AI Engineering & RAG
Clean Inputs Produce Sharp AI Outputs
Garbage in, hallucination out. When you feed raw HTML or clumsy PDF copies into modern reasoning LLMs, 50% of your prompt window is consumed by CSS rules, broken hyphens, and repeated copyright notices.
Interactive Tool
PDF to Clean Markdown
Converts technical whitepapers, legal agreements, and corporate reports into clean GitHub Flavored Markdown. Strips headers and page footers automatically.
Open Cleaner → Developer API
Web to Markdown CLI
Feed a URL and get pure article Markdown back with zero ad bloat. Built for autonomous agents, Cursor background indexing, and RAG vector databases.
Explore API Plans →Token Consumption Benchmarks (100-Page Document)
Based on standard benchmark tests feeding SEC 10-K filings into Claude 3.7 Sonnet ($3.00/M input tokens):
| Ingestion Method | Prompt Token Count | Estimated Cost / Run | Context Window Efficiency |
|---|---|---|---|
| Raw PDF Ingestion | ~142,000 tokens | $0.426 | Baseline (Heavy noise) |
| Unsanitized HTML Scrape | ~185,000 tokens | $0.555 | -30% (CSS/DOM Bloat) |
| BytePlain Clean Markdown | ~68,000 tokens | $0.204 | +52% Savings (High Signal) |