Preparation layerSEO landing

Turn messy pages into LLM-ready content

LLM-ready content is readable by both humans and machines: concise, hierarchical and free of site chrome that competes with the actual answer surface.

Primary use

Prepare documents for models with clean headings, preserved code and lower-noise context windows.

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.

Characteristics of model-friendly documents

Good model inputs preserve document hierarchy. Headings define scope, paragraphs hold explanation, and code or tables stay intact when they carry implementation detail.

Predictable structure for chunkers and retrievers.
Reduced prompt waste from decorative markup.
Better diffs when pages change over time.

Why Markdown is a strong interchange format

Markdown is compact, easy to store, easy to compare and readable without a browser renderer. That makes it a reliable boundary between web content and AI pipelines.

Internal links

Related technical paths

Open Playground

Core positioning

AI ingestion infrastructure for websites

Normalize public web content into consistent, low-noise context that AI systems can index, retrieve and reason over.

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.

Chunk quality

Semantic chunking starts with clean structure

Chunk by meaning instead of arbitrary token windows by preserving headings, lists and tables before segmentation.