Cost controlSEO landing

Reduce HTML token usage before AI processing

HTML is designed for browsers, not prompt budgets. Every repeated menu, hidden widget and script-adjacent fragment inflates costs without improving model performance.

Primary use

Shrink token spend by converting bloated HTML into compact Markdown before chunking, prompting or embedding.

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.

Where token waste comes from

The biggest cost drivers are usually repeated layout shells, deeply nested wrappers and duplicated CTA blocks. These structures can dominate token counts on modern documentation and marketing pages.

Global navigation rendered on every page.
Recommendation modules unrelated to the requested topic.
Inline styling, ARIA wrappers and tracking-heavy markup.

How to measure savings

Use the same tokenizer before and after conversion. AIngestor uses deterministic token metrics so savings are comparable across repeated crawls and API requests.

Internal links

Related technical paths

Open Playground

Token efficiency

Context optimization for LLM retrieval

Reduce noisy HTML and preserve semantic structure so every prompt and chunk carries more useful signal per token.

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.

Agent browsing

Prepare browser agent content for model use

Give browser agents compact page context instead of forcing them to reason over raw DOM noise.