Advanced RAG

Content Intelligence extracts, chunks, and enriches diverse data formats and modalities so that developer teams can focus on building advanced and customized RAG solutions tailored for their use case.

The problem

In production RAG implementations, engineering teams often encounter the need to construct custom data pipelines and prompt chains. However, enterprise data in its raw state lacks proper structure and necessary metadata, hindering these efforts. To tackle these challenges, engineering teams are forced to undergo a slow and often manual process of data extraction and enrichment. This diversion from the main business case and the slow pace of progress frequently results in unsuccessful RAG initiatives.

The solution

Content Intelligence takes care of data processing and extraction and feeds your RAG pipeline with well-structured, semantically-chunked and metadata-enriched information.

[ Steps to solution ]

Content Intelligence simplifies integration and chunking processes through its 15 pre-built connectors and format specific semantic content chunking. This allows teams to seamlessly integrate content from various enterprise data sources and modalities into their RAG pipeline.

Content Intelligence automatically enriches processed data through techniques such as topic modeling, hierarchical content chunking, metadata extraction and textual image and table representation. By evaluating content quality and flagging potential risks like conflicting or outdated information, Content Intelligence ensures data reliability. Leveraging these enrichments and scores, developer teams can implement advanced RAG pipelines featuring intent-based content retrieval and filtering, as well as conversation-specific augmented generation.

Content Intelligence adopts an API-first approach and provides thorough developer documentation and SDKs for Python and Javascript, facilitating integration with any VectorDB and LLM framework. This ensures Content Intelligence easily fits into your RAG tech stack.