Exa
Company Information
Headquarters
San Francisco, California, USA
Year Founded
2021
Industries
Artificial Intelligence, Search Technology, Developer Tools, Data Infrastructure, Enterprise Software, Research
What Is Exa?
Exa is an AI infrastructure company developing a search engine and web data platform built for AI systems. Its technology gives AI agents, applications, and developers programmatic access to current information from the web through search, content retrieval, and research APIs. Exa operates its own large-scale web index and combines semantic search and retrieval models with web crawling and structured data capabilities designed for machine consumption.
What Does Exa Do?
Exa provides infrastructure that allows AI applications to search, retrieve, and process information from the web. Developers can integrate Exa’s APIs into AI agents, retrieval-augmented generation systems, research tools, coding agents, and other applications that require current external information.
Rather than relying exclusively on traditional keyword-based search results, Exa supports semantic retrieval that can identify webpages based on the meaning of a query. Its platform can also retrieve webpage contents, conduct deeper multi-step research, and return structured information that downstream AI systems can process. This gives developers a web-information layer for grounding AI models with current data.
Exa Technology
Exa has developed an independent web-scale search index rather than operating simply as a wrapper around an existing search engine. The company says its infrastructure tracks more than one trillion URLs and serves tens of billions of documents through its vector database.
Its search architecture combines semantic retrieval with traditional keyword techniques. Exa trains embedding and retrieval models that represent webpages and queries mathematically, allowing its systems to identify information based on semantic relevance rather than exact keyword matches alone.
The infrastructure also includes web crawling, content extraction, vector search, and agentic retrieval. Exa 2.0 introduced different retrieval modes optimized for speed and search depth, including a deeper mode that can iteratively search and process information. Exa also extracts relevant portions of webpages so AI models can receive token-efficient web context instead of processing complete pages unnecessarily.
Why Exa Matters
As AI systems become more agentic, they increasingly need a way to obtain information that is current, external to their training data, and suitable for machine processing. Search therefore becomes infrastructure for AI rather than simply an interface used directly by people.
Exa is relevant to this shift because it is building both the search technology and underlying web index specifically around AI retrieval workloads. Its combination of semantic search, web-scale indexing, and agent-oriented APIs illustrates an emerging layer of the AI stack: systems that connect language models and autonomous agents to continuously changing information on the web.
Exa Products & Platforms
Search API
The Search API gives developers programmatic web search for AI applications and agents. It supports semantic and keyword-oriented retrieval and can be configured for workloads ranging from low-latency searches to deeper information retrieval.
Contents API
The Contents API retrieves information from webpages returned through Exa. It can provide webpage content and relevant excerpts that applications can pass to language models, reducing the amount of unnecessary page content an AI system needs to process.
Agent API
Exa’s Agent API supports more complex research tasks in which an AI system needs to search and investigate information rather than execute a single retrieval request. It provides agentic web research capabilities for applications that require deeper information gathering and structured outputs.
Websets
Websets allows users and applications to build structured collections of web information based on specified criteria. It can find and verify entities such as companies or people, enrich results with additional information, and organize them into reusable datasets. The product is applicable to research, market intelligence, recruiting, and B2B data workflows.
Exa Applications
Exa’s technology can serve as a web retrieval layer for AI agents that need access to information beyond the data contained in a language model. Applications include retrieval-augmented generation (RAG), AI assistants, coding agents, automated research, question-answering systems, and applications that generate answers grounded in current web sources.
Its structured retrieval capabilities also support company and people research, data enrichment, and market intelligence. Websets, for example, can generate targeted collections of organizations or people based on natural-language criteria and add structured information to those results.
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Frequently Asked Questions
Does Exa operate its own search index?
Yes. Exa says it operates an independent web index rather than simply forwarding searches to Google or another conventional search provider. Its infrastructure combines web crawling, indexing, retrieval models, and its own vector database.
How is Exa different from a traditional search engine?
Traditional search engines are primarily designed to return results to human users. Exa’s APIs are designed primarily for AI agents, developers, and machine-to-machine retrieval, providing web content and structured information that software can directly incorporate into AI workflows.
Can Exa be used for RAG applications?
Yes. Exa’s search and content-retrieval capabilities can supply current external information to retrieval-augmented generation (RAG) pipelines, allowing language models to work with information retrieved from the web rather than depending entirely on their pretrained knowledge.
What are Exa Websets?
Websets are structured collections of web results built around specified criteria. They can be used to find, verify, organize, and enrich web data, including information about companies and people.
Why would an AI agent need a search API like Exa?
Language models do not inherently have complete access to current web information. A search API can give an agent up-to-date external context when it needs to research a topic, retrieve documentation, investigate companies, answer questions, or complete other tasks requiring information outside the model’s existing knowledge.
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