What AI ingestion actually does
It fetches content safely, removes boilerplate, preserves semantic structure, computes token metrics and emits a format that fits retrieval, prompting and caching workflows.
Core positioningSEO landing
AI ingestion infrastructure for websites
AI ingestion is the missing layer between the open web and model workflows. It turns inconsistent browser-first pages into consistent machine-readable inputs with measurable cost savings.
Primary use
Normalize public web content into consistent, low-noise context that AI systems can index, retrieve and reason over.
Recommended flow
Fetch, clean, measure tokens, then hand consistent Markdown to agents or retrieval systems.
Next step
Use the Playground to compare raw HTML against optimized output before integrating the API.
Why this matters operationally
Without an ingestion layer, every agent or RAG pipeline ends up rebuilding extraction, normalization and token accounting on its own. That creates drift and weak observability.
FAQ
Is AI ingestion the same as crawling?
No. Crawling discovers pages. AI ingestion converts page content into a machine-friendly format that downstream systems can actually use.
Internal links
Related technical paths
Preparation layer
LLM-ready content from messy HTML
Prepare documents for models with clean headings, preserved code and lower-noise context windows.
Agent access
Reader API for AI systems
Expose web content to agents and retrieval systems through a reader-style API that prioritizes clarity over browser markup.
Reader API
URL to Markdown API for AI ingestion
Convert live pages into clean Markdown with stable token metrics and predictable output for AI systems.