RAGFlow
Deep document understanding — tables, images, multi-language
Carbon is a RAG (Retrieval-Augmented Generation) framework that helps developers connect external data sources to Large Language Models. The platform provides pre-built connectors to ingest unstructured data from any source and load it into any destination, with AI-ready data processing that chunks, embeds, and cleans content for optimal LLM performance. Carbon was designed to simplify building RAG applications with features including credentials and content encryption at rest and in transit, full SOC 2 Type II compliance, and advanced data processing capabilities. In December 2024, Carbon was acquired by Perplexity AI to enhance their enterprise search capabilities, allowing users to search through files and work messages in Notion, Google Docs, Slack, and other enterprise applications.
Core capabilities this platform advertises.
What this tool does well, and the limitations to keep in mind.
Pros
Cons
Top companies in RAG Frameworks you can use instead of Carbon (Perplexity).
RAGFlow
Deep document understanding — tables, images, multi-language
Unstructured
Ingests 65+ file formats: PDFs, DOCX, PPTX, HTML, images, emails
LlamaIndex
Data framework for LLM applications
Haystack
Modular RAG framework
Reducto
Vision parsing
Pathway
Rust-powered streaming engine — millions of data points/sec
Vectara
R2R
RAG engine
Docling
Converts PDFs, DOCX, PPTX, HTML, images to structured JSON/markdown
Chunkr
Captain
Scalable knowledge search
WhyHow
Compresr
Context compression
Side-by-side comparisons with other tools in this category.
Companies from adjacent layers in the AI stack that work well with Carbon (Perplexity).