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One API to scrape, enrich, and extract the internet - Context.dev is the web context API for AI products and agents. Scrape any URL, crawl sites, turn pages into LLM-ready Markdown, extract structured data into your own schema, capture screenshots, and retrieve logos, colors, fonts, styleguides, company data, and…
AI product teams waste hours building and maintaining scrapers, parsing HTML, and cleaning data for LLM context. Context.dev offers one API for scraping, crawling, Markdown conversion, structured extraction, and enrichment in minutes.
Engineering teams at AI-first startups (5-50 engineers) building agents, chat interfaces, or research tools that need web data to ground responses, enrich user inputs, or automate workflows. Primary buyer is technical founder or VP Engineering with budget authority. User is backend or AI engineer shipping features weekly. Pain: current solutions require stitching together 3-5 vendors (scraping, proxy rotation, parsing, enrichment APIs) with inconsistent schemas, rate limits, and quality, costing 8-15 hours per integration and $500-2000/month across tools.
5 competitors are named in the Pro brief, with positioning and the gaps they leave open. Hobby ($19) opens the solution, market signals, and go-to-market plan.
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Analysis, scores, and revenue estimates are for educational purposes only and are based on AI models. Actual results may vary depending on execution and market conditions.
Agencies and platforms struggle to operate 5–100+ web properties: deployments, updates, analytics, and compliance become manual and error-prone. A hub that centralizes orchestration, observability, and AI-assisted automation solves scale pain and reduces ops cost.
Mobile titles lose DAU and revenue to backend latency, poor autoscaling, and costly live‑ops. An AI-first backend optimization platform auto-tunes infra, predicts load, and reduces TCO for studios and publishers.
Voice leads slip through CRMs and call logs. Provide an API first phone system that captures, transcribes, scores and routes calls so developers embed qualification into workflows.
Developers re-explain project context every AI session. Build a persistent, encrypted memory layer that works across IDEs, chats, and browsers so tools remember intents, state, and preferences.
Scientific benchmark tasks are few and shallow because defining correctness needs domain expertise. Offer a platform of expert-curated, reproducible benchmarks + evaluation pipelines for hard, open-ended scientific problems.
Checkout/payment flows in delivery apps break frequently; automated AI-first end-to-end tests + live observability pinpoint and auto-heal checkout breakages before customers notice.
