Let LLMs read your documents with speed and accuracy

Have you ever wondered how to get large language models (LLMs) to handle complex documents? While LLMs are undoubtedly useful for document QA or RAG, they cannot natively read and process document files. To bridge this gap, we need to convert these files into LLM-readable formats. Document parsing is the process that exactly does this—transforming complex document files into formats such as HTML and Markdown. As simple as it may sound, this is not an easy problem, particularly due to the diversity of document layouts, table formats, and the variety of elements involved. However, if this is done right, we gain a significant boost in accuracy when processing documents with LLMs.

This is why Upstage has developed Document Parse, a document parser designed to enhance the performance of both RAG systems and LLMs. In enterprise environments, where parsing must meet critical demands—speed, accuracy, and usability—Document Parse excels by delivering high-quality results that ensure precise, reliable outputs from LLMs, even with complex documents.

Blazing fast with multi-page documents

Upstage Document Parse processes 100 pages in under a minute. Our tests show it is, on average, 10 times faster than competitors such as Unstructured and AWS Textract, and 4 times faster than LlamaParse, when processing documents between 50 and 100 pages with similar configuration settings.

Unmatched accuracy for the most difficult use cases

Upstage Document Parse outperforms both open-source and commercial alternatives, as demonstrated by our newly introduced benchmark, DP-Bench. According to this benchmark, Document Parse leads in accuracy metrics like TEDS (93.48) and TEDS-S (94.16), surpassing major competitors such as Google and Microsoft in document layout and table structure recognition by more than 5%.