Converting documents to Markdown and JSON at scale — A modern approach for AI/LLM practitioners

After some years of developing AI applications, I faced a common challenge: transforming amounts of content from various sources into uniform formats suitable for AI pipelines. Whether working with Word documents, web pages, or spreadsheets, each project required a dependable conversion process that preserved the original structure.

Most devs begin with open-source Python libraries, such as pdf2text for documents or BeautifulSoup for web scraping. While these tools are effective for small projects, they quickly become inadequate when scaling to thousands of documents or websites. Web content, in particular, poses challenges—different sites utilize diverse HTML structures, employ dynamic loading, and feature complex layouts, making consistent extraction difficult.

The complexity arises when processing needs to scale. Scraping multiple websites simultaneously requires careful rate limiting and effective error handling. Furthermore, documents must have a consistent format for AI training, but web pages and Word files often contain mixed content types that require special attention.

Through the implementation of different document processing pipelines, I have identified several critical factors. Caching processed content helps to prevent redundant processing, which is particularly important for frequently accessed web pages. Parallel processing is important when managing large volumes of documents. Maintaining the semantic structure of the content—whether from PDFs or websites—ensures that the output remains meaningful for AI apps.

After repeatedly solving this problem, I developed a cloud-based approach that focuses on automated format handling and consistent output generation. This approach evolved into Monkt (https://monkt.com/), which manages both document conversion and web content processing.

Looking ahead, the infrastructure for document and web content processing continues to evolve alongside AI systems. As language models advance, they require increasingly structured and consistent input data. Teams developing AI applications need robust processing pipelines capable of handling diverse content sources while maintaining quality and scalability.

The key is to build systems that not only address current document processing needs but can also adapt to future changes in content formats and AI requirements. Whether you are building a knowledge base, training AI models, or developing content-based applications, investing in reliable document processing infrastructure is crucial for long-term success.