Parallel
Web infrastructure for AI to search, extract, monitor, and reason over the world's info.
Parallel is a web search and retrieval platform that gives AI agents and applications access to fresh, cited information from the public web. It addresses the problem of models relying on training-time knowledge by providing real-time search, content extraction, monitoring, deep research, and structured data enrichment through APIs, all served by a proprietary web index built for agent workloads.
The platform offers six APIs: Task for deep research and structured enrichment, Search for ranked URLs with compressed excerpts, Extract for fetching page contents including PDFs and JavaScript-heavy pages, Responses for cited answers in seconds (OpenAI-compatible), Monitor for tracking changes and sending alerts, and FindAll for building structured datasets from queries. Outputs can include citations, reasoning, confidence scores, and excerpts, with provenance attached through the Basis system. The APIs are designed to compose through a single interface.
Parallel is aimed at developers and teams building AI agents, copilots, and products that need current web context, as well as workflows in finance, sales, legal, and life sciences. Pricing is pay-as-you-go per request, with a free tier covering up to 5,000 requests per month and an Enterprise edition offering features such as zero data retention.
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