TokenPig
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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.
TokenPig DoclingNo 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.
Based on our record, Docling seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
If you have not seen docling yet, it is IBM's document processing library. PDF, DOCX, PPTX, scanned images, tables, the whole lot โ out comes structured output. Very good at its job. The problem is not docling. The problem is everything around it. - Source: dev.to / 4 months ago
OCR was the obvious option and with so many opensource libraries available, we were spoilt for choices. I Wanted to use Docling as my prior experience with it has been good so Far (I shall write a separate blog on those use-cases) but we were constrained by the infra. - Source: dev.to / 5 months ago
Granite Docling is a multimodal Image-Text-to-Text model engineered for efficient document conversion. It preserves the core features of Docling while maintaining seamless integration with DoclingDocuments to ensure full compatibility. - Source: dev.to / 11 months ago
Docling* works pretty well in PDF hell, but is terribly slow. *https://docling-project.github.io/docling/. - Source: Hacker News / about 1 year ago
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