PDF → Markdown

PDF to Markdown you can edit, search, and retrieve.

PDFs are built for reading and sharing, not for editing, search, or RAG. We convert PDF to Markdown with headings, tables, lists, and outline intact — ready for docs, retrieval, and AI.

Convert PDF to Markdown See a sample conversion

Extract the text and the document: headings, tables, lists, links, and hierarchy — not a pile of unstructured characters.

100% local in your browser — files never leave your device. Up to 50 MB. Try a sample PDF in the converter.

RAGLLMsKnowledge basesDocsSearchWebsites

report.pdf · page 12

Confidentialp. 12

API Guide

Authentication

2.1 API Keys

API keys are…

2.2 OAuth

OAuth is…

Method	Use
Keys	Server
OAuth	Apps

report.md

# API Guide: Authentication

## 2.1 API Keys

API keys are...

## 2.2 OAuth

OAuth is...

| Method | Use |

| --- | --- |

| Keys | Server |

| OAuth | Apps |

Convert in the browser

Drop a PDF. Get structured Markdown.

No upload. Headings, tables, lists, and outline stay intact — then copy or download.

100% private — files never leave your browser

Drop your PDF here, or

click to browse

Full document 100% local Max 50MB

No PDF handy?

The problem

A PDF remembers how a page looks. Not what the content means.

PDF (Portable Document Format) was built to save and exchange electronic documents so they look the same on every device, in every app. That is a feature when you need a contract, a report, or a printed handbook. It is a problem when you need the content.

A PDF is saving “how this document should look,” not “what this document is made of.”

What a PDF is good at

Built for presentation

Trait	What it means in practice
Fixed layout	Fonts, images, tables, spacing, headers, and footers stay put. The page does not reflow when you change devices.
Cross-platform	Windows, macOS, Linux, iOS, and Android can all open it.
Print-ready	Page size and print layout hold up, which is why contracts, reports, papers, and manuals ship as PDF.
Stable distribution	Once generated, it works as a final version: read it, share it, archive it.
Complex content	A PDF is rarely “just text.” It may include images, tables, links, vector graphics, embedded fonts, and scanned pages.

Where that design breaks down

Hard for work — and for AI

Trait	Why it hurts modern workflows
Structure-unfriendly	PDF cares about the page, not the outline. Machines struggle to recover headings, paragraphs, lists, and tables.
Awkward to edit	Unlike Word or Markdown, PDF was not designed for frequent rewriting or reorganization.
Hard to parse	Embedded fonts, multi-column layouts, tables, images, and footnotes all make extraction harder.
Sometimes not text	Many PDFs are scans — images of pages. You need OCR before you can even get the words.

What you see

On the page, a PDF might look like a chapter, a section, a list, a table.

Chapter 1
Introduction

This is the introduction...

1. Background
2. Methodology
3. Results

[Table]

What software sees

A program often sees something else: strings, coordinates, fonts, and images scattered across a canvas. Recovering the real title, the paragraph boundaries, the list, and the table is not guaranteed.

PDF is for humans. It emphasizes visual layout.
Markdown is for structured content — editing, software, and AI.
Once you have Markdown, the same document is ready for search, knowledge bases, RAG, embeddings, and LLMs.

Dropping a PDF straight into a knowledge base is often the wrong first step. The model may get text. It does not get structure.

The destination

Markdown keeps the content. And makes the structure explicit.

Markdown is a lightweight markup language. A few plain-text symbols stand in for headings, paragraphs, lists, tables, links, and code. You can read the source without rendering it.

# Product Documentation

## Introduction

Markdown is simple and easy to read.

- Easy to edit
- Easy to search
- Easy to convert

What makes Markdown different

Clear structure

#, ##, - and similar markers state headings and lists outright.

Plain text

It is a text file. You are not trapped in a layout format.

Easy to edit

Any text editor works. No specialized office suite required.

Easy to read

Even the raw file is readable.

Small

Usually much lighter than PDF or Word.

Easy to search

Structured text is straightforward to full-text search.

Easy to convert

Markdown becomes HTML, PDF, Word, and more.

Program-friendly

Headings, paragraphs, and lists are explicit, so software can parse them.

Version-control friendly

Plain text belongs in Git.

Cross-platform

Windows, macOS, Linux — all of them.

AI-friendly

Structured text is easier for LLMs, embeddings, and RAG to use well.

The real reason to use it

Markdown separates content from visual layout.

PDF asks: How should this look on the page?

Markdown asks: What is this, and how is it structured?

That is not a cosmetic difference. It is a working difference.

