Enterprise AI Accuracy: Building a More Trustworthy RAG Application

Summary

An enterprise RAG pipeline scored above 90% on standard text-retrieval benchmarks but approximately 46% on complex, customer-representative documents, with failures spanning parsing, chunking, enrichment, embeddings, retrieval, and answer generation. The proposed diagnostic traces wrong answers backward from generated response to retrieved context, chunks, and parsed source, using human-validated end-to-end evaluation and changing one stage at a time. Intelligent Parsing routes pages according to complexity and preserves relationships in structures such as multi-page tables; in a staged benchmark, it raised accuracy from 65.1% to 84.4%, while Enriched Indexing raised it to 86.8%. Retrieval improvements include dynamic metadata pre-filters for explicit constraints and GraphRAG for multi-hop evidence, with GraphRAG slated for general availability in November 2026. Across broader improvements, complex-document accuracy exceeded 90%, and service-based Just-in-Time indexing reduced sub-100 MB payload indexing from 30+ minutes to approximately seven to ten minutes.

Takeaways

1. 1

   Accuracy on complex enterprise documents increased from approximately 46% to more than 90% across broader improvements to parsing, chunking, enrichment, embeddings, and retrieval.

2. 2

   Intelligent Parsing increased staged-benchmark accuracy from 65.1% to 84.4%, and Enriched Indexing raised it further to 86.8%.

3. 3

   Dynamic metadata pre-filters enforce constraints such as product, region, or customer tier before similarity ranking, while GraphRAG supports multi-hop questions distributed across connected evidence.