When Retrieval Finally Works: Benchmarking the Next Era of Enterprise AI
By Corvic AI Research
Enterprise retrieval-augmented generation (RAG) has been the go-to approach for grounding AI in real data. But traditional RAG has a dirty secret: it works well for simple lookups but falls apart when you need to reason across multiple data types, connect disparate sources, or handle nuanced enterprise queries.
We set out to quantify this gap. Our team benchmarked Corvic's Mixture of Spaces (MoS) technology against leading RAG implementations across five representative enterprise scenarios: multi-source customer analytics, supply chain anomaly detection, compliance document review, cross-functional reporting, and real-time operational intelligence.
The results were striking. MoS outperformed traditional RAG by 30-45% on accuracy across all five benchmarks, with the gap widening on tasks requiring cross-modal reasoning — exactly the kind of tasks enterprises actually need.
The key insight: it's not about better retrieval. It's about better representation. When you embed diverse data types into a unified space, reasoning becomes composition rather than search-and-stitch.