Metaphor is an AI-native search engine that uses large language models to understand queries the way people do, not just as a list of keywords. Instead of matching exact terms, Metaphor predicts which links are most likely to answer your question based on patterns it has learned from how humans write, search, and share information on the web. This makes it especially powerful for exploratory research, content discovery, and complex questions that traditional search engines often struggle with. Developers can integrate Metaphor through a simple API to build intelligent search into their own products, knowledge bases, and workflows. Because queries can be written in natural language or even in the style of a link, Metaphor is well suited for recommendation systems, research assistants, and AI agents that need reliable external information. Metaphor focuses on relevance, context, and semantic understanding, surfacing high‑quality pages that align with the intent behind your query. It can help you find long‑tail resources, niche expert content, and up‑to‑date information that might be buried in traditional search results. Whether you’re building a new AI application, enhancing enterprise search, or just looking for better ways to explore the web, Metaphor offers a developer‑friendly, language‑model‑powered approach to search.
Build an AI-powered research assistant that pulls high-quality, relevant links for analysts, writers, or students directly inside your app.
Enhance enterprise knowledge bases with semantic search so employees can ask natural language questions and find precise internal documentation.
Power AI agents and chatbots with reliable web retrieval, enabling them to browse, ground answers in sources, and stay up to date.
Create content discovery or recommendation features that surface niche blogs, expert articles, and long-tail resources users would otherwise miss.
Replace or augment traditional keyword search in your product with LLM-based search to improve relevance and user satisfaction.