Every Format, Clean Markdown

PDF is the most common document format in business — reports, contracts, whitepapers, research papers. But PDFs are notoriously difficult for AI to process. The binary format stores text in positioning coordinates rather than reading order, which means standard PDF text extraction often produces garbled output with merged columns, lost tables, and broken headings.

MDSpin extracts PDF content and converts it to clean Markdown that preserves heading hierarchy, table structure, and list formatting. Multi-column layouts are detected and reordered into linear reading flow. The result is Markdown that LLMs can parse perfectly — no more text soup from your quarterly reports.

Microsoft Word documents are the workhorse of business communication — proposals, specifications, SOPs, internal memos. DOCX files contain rich formatting in XML, but most of that formatting is irrelevant to AI processing and inflates token counts.

MDSpin converts DOCX files to Markdown while preserving the document's semantic structure: headings map to Markdown heading levels, tables become Markdown tables, and lists maintain their hierarchy. Tracked changes, comments, and formatting metadata are stripped, leaving only the content your AI needs.

Presentations contain some of the most valuable information in any organization — strategy decks, product roadmaps, client pitches, training materials. But the slide format makes them nearly impossible for AI to process meaningfully. Content is scattered across text boxes, shapes, and speaker notes with no inherent reading order.

MDSpin processes each slide sequentially, extracting text from all content areas and speaker notes. The output uses Markdown headings to separate slides and preserves bullet hierarchies. The result is a linear, readable document that captures all the information in your presentation in a format LLMs can understand.

Web content, saved pages, and exported emails often come as HTML files. While HTML preserves document structure through tags, it carries massive overhead — CSS styles, JavaScript, navigation elements, advertisements, and metadata that inflate token counts by 2-5x without adding information.

MDSpin strips HTML to its semantic core, converting headings, paragraphs, tables, lists, and links to clean Markdown. All presentational markup, scripts, and styles are removed. The result is a token-efficient representation of the content that preserves structure without the noise.

Spreadsheet data exported as CSV is one of the most common data interchange formats. But raw CSV text — comma-separated values with no visual structure — is difficult for LLMs to interpret accurately. Models struggle to associate values with column headers and often misinterpret row boundaries.

MDSpin converts CSV files to properly formatted Markdown tables with aligned columns and clear header rows. This gives LLMs the visual structure they need to accurately read tabular data, answer questions about specific cells, and perform data analysis tasks.

Plain text files have no formatting at all — no headings, no emphasis, no tables. While they are token-efficient, they lack the structural cues that help LLMs understand document organization. A 50-page plain text document is a wall of undifferentiated text to an AI.

MDSpin analyzes plain text files to detect implicit structure — lines that look like headings, content that follows list patterns, and tabular data separated by tabs or fixed widths. The output adds Markdown structure where appropriate, making the content more parseable for LLMs.

Rich Text Format files are found across legacy systems, older document management platforms, and cross-platform text editors. RTF encoding uses control codes for formatting that are completely opaque to LLMs and inflate token counts significantly.

MDSpin strips RTF control codes and converts the underlying content to clean Markdown. Formatting like bold, italic, headings, and lists is preserved through Markdown syntax while all RTF-specific encoding is removed. The result is a clean, LLM-friendly document.