# Product Documentation

## Installation

### Requirements

- Node 18+
- A valid API key

You do not just have words. You have a tree.

That hierarchy is what modern content workflows run on.

Product Documentation
└── Installation
    └── Requirements
        ├── Node 18+
        └── A valid API key

AI does not only need “the text.” It needs the relationships between pieces of text. For an AI or RAG system, heading levels, paragraph boundaries, lists, and tables are suddenly unambiguous.

That is why Markdown fits:

LLMsRAGVector databasesEmbeddingsAI knowledge basesAI agentsSemantic searchDocumentationContent management

If the next step is editing, retrieval, or generation, Markdown is the format that already looks like the job.

Two formats, two jobs

PDF is for delivery. Markdown is for work.

PDF saves what a document looks like. Markdown saves what a document is.

	PDF	Markdown
Core purpose	Preserve the page and visual layout	Express content and structure
Editability	★★	★★★★★
Machine parsing	★★	★★★★★
Search	★★★	★★★★★
AI processing	★★	★★★★★
RAG	A poor fit as-is	A natural fit
Version control	Awkward	Native
File size	Usually larger	Usually small
Cross-platform	Yes	Yes
Human reading	Yes	Yes
Page layout	Strong	Weak

You still need PDFs. Contracts, reports, and papers have to look the same for every reader. You need Markdown when the same material has to be reused, queried, updated, or handed to a model.

The formats are not enemies. They have different jobs. The missing piece is the conversion in between.

The product

PDF to Markdown is the missing step — not a novelty converter.

Most important material already lives as PDF. The work you want next does not: rewrite it, search it, retrieve it, summarize it, publish it. That work needs structure.

PDF — locked layout, hard for machinesPDF to MarkdownMarkdown — editable, searchable, AI-friendlyLLM / RAG / knowledge base / workflow

You are not buying “text from a PDF.” You are buying a document the rest of your stack can use.

What conversion should recover

Running textThe words, in reading order

Headings & outlineChapter, section, subsection

TablesRows and columns, not a picture of a grid

ListsItems, not leftover bullets on a canvas

LinksDestinations you can still follow

Document structureThe relationships, not only the characters

Turn your PDFs into clean, structured Markdown — ready for editing, search, AI, RAG, and more.

Convert a PDF

Choose the right format

One content pipeline. Three jobs.

Think of them as a chain, not a choice: PDF is for delivery and reading → Markdown is for editing and processing → PDF to Markdown turns existing PDFs into structured content you can actually work with.

PDF = Presentation / Distribution / Archive

When the content is done

PDF is at its best when the content needs to be shown, shared, printed, or archived without drift.

PDF’s job, in one line: What they see should match what I made.

Use	Why PDF
Contracts	Layout stays fixed for both parties and for the archive.
Reports	Charts, text, and page layout stay consistent.
Papers / research	Formal publishing and reading.
Ebooks	Chapters, figures, and page design hold.
Product manuals	People can download, read, or print.
Invoices / receipts	The form does not move.
Company documents	Formal distribution.
Course materials	Lectures and textbooks packaged as a single file.
Print	Page geometry survives the printer.
Archives	A stable final version.

Markdown = Content / Structure / Processing

When the content still has a life

Markdown is at its best when you still need to edit it, update it, search it, convert it, or let software read it.

Markdown’s job, in one line: Make the content easy to edit, manage, search, and parse.

Use	Why Markdown
Technical docs	Developers can edit it directly.
Knowledge bases	Structure is clear; organization is easy.
Blogs / writing	Fast to write; easy to turn into a page.
Product documentation	Chapters, sections, and code blocks are first-class.
AI knowledge bases	Models handle structured text more reliably.
Search	Plain text is easier to index.
RAG	Cleaner chunking, embedding, and retrieval.
Git / GitHub	Plain text is what version control is for.
Project READMEs	Native on GitHub and similar platforms.
Content conversion	One source → HTML, PDF, and other outputs.

How it works

Convert PDF to Markdown in three steps.

Upload

A report, manual, paper, handbook, SOP, or archive PDF — including files that mix text, tables, images, and links.

Convert

We recover more than a text dump: headings, lists, tables, links, and reading order, so the Markdown still has a document shape.

Use it

Edit it. Search it. Chunk it. Embed it. Publish it. Hand it to an LLM. Put it in Git.

PDFConvertstructured Markdownyour workflowConvert a PDF

Use cases

PDF to Markdown for RAG, docs, and reuse.

