Natural language query (NLQ) systems use parsing techniques and large language models to interpret a user’s question, identify the entities and metrics involved, and translate the request into a structured query.

For example, ‘show me monthly revenue for Europe’ would map ‘revenue’ to a measure, ‘Europe’ to a dimension filter and ‘monthly’ to a time aggregation.

The semantic layer provides context so the model can choose the correct tables and joins.

After the query runs, the system formats results as charts or narratives.

Continuous learning improves accuracy: user feedback helps correct misinterpretations.

MageMetrics’ intent‑to‑SQL pipeline draws on usage history and model evaluations to refine translations and reduce errors over time.