Schema Archaeology: How to Use AI to Reverse-Engineer Business Meaning From an Undocumented…

2026-07-12 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, FinTech & Digital Financial Services · Depth: Intermediate, quick

Summary

The article introduces "Schema Archaeology," a system designed to reverse-engineer business meaning from undocumented financial databases using AI. This first part details the core application components, including `seed_db.py` for populating `p2p.db` with realistic data and anomalies, `schema_agent.py` which leverages Claude to generate a `schema_context.json` semantic layer, and `rag_pipeline.py` for interactive Q&A through a hybrid RAG approach combining SQL and ChromaDB. A Streamlit UI (`app.py`) provides the chat interface. The system aims to build GenAI solutions that avoid hallucination when processing financial data, with a second article planned to cover accessory functions like `context_packer.py` for LLM context assembly and `anomaly_agent.py` for SQL integrity rule checks.

Key takeaway

For AI Engineers tasked with integrating LLMs into financial data systems, this architecture offers a robust approach to mitigate hallucination. You should consider implementing a `schema_agent` to automatically generate semantic layers from undocumented databases, paired with a hybrid RAG pipeline for accurate Q&A. This strategy ensures reliable data interpretation and reduces the manual effort in understanding complex, legacy financial schemas, accelerating development of trustworthy GenAI applications.

Key insights

AI agents can reverse-engineer undocumented database schemas to create semantic layers for reliable financial data analysis.

Principles

• Combine RAG with SQL for financial data.
• Use LLMs to generate semantic schema context.
• Integrate anomaly detection for data integrity.

Method

Architect a GenAI system using a `schema_agent` for semantic layer creation, a hybrid RAG pipeline (SQL + ChromaDB) for Q&A, and a Streamlit UI.

In practice

• Implement `schema_agent.py` with Claude for schema context.
• Build `rag_pipeline.py` for SQL and ChromaDB queries.
• Develop `app.py` for a Streamlit-based chat interface.

Topics

• Schema Archaeology
• Financial Data
• Generative AI
• RAG Systems
• Claude LLM
• Streamlit

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.