D-RAC cuts enterprise document chunking tokens by 95.7%, cost by up to 85.6%
Yellow-AI-NLP · hf · 2026-09-22
Yellow AI-NLP presents D-RAC (Document Retrieval-Aware Chunking), tackling a core enterprise RAG pain point: ingesting heterogeneous formats (PDF, Word, slides, scans) locked in complex layouts, where rule-based extraction/OCR destroys reading order and tables, and fully agentic chunking is costly and hallucination-prone.
The pipeline: normalize any input to PDF, then a single multimodal LLM pass converts rendered pages into retrieval-optimized Markdown (tables rewritten as self-contained prose, heading hierarchy preserved). Chunking then proceeds deterministically into ID-addressable units with lightweight LLM planning over identifiers—not text—never regenerating source.
On a 236-document, 795-page PDF subset of RAG-Multi-Corpus, D-RAC converts and chunks the corpus in 72 minutes with zero errors (1,748 chunks). Versus agentic chunking with frontier LLMs, it cuts chunking output tokens by 95.7%, cost by 77.8% (GPT-4.1 pricing) to 85.6% (Gemini 2.5 Pro pricing), and time by 75%, scaling linearly to 500+ page documents.
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