Improving the accuracy of document parsers does almost nothing to help the final quality of an enterprise AI system.
Enterprise RAG pipelines are often built on the assumption that if you fix the first step, the rest will follow. This benchmark proves that parsing quality has a shockingly weak correlation with the final answer generated by the AI. Most systems are factually correct but fail because they miss about 40 percent of the relevant information. This incompleteness is the true bottleneck for business AI, not the formatting of the source data. Companies should focus on information retrieval and synthesis rather than spending more money on better OCR. We are solving the wrong part of the document processing problem.
Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI
arXiv · 2604.26382
Most enterprise document AI today is a pipeline. Parse, index, retrieve, generate. Each of those stages has been studied to death on its own -- what's still hard is evaluating the system as a whole.We built EnterpriseDocBench to take a swing at it: parsing fidelity, indexing efficiency, retrieval relevance, and generation groundedness, all on the same corpus. The corpus is built from public, permissively licensed documents across six enterprise domains (five represented in the current pilot). We