How Box is unlocking multimodal enterprise agents with Gemini Embeddings 2
Enterprise content management is experiencing its biggest architectural shift since the cloud migration era.
For years, enterprises have stored trillions of gigabytes of critical data in Box: financial models, clinical trial protocols, M&A due diligence rooms, engineering schematics, and legal compliance playbooks. Up to this point, text-based search and retrieval-augmented generation (RAG) have successfully unlocked the vast narrative knowledge within these repositories, establishing a powerful and highly effective baseline for enterprise AI intelligence.
Traditional RAG architectures have mastered text processing, but the agentic era demands more. The next logical evolution is to extend this framework to capture theinherently multimodal, deeply spatial, and highly structured elements that exist alongside text. While text embeddings excel at indexing prose, multimodal architectures unlock a major new capability: For example, they preserve the strict row-column semantics of financial tables, interpret visual evidence like clinical data, and map the logic of multi-page flowcharts without losing...
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