Proxy-Pointer RAG introduces a novel approach to reduce wasteful entity and relationship extraction in enterprise knowledge graphs. Legal documents like credit agreements often exceed 100 pages and 500k characters, requiring expensive LLM processing for every word before graph ingestion even begins. The system leverages structural predictability in legal contracts to minimize token consumption.

Traditional optimization methods fall short. spaCy-based funnel approaches identify entity hotspots but miss relationship density, while smaller router LLMs still process entire documents. Both waste tokens on boilerplate sections lacking actionable business connections. The key insight is that contracts share similar structures across organizations, with only fractions containing meaningful graph content.

Proxy-Pointer deploys Graphability Indexing, classifying document sections by relational density rather than entity counts. High-yield sections like payment obligations receive priority LLM processing, while low-yield areas like governing law get bypassed. Ontological foundations such as subsidiary definitions remain flagged as very high priority regardless of density. This creates a business heatmap for strategic extraction routing.

Testing on Emerson, AT&T, and Texas Roadhouse credit agreements demonstrates significant cost reduction without compromising graph integrity. After initial training on a few documents, the system learns to ignore noise sections, potentially cutting extraction costs by focusing LLM resources only on content that builds valuable corporate hierarchies and business relationships.