Spend context on content
+Cleaner Markdown cuts layout noise before the document reaches your model. On the supplied financial-PDF example, an estimated 15K-token input lands near 4.5K after conversion.
+diff --git a/.github/workflows/deploy-pages.yml b/.github/workflows/deploy-pages.yml new file mode 100644 index 0000000..6f979d4 --- /dev/null +++ b/.github/workflows/deploy-pages.yml @@ -0,0 +1,37 @@ +name: Deploy MDify Pro OnePage to GitHub Pages + +on: + push: + branches: ["main"] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: "pages" + cancel-in-progress: true + +jobs: + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Configure GitHub Pages + uses: actions/configure-pages@v5 + + - name: Upload static site + uses: actions/upload-pages-artifact@v3 + with: + path: "." + + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/assets/mdify-icon.png b/assets/mdify-icon.png new file mode 100644 index 0000000..b9cc4f7 Binary files /dev/null and b/assets/mdify-icon.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..79f2c0b --- /dev/null +++ b/index.html @@ -0,0 +1,356 @@ + + +
+ + + + + + + + + + + + + + + + +Drop in the document you already have. MDify Pro returns clean Markdown with headings, tables and useful structure intact, ready for RAG ingestion, Git or the next prompt.
+No sign-up. No watermark. Open source from line one.
+ + + +Drop files herePDF, DOCX, PPTX, XLSX and more
+# Q4 Financial Review
+
+## Revenue
+Revenue reached $48.2M across...
+
+| Region | ARR |
+| North America | $31.8M |
+| Europe | $10.7M |
+ The case for Markdown
+PDF layouts and exported HTML can drag formatting debris into an LLM pipeline. MDify Pro strips the wrapper and keeps the parts you meant to send: headings, paragraphs, lists and tables.
+Cleaner Markdown cuts layout noise before the document reaches your model. On the supplied financial-PDF example, an estimated 15K-token input lands near 4.5K after conversion.
+Headings stay headings. Lists stay lists. Tables stay readable. Your chunker gets cleaner boundaries and retrieval starts with less junk to untangle.
+Markdown is plain text. Diff it, search it, review it in a pull request, feed it to a static-site generator, or store it next to the code it documents.
+Token estimates change by document and tokenizer. The point is the direction: removing layout metadata can shrink what reaches the model without stripping the document's meaning.
+ Read the full use case +Who it's for
+You already have the documents. MDify Pro gets them into a format your editor, repo, CMS or retrieval pipeline can work with.
+Turn a stack of PDFs and office files into cleaner input for RAG ingestion, context windows and model prep.
Convert specs, runbooks and wiki exports once. Commit the Markdown and review documentation with the same Git workflow as code.
Pull structured text from papers without copy-paste cleanup. Keep headings and tables useful for notes, citations and search.
Move Word docs, slide decks and HTML into Markdown before they hit Ghost, Jekyll, Notion or your CMS workflow.
Turn spreadsheets and CSV files into readable Markdown tables for READMEs, reports, issue threads and internal docs.
Convert policy files, contracts and old documentation into plain text your knowledge base can index and your team can search.
Many formats in, one format out
+The converter handles office files, web exports, tabular data and structured text through the same pipeline. Scanned PDFs are flagged instead of being passed off as good output.
+Text-native extraction
Document structure
Slides and notes
Rows to tables
Structured Markdown
Tabular Markdown
Structured data
Clean and analyze
Metadata today, OCR next
Three steps
+Mix supported formats in the same batch. The app validates each upload before conversion starts.
+Standard keeps the source close. Clean strips leftovers. Compact cuts noise. RAG-ready reshapes the output for model ingestion.
+Read the Markdown inline with word, character and token estimates. Copy one result or download the whole batch as a ZIP.
+Under the hood
+The frontend and backend deploy independently. MarkItDown sits behind an adapter, so another processor can slot in later without forcing the interface to change.
+Vercel hosts the typed frontend, file queue, output views, backend status handling and Liquid Glass UI.
+Render hosts the Python service. Files cross validation first, pass through the converter adapter, get normalized, and return as typed JSON.
+The current converter stays lean. Heavier work moves behind clean seams as the product grows.
+Open source from line one
+Try it on a document you already use. If the output belongs in your pipeline, keep it. If you want to see how it works, the repository is open.
+