Enterprise RAG projects face a 65 percent accuracy ceiling because PDFs and Word files flatten tables, break cross-references and cause definitions to drift, according to the article. It describes a language model reviewing a 47-page contract that quotes a clause that doesn’t exist and misses that a defined term was amended 40 pages later, attributing the problem to the document rather than the model. The article identifies three failure modes: collapsed tables, where standard parsers flatten data-dense tables into pipe-delimited rows; broken cross-references, where keyword-based retrieval misses context when definitions are modified by amendments dozens of pages later; and silent definition drift, where outdated document versions remain in vector indexes. It says Gartner and IDC put unstructured data at 80 to 90 percent of all enterprise data, growing at 55 to 65 percent per year, three times faster than structured data. A 2024 Stanford RegLab study found general-purpose LLMs hallucinate on legal questions 69 to 88 percent of the time, while specialized, RAG-augmented, “production-ready” legal AI tools still hallucinated on roughly 17 percent of queries. Independent surveys put accuracy ceilings around 65 percent, and 70 to 80 percent of enterprise RAG projects never reach production. The article says teams must shift engineering focus from the model layer to the substrate, treating documents as structured data graphs with typed nodes, live cross-document links and deterministic consistency checks enforced at write