Researchers have developed TADI, an agentic AI system designed to analyze heterogeneous wellsite data for drilling intelligence. TADI integrates various data sources, including drilling reports and real-time measurements, using a dual-store architecture with DuckDB and ChromaDB. The system employs a large language model to orchestrate twelve domain-specific tools for evidence gathering, cross-referencing structured data with narrative reports. A new metric, the Evidence Grounding Score (EGS), is proposed to assess compliance, and the implementation is made reproducible. AI
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IMPACT Demonstrates how LLM orchestration and domain-specific tools can enhance analytical quality in technical operations, potentially improving efficiency in data-intensive industries.
RANK_REASON This is a research paper detailing a novel AI system and methodology. [lever_c_demoted from research: ic=1 ai=1.0]