MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

MM-BizRAG introduces a structured approach to multimodal retrieval-augmented generation for enterprise document analysis, dynamically routing documents through layout-specific processing pipelines and outperforming existing vision-centric baselines by up to 32% on heterogeneous enterprise datasets. The system decouples retrieval from generation contexts and introduces FastRAGEval, a cost-efficient evaluation metric for RAG system quality assessment.

MM-BizRAG addresses a fundamental limitation in current multimodal RAG systems: their tendency to treat all document types uniformly through minimal parsing and page-level image processing. This approach, while computationally efficient, loses critical structural information embedded in enterprise documents like financial reports and presentations. The research demonstrates that explicit handling of document layout significantly improves answer quality and grounding.

The enterprise document processing landscape has evolved toward faster, less computationally expensive solutions, often sacrificing accuracy for speed. MM-BizRAG's innovation lies in its orientation-aware routing mechanism—vertically structured documents receive explicit layout-aware parsing while horizontally structured documents leverage holistic representations. This hybrid strategy balances efficiency with precision, maintaining natural reading order through LLM-driven artifact transformation and placeholder-based alignment.

For enterprise AI practitioners and organizations deploying RAG systems at scale, MM-BizRAG's 32% performance improvement over baselines represents significant competitive advantage in document understanding and question-answering accuracy. The introduction of FastRAGEval addresses a practical pain point: evaluating RAG system quality efficiently without manual annotation overhead. By halving evaluation costs while improving human alignment, organizations can iterate faster on RAG pipelines and achieve production-ready systems more economically.

The research trajectory indicates continued specialization in domain-specific RAG architectures. Future developments will likely focus on adaptive routing mechanisms that automatically select optimal processing strategies per document, and expansion of evaluation frameworks to handle increasingly complex document heterogeneity. Enterprise adoption of these techniques could reshape how organizations extract structured insights from unstructured document repositories.

• →MM-BizRAG achieves up to 32% performance improvement over state-of-the-art baselines through structure-aware document processing
• →Dynamic routing system applies layout-specific ingestion pipelines based on document orientation and structure type
• →FastRAGEval metric reduces RAG evaluation costs by 50% while maintaining stronger human alignment than existing approaches
• →Decoupled retrieval and generation contexts enable richer, grounded answers without model finetuning requirements
• →Strong performance gains demonstrated on report-style layouts and two public benchmarks including FinRAGBench-V