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TokenPig converts documents into clean, structured Markdown built specifically for LLM and RAG workflows โ ChatGPT, Claude, Gemini, and retrieval pipelines.
Raw PDF, Word, PowerPoint and Excel exports carry a lot of formatting noise โ repeated headers, broken tables, inconsistent whitespace โ that eats into an LLM's context window without adding useful information. Cleaning that up manually is tedious, and general-purpose converters weren't built with token efficiency in mind.
Researchers, consultants, and students who regularly paste documents into ChatGPT or Claude and want cleaner, cheaper context โ plus developers and teams building retrieval-augmented generation pipelines who need reliable document-to-Markdown conversion, with an API for automation.
Compared to open-source libraries like MarkItDown or Docling, TokenPig is built for people who want a ready-to-use tool with no setup, along with visibility into token savings โ while still offering an API for teams that want to integrate it into their own pipeline.
Mark2PDF TokenPigNo features have been listed yet.
TokenPig's answer:
TokenPig started from a recurring frustration: pasting PDF or Word exports into an LLM and watching layout noise โ repeated headers, broken tables, stray whitespace โ burn through the context window before the actual content even got read. TokenPig was built to solve that specific problem: clean, structured Markdown output plus visibility into the tokens saved.
TokenPig's answer:
Two main groups: individuals who regularly feed documents into ChatGPT or Claude and want cleaner, cheaper context (researchers, consultants, students), and developers/teams building RAG pipelines who need reliable document-to-Markdown conversion via API.
TokenPig's answer:
TokenPig focuses specifically on token efficiency, not just format conversion. Alongside clean Markdown output, it shows an estimated token savings for every conversion, so users can see exactly how much context window they're recovering before pasting a document into ChatGPT, Claude or Gemini โ something general-purpose converters don't surface.
TokenPig's answer:
TokenPig runs entirely in the browser โ no Python setup, no libraries to install, no code to maintain. That makes it accessible to non-developers (consultants, researchers, students) while still offering batch processing and an API for teams that want to automate document ingestion at scale.
TokenPig's answer:
TokenPig's answer:
Built as a modern web application using Next.js and TypeScript, with a focus on fast, reliable document processing entirely server-side โ no client installation required.
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