Private document conversion for Claude

Claude's long context is built for real documents — contracts, board papers, research archives. Which is exactly the material you least want passing through a random converter website on its way there. Convert on your Mac instead; only the text you choose reaches Claude.

The privacy chain, kept intact

When you drop a PDF or Word file into a web-based converter, the original — metadata, embedded content, all of it — lands on a third-party server before Claude ever sees a word. Milldown breaks that pattern: conversion and OCR run entirely on-device, you review the Markdown in the app, trim what shouldn't travel, and paste or drag exactly that into Claude. The original never leaves your Mac; what Claude receives is a decision, not a side effect.

Why Claude works better on Markdown

• Structure survives. Headings, lists, and tables arrive as headings, lists, and pipe tables — so "summarize section 4" and "compare the two tables" actually resolve to the right content.
• Native PDF attachment bills every page twice. Text plus a per-page image — Anthropic documents roughly 1,500–3,000 tokens per page. Markdown carries the words alone: 68–90% fewer tokens on our benchmark corpus, re-saved on every conversation turn.
• Projects and long chats stay roomy. A lean document leaves the context window for the conversation. For multi-document work, a Context Pack merges a folder into one paste-able file with a table of contents.

The workflow

1. Drop files — or a whole folder — into Milldown.PDF, Word, PowerPoint, Excel, EPUB, web pages; scans are OCR'd on-device with Apple's Vision framework.
2. Clean up and review.One click strips page furniture; the editor lets you cut anything that shouldn't be shared before it goes anywhere.
3. Drag into Claude as a real .md file.Or copy-paste. The token badge shows what the document costs before you send it.

For RAG on Claude

Building retrieval over your documents? Export heading-aware chunks (512–2,048 tokens, Markdown or JSONL) with provenance front matter — source path, timestamp, SHA-256 — so every retrieved chunk stays traceable to its original. The pipeline is in preparing documents for RAG.