Problem

Context.dev addresses the infrastructure gap between AI agents and the live web. Developers need fresh, structured information from websites, but building and maintaining crawlers, JavaScript rendering, anti-bot handling, data pipelines, and vendor integrations is costly engineering work that sits outside the core product. Without this layer, agents rely on stale model knowledge; with it, they can access current web content when a task requires it.

The clearest use case is automated product enrichment that must work instantly and at scale. Mintlify, for example, needed to turn arbitrary GitHub repositories into branded documentation sites, automatically retrieving the correct logo, colors, and visual identity for each project. Doing that manually would undermine the product's no-configuration promise, while Context.dev lets Mintlify handle the brand data through an API and focus its engineering effort on the documentation product.

Product / Service

Context.dev is a self-serve, metered API for live web context. A single REST interface can scrape a URL into clean, LLM-ready Markdown or HTML, crawl sites and sitemaps, render JavaScript, extract schema-validated or typed data, and retrieve brand information such as logos, colors, fonts, and social profiles. It also includes stealth capabilities such as anti-bot bypass and premium proxies without separate surcharges, with SDKs available for common programming languages.

The service is delivered as a usage-based SaaS product: developers can start with 500 free credits, while paid Developer, Pro, and Scale plans cost $25, $149, and $499 per month respectively, with enterprise plans for higher volumes and procurement requirements. This model replaces internally operated scraping infrastructure and multiple specialist vendors with one integration; simple page scrapes cost one credit, while richer extraction and brand operations consume more credits.

Market

Context.dev competes in AI developer infrastructure and web-data APIs, spanning managed web scraping, LLM context retrieval, structured extraction, and brand-enrichment APIs. Its comparison set includes Firecrawl, ScraperAPI, ScrapingBee, Spider, Browse.ai, Jina AI, Apify, Crawl4AI, Tavily, Exa, and other scraping or search providers, as well as Brandfetch, Logo.dev, Clearbit, Klazify, RiteKit, and Parqet in brand and logo data. Its positioning is to combine live, LLM-ready web data with brand intelligence in one managed API rather than requiring teams to assemble or self-host those components.

The company appears to be an early but commercially launched startup rather than a purely pre-product venture: it has public paid plans, a free developer funnel, named users including Mintlify, Super, Vizzy, and Klarna, and a Y Combinator profile identifying it as an active Summer 2026 company founded in 2025. Public traction claims are not fully consistent—Y Combinator cites 280 other companies, while Product Hunt says the product is trusted by 5,000+ businesses—so the scale should be treated as company-reported adoption rather than independently verified revenue. No revenue figure is disclosed in the available sources.