# EY Re-envisions RAG Around Multimodal Knowledge Graphs to Improve Accuracy

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-07-17T17:54:01.000Z  
> **Category:** RESEARCH  
> **Impact Score:** 160/100  
> **Primary Source:** [SiliconANGLE](https://siliconangle.com/2026/07/17/ey-re-envisions-rag-around-multimodal-knowledge-graphs-improve-accuracy)  
> **Canonical Citation:** [https://spidits.com/timeline/ey-re-envisions-rag-around-multimodal-knowledge-graphs-to-improve-accuracy](https://spidits.com/timeline/ey-re-envisions-rag-around-multimodal-knowledge-graphs-to-improve-accuracy)

## Executive Summary
Retrieval-augmented generation is a standard way to ground large language models in enterprise information, but new research from EY, the business name of Ernst & Young LLP, says most implementations overlook a lot of useful data.

## Why It Matters (Strategic Analysis)
Crucially, this shifts enterprise AI dependency away from text-bound architectures toward unified multimodal indexing. As a result, systems can synthesize insights from complex technical diagrams and financial tables.

## Referenced Coverage & Sources
- **[SiliconANGLE](https://siliconangle.com/2026/07/17/ey-re-envisions-rag-around-multimodal-knowledge-graphs-improve-accuracy)**: EY re-envisions RAG around multimodal knowledge graphs to improve accuracy — _Retrieval-augmented generation is a standard way to ground large language models in enterprise information, but new research from EY, the business name of Ernst & Young LLP, says most implementations overlook a lot of useful data. Conventional RAG systems are built mainly to retrieve text..._

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