Firecrawl's Extract with AI Agent changed how I pull structured data out of websites for my AI pipeline

Natural language prompts to pull structured data from single or multiple pages without writing complex code is what it does. You describe what you want in plain English and the agent navigates the site, finds the relevant information and returns it in the structure you specified. For sites with consistent page structures the accuracy is high enough to be genuinely useful without significant post-processing.

The Search feature retrieving clean content from top search results for a given query is the complementary capability that handles research-style data gathering rather than site-specific extraction. The Map feature generating the full URL structure before crawling is the workflow I use to understand a site's content architecture before committing to a full crawl job.

For anyone building RAG systems from web content the combination of Map, Crawl and Extract is what converts an arbitrary website into clean LLM-ready data without maintaining a custom scraping infrastructure.

What types of sites or data structures have you found Firecrawl handles well versus where you still end up writing custom scrapers?