This article details a method for reverse-engineering business meaning from undocumented databases using AI, a process termed "schema archaeology." The approach involves building a pipeline that transforms a disorganized P2P (Purchase-to-Purchase) SQLite database into a human-readable RAG application. Key components include a schema agent that learns database relationships and a RAG engine for querying, with a focus on improving retrieval precision for financial analysis by creating knowledge units that span multiple tables and employing a hybrid search strategy. The system is designed to mitigate AI hallucinations by explicitly instructing the model to express uncertainty when retrieval confidence is low. AI
IMPACT Enables safer and more accurate AI-driven financial analysis on previously inaccessible, undocumented enterprise data.
RANK_REASON Article describes a technical method and pipeline for using AI tools to solve a specific data engineering problem, rather than a new product release or core research.
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