadata combined with semantic ranking
Built to be relevant and fast
Good search is judged on the first few results. We tune ranking on real queries from your users, combine semantic and keyword signals where that helps, and keep it fast enough to feel instant.
As your content changes, the index stays current. We measure relevance on real examples rather than assuming it works, and we're upfront that some queries — very short, very ambiguous, or about content you don't have — will always be hard.
- Hybrid ranking that blends semantic and keyword matching
- Relevance tuned and tested on your actual queries
- Incremental indexing so new and updated content stays searchable
- Latency kept low enough for type-ahead and live search
- Honest handling of 'no good answer' instead of a bad guess
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Frequently asked questions
How is this different from the search we already have?
Traditional search matches words; semantic search matches intent. If someone searches 'can't log in' and your article is titled 'authentication troubleshooting,' keyword search may miss it while semantic search finds it. We often combine both so you get the strengths of each.
Does this require sending our content to an outside service?
Not necessarily. We can run the models and index on infrastructure you control if your content is sensitive, or use hosted services for lower operational overhead. We'll lay out the cost and privacy trade-offs and let you decide.
Can this power an AI chatbot over our documents?
Yes — semantic search is the retrieval layer that lets an AI assistant answer from your real content and cite where the answer came from, rather than making things up. Many clients start with search and grow into an assistant. We can scope either or both in a free consultation.