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To prepare data for LLM-powered analytics in compliance-heavy environments, it would be beneficial to have an automated data labeling feature that structures and classifies data upon ingestion. This would help in making the downstream work of building the semantic layer and LLM context easier, especially in healthcare where data sensitivity is crucial.
Working in healthcare and leadership wants us to deploy llm powered analytics so clinicians can ask natural language questions against our operational data. For an llm to reason about your data it needs context, column descriptions, business rules, relationship mappings. Our warehouse has tables with field names like "enc\_typ\_cd" and "adj\_rev\_v3" with zero documentation. A human analyst knows what those mean through institutional knowledge. An llm does not and will hallucinate answers. Also in healthcare every data pipeline needs audit trails, access controls, and sensitivity classifications. Patient data needs to be masked or excluded from the llm context entirely. Operational and financial data has different rules. You cant just pipe everything into a vector store and let the llm loose. The ingestion layer matters more than expected for ai readiness. If data arrives in the warehouse already structured, labeled with descriptions, and classified by sensitivity level, the downstream work of building the semantic layer and llm context is dramatically easier. Some of the newer data integration tools handle this labeling automatically at ingestion time. Anyone tried getting enterprise data ai ready for llm use cases while dealing with strict compliance requirements?