• Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. • Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. • Retrieval-augmented generation (RAG) was created to ground LLMs in trusted enterprise knowledge-retrieving relevant source data at query time to reduce hallucinations and improve accuracy. • But if a RAG system processes only surrounding text, it misses key signals embedded in tables, charts, and diagrams-resulting in incomplete or incorrect answers. • An intelligent agent is only as good as the data foundation it’s built on. • Modern RAG must therefore be inherently multimodal-able to understand both visual and textual context to achieve enterprise-grade accuracy.

Article Summaries:

  • Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. Retrieval-augmented generation (RAG) was created to ground LLMs in trusted enterprise knowledge-retrieving relevant source data at query time to reduce hallucinations and improve accuracy. But if a RAG system processes only surrounding text, it misses key signals

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