Primary job

AI / RAG knowledge bases

A company already has a thousand PDFs: product manuals, technical documentation, internal SOPs, contracts, research reports, training materials. The goal is an assistant that answers from those files. Dumping PDFs into the pipeline is usually not the best first move.

The pile you already have

Product manuals
Technical documentation
Internal SOPs
Contracts
Research reports
Training materials

The pipeline that works

PDF
  → structured Markdown
  → chunking
  → embedding
  → vector database
  → RAG
  → AI assistant

PDF to Markdown is the data-prep step for an AI knowledge base. Without structure, chunking cuts in the wrong places, embeddings mix unrelated sections, and retrieval returns blobs instead of answers. With Markdown, a heading is a heading, a table is a table, and a section can be retrieved as a section.

PDF to Markdown for ChatGPT & RAG

Feed a long PDF to an LLM

You have a 100-page file: 2026 Global Market Research Report. You want ChatGPT, Claude, or Gemini to summarize the report, pull key figures, analyze competitors, find market trends, and answer questions from inside the document.

What you want the model to do

Summarize the report
Pull key figures
Analyze competitors
Find market trends
Answer questions from inside the document

After conversion, the model can follow the outline instead of guessing at it. That is the difference between “paste a wall of text” and “give the model a document.”

# Executive Summary

## Market Overview
...

## Market Size
...

## Competitive Landscape

### Company A
...

### Company B
...

Papers and research

Researchers live in PDFs. Once they are Markdown, you can search the full text, extract sections, organize a research library, build a literature knowledge base, summarize with AI, run RAG over the corpus, and compare papers against each other.

Research Paper 1.pdf
Research Paper 2.pdf
Research Paper 3.pdf
Research Paper 4.pdf
…

PDF
  → Markdown
  → research knowledge base
  → AI
  → “Compare what these 50 papers
     conclude about RAG.”

The papers stay citable as PDFs. The working copy becomes something you can query.

PDF → website

A product manual that used to be a download can become a site. The PDF is a file people save. The Markdown path becomes documentation people can search, navigate, and open on the web.

PDF product manual
  → Markdown
  → HTML
  → documentation website

Content reuse

The company already has Company Handbook.pdf. It should not stay trapped in that one file. That is content repurposing: one source of truth, many outputs — because the structure finally exists independently of the page.

PDF
  → Markdown
       ├── blog article
       ├── website content
       ├── FAQ
       ├── knowledge base
       ├── AI dataset
       └── documentation

Before / after

Text extraction is not document understanding.

Before — what a PDF often is to software

Introduction
Getting started

Install the CLI...

1. Download
2. Configure

After — the same content as Markdown

# Introduction

## Getting started

Install the CLI...

1. Download
2. Configure

Lines on a page. Maybe the right order. Maybe not. Heading level is a guess. After conversion, headings, boundaries, and hierarchy are explicit — that is the difference between pulling strings off a page and recovering a document.

AI systems use that hierarchy. Search uses it. Editors use it. A PDF does not hand it to you.

After conversion

One conversion. Three directions.

Keep the PDF for people. Convert it when the next reader is an editor, a search index, a model, or a site.

PDFRead / share / print / archive

Read / shareA person reads the page

ConvertExtract content

MarkdownStructured source of truth

Human editingContent ops

AI / RAGKnowledge base

WebsiteHTML / docs site

PDF

Read / Share / Print

Markdown

Write / Structure / Process

PDF to Markdown

Extract / Convert / Reuse

Who it’s for

Built for people who inherited a pile of PDFs and need to do something next.

AI and platform teams

Turning manuals, SOPs, contracts, and training decks into RAG-backed assistants.

Researchers and analysts

Search, compare, and ask questions across papers and long reports.

Docs and content teams

Moving API PDFs, user manuals, and handbooks into Git, Notion, or a documentation site.

Anyone unsticking a PDF

Edit, extract tables, republish as a website, or reuse a file that was never meant to be a working copy.

If the PDF is finished, leave it as PDF. If the work is not finished, convert it.

What people search for

Scans, tables, RAG, chat windows, and whole folders.

These sit next to the main query. Each is a job the file has to survive — not another lecture on formats.

OCRConvert scanned PDFs with OCR

A scanned handbook is a stack of images. There is no text layer until OCR runs — and OCR alone still leaves a stream of characters. The useful path is OCR plus structure: reading order, heading levels, and tables rebuilt as tables, not as a photograph of a grid. That is how a scan becomes something you can search, chunk, or edit. Contracts, invoices, and older papers are often scans; born-digital files can skip OCR, scans cannot skip the outline. Low-contrast stamps, skewed pages, and mixed handwriting still fail and need a second look. If you index the OCR dump as-is, a knowledge base stores noise.

