Semantic Search

What It Does

Replace keyword search with AI-powered semantic search. Users type questions in natural language — the search understands meaning, not just matching words. Returns ranked results with relevance explanations from your uploaded content corpus.

"How do I reset my password?" finds your account recovery docs even if they never mention "reset" or "password" together.

How It Works

1. Upload your content — documents, articles, product data, FAQs, or any searchable text
2. User searches in natural language through your app or API
3. Vector similarity ranks all content by semantic relevance to the query
4. AI-enhanced results with relevance scores and context snippets explaining why each result matches

Use Cases

• Documentation search — developers find answers to "how do I..." questions without knowing exact terminology
• Knowledge bases — support agents find relevant past tickets and resolution steps from natural descriptions
• Product catalogs — users describe what they need instead of guessing the right filter combination
• Research platforms — search academic papers, reports, and datasets by concept rather than keyword

vs. Elasticsearch/Algolia

Traditional search engines index words. Semantic search indexes meaning. A query for "annual revenue" matches documents about "yearly income" and "fiscal year earnings." No synonym dictionaries needed, no query expansion rules — understanding is built in.