The table extraction it generates is well structured, and I was able to get results in an easy-to-apply style. It supports Excel and provides a copy-to-paste Markdown format for Claude, plus great summary options to condense a 40-page document down to 2.

Support for different file formats is my favorite part, especially since it handles presentations and images. I also like being able to extract information from PPTs, get structured output, and export the results to other platforms.

In my work evaluating AI frameworks and LLM pipelines, LlamaIndex stands out for making Retrieval-Augmented Generation (RAG) implementation fast and seamless. Its data connectors (LlamaHub) make it easy to ingest custom documents such as PDFs and Markdown files. The Python and TypeScript SDKs integrate naturally into typical developer workflows, and onboarding is quick thanks to the excellent documentation. I also appreciate the advanced indexing strategies, which help deliver strong retrieval performance. Because it’s open source, it offers exceptional ROI for both prototyping and production RAG applications.