Open the OCR converter

RAGStructured files for RAG and embeddings

When you embed a PDF as-is, the splitter often cuts mid-table or mid-section because it never saw a heading. Retrieval then returns a blob that looks related and is not. Give the pipeline Markdown with explicit headings and real tables, and chunking can follow the outline: one section, one table, one list. Embeddings stay closer to a single topic. That is the data-prep job — not a nicer preview. You still choose chunk size, overlap, and the vector store; conversion does not replace that. It stops the model from embedding headers, footers, and page numbers as if they were the answer.

LLM & RAG-ready Markdown

StructureKeep tables and headings

Copy-paste and naive extractors flatten a two-column report into one stream: the heading is just larger type, the table is leftover spaces. Downstream you cannot sort a column or retrieve “section 4.2.” Conversion should emit Markdown headings and pipe tables, with reading order that matches how a person would read the page — including multi-column layouts when they exist. Footnotes and captions should stay attached to the block they belong to. If a table was a screenshot, OCR has to run on that image or the grid is lost. The test is simple: can you jump the outline, and can you copy a row?

Extract tables to Markdown

ChatGPTConversion vs copy-paste into ChatGPT

Pasting pages into ChatGPT, Claude, or Gemini is fine for a one-off summary. It is a weak plan for a library. The model gets a weakly ordered blob; headings and tables are guesses. You cannot version the source, re-chunk it, or hand a clean file to the rest of the team. Convert first, then prompt: the outline sits in the file, figures stay in tables, and the same Markdown can go to another model, an internal RAG, or Git. Use the chat window to ask questions. Do not use it as your only extractor — especially not for a 100-page report you will query again next month.

Convert for ChatGPT & LLMs

BatchBatch convert PDFs to Markdown

One file is a demo. Operations look like a folder: two hundred manuals, a contract archive, a paper corpus. Batch conversion should keep filenames, skip files that already have a .md, and fail visibly when a scan needs OCR or a table collapsed. Put output next to the original — or in a parallel tree — so you can diff and re-run. After that, the same corpus can be indexed, embedded, or published without someone opening each PDF. Start with a sample of ugly files (multi-column, scans, mixed tables) before you point the job at everything.

Open the batch converter

FAQ

Questions worth answering before you convert.

Why not just upload the PDF to ChatGPT, Claude, or Gemini?

You can, for a single pass. A 100-page report still lands as a weakly structured blob: headings, tables, and section boundaries are unreliable. Converted Markdown makes the outline explicit — Executive Summary, Market Size, Competitive Landscape — so summarization, extraction, and Q&A have something to hold onto. For a knowledge base, the gap is larger: RAG wants chunks with real headings, not page coordinates. Conversion still runs entirely in your browser; the file is never uploaded to us.

Why Markdown instead of Word or plain text?

Word still mixes layout into the file. Plain text throws the hierarchy away. Markdown keeps content and structure and drops the page design — which is exactly what editing, Git, search, embeddings, and LLMs need.

What do you actually extract?

Text, headings, tables, lists, links, and document structure. The point is not a raw dump. The point is a file that still has an outline. Page headers, footers, and page numbers are stripped automatically.

What about scanned PDFs?

Many PDFs are images of pages. Those need OCR before there is any text to structure. Use the built-in OCR converter, which recognizes text right in your browser. Scanned input is one of the reasons “just parse the PDF” fails in the real world.

Will multi-column layouts, footnotes, embedded fonts, and mixed tables survive?

Those are the hard cases — and they are common. They are also why a dedicated PDF → Markdown step exists. A conversion that only concatenates strings will scramble reading order and miss the outline.

Is this for one file or a whole archive?

Both. One report, so you can ask an LLM about it. A thousand manuals, SOPs, and training PDFs, so you can chunk, embed, and retrieve. Use the batch converter for folders. The product is the prep step either way — and it stays local.

Do I still need the original PDF?

Yes, when you need a stable, print-faithful, shareable original. Conversion does not replace PDF for contracts, invoices, or archival copies. It unlocks a second life for the same content.

Can I turn the Markdown back into a site or other formats?

That is one of the reasons to convert. Markdown is easy to turn into HTML, PDF, Word, docs sites, FAQs, blog posts, and AI datasets. The PDF was a download. The Markdown is a source.

Get started

Stop treating PDFs as a dead end.

PDFs are great for reading and sharing, but they're not designed for modern content workflows. Markdown makes content structured, editable, searchable, and AI-friendly.