AI Model
Training Data

Teams use Firecrawl to build domain-specific pre-training sets, instruction and Q&A datasets for fine-tuning, reinforcement learning from human feedback (RLHF) preference data, and evaluation sets derived from real docs and sites. The pattern is simple: define domains and paths, crawl with Firecrawl, then turn the structured output into your preferred training format.

Each Firecrawl job can be scoped by domain, path, and custom filters so you only ingest the sources you’ve explicitly allowed. Because those settings live in code or config, you can review and diff what changed any time someone updates the training data surface.

Yes. You can schedule Firecrawl jobs to run daily, weekly, or on any cadence you need. Many teams refresh key domains regularly so their fine-tuned models and evaluation sets reflect the latest docs, policies, and product changes without one-off scraping runs.

Firecrawl exposes a simple HTTP API and SDKs. Most teams add a data collection step at the start of their pipelines that calls Firecrawl, writes structured outputs to object storage, and then hands those files to their existing preprocessing and training jobs.

Firecrawl is designed to respect robots.txt and standard web crawling conventions. You still own the decision about which sites to include and are responsible for making sure your AI training use complies with each site’s terms and any regulatory requirements.

The world's most comprehensive web data API. Our custom browser stack and semantic index deliver superior data quality across any website, handling more content types and edge cases than any competitor.

JavaScript rendering, dynamic content, and robust request handling built-in.

Process millions of pages with automatic rate limiting, caching, and distributed infrastructure.

Optimized scraping engine with parallel processing and smart caching for instant results.

Comprehensive docs, SDKs for all major languages, and dedicated support to help you succeed.