🤖 LLM Training

Foundation and product teams need domain-specific corpora to fine-tune large language models, but the open web is noisy, unstructured, and riddled with licensing traps. Leading scraping platforms combine resilient extraction with opt-out tooling, deduplication, and automated redaction so AI teams can gather content responsibly. This allows researchers to focus on prompt design, evaluation, and alignment rather than data janitorial work.

A production-ready pipeline spans several stages. Discovery jobs find fresh URLs, crawlers capture raw HTML or rendered text, and enrichment layers classify content, remove PII, and attach metadata such as quality scores. Structured delivery—Parquet files, embeddings, or vector-ready chunks—slots directly into model training workflows. Providers with managed delivery can even stream curated datasets on a recurring cadence so experiments stay reproducible.

Governance is critical. Maintain audit trails for every source, record licensing status, and respect robots exclusions or explicit opt-out endpoints. Combining self-managed actors with managed data services gives teams the flexibility to explore new domains without compromising legal posture.