A new method tackles the enterprise RAG chunking problem while cutting cost by 95.7%. Title: D-RAC: Document Retrieval-Aware Chunking URL: 📌 Overview D-RAC is a four-stage pipeline that normalizes messy enterprise documents (PDF, DOCX, PPTX, scans) into PDF, converts them once with a multimodal LLM into retrieval-optimized Markdown, then plans chunks deterministically over element IDs. ❗ The problem Rule-based extraction destroys tables and heading hierarchy, while accurate agentic chunking regenerates the entire document, making it expensive at scale. 🛠️ Method Tricks like turning each table row into a self-contained prose sentence, reconstructing heading hierarchy, and passing only element IDs to the planning LLM keep both retrieval quality and cost efficient. 📊 Results Across 236 documents and 795 pages, D-RAC cut output tokens by 95.7% while Recall@6 reached 0.798, beating agentic chunking (0.795) and rule-based extraction (0.717), with 75% less processing time. 🏢 Use cases Performance stayed stable across automotive, banking, cloud, and other enterprise domains, making it a strong fit for production RAG pipelines. #RAG# #DocumentAI#

One of Europe's leading telecommunications companies processes thousands of payment orders and payment forms every month. Until recently, every single one was handled manually. Staff reviewed each document individually, extracted transaction details by hand, categorized payment types, and entered data into downstream systems. As volume grew, the team couldn't keep up without adding headcount. Errors accumulated. Rework was constant. Compliance exposure grew alongside it. The technology to fix this existed. The infrastructure to connect it to their actual documents didn't. That's where we came in. We deployed Document AI and Decision AI on their existing infrastructure, no rip and replace, no migration project, no disruption to live operations. The AI agents took over document classification, field extraction across transaction details, payer information, and payment amounts, and routing to downstream systems. Built around their document types. Integrated with their existing stack. The results came immediately: 50%+ reduction in processing time across payment orders and payment forms. 93%+ classification accuracy significantly above the manual baseline. Fewer errors, less rework, and a compliance posture that no longer depended on individual staff not having a bad day. More importantly: the foundation is now in place to expand automation across additional document types as volume grows without expanding the team proportionally. This is what scalable AI looks like in practice. Not a pilot that produces a dashboard. A deployed system that changes the economics of a core operation. #DocumentAI# #AgenticAI# #PaymentAutomation# #FinancialServices# #AI#

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Documentary filmmaker Alex Holder who spent the last few years following Sen. Lindsey Graham joins “The Takeout” to discuss his experience and share footage of rare access with the late senator. “I think he had a enormous impact in geopolitical issues, far more than people actually think,” Holder said.

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