How to convert natural language to a SQL query

Give the tool the table structure, then state the metric, grouping and filters in plain language. You do not need to train a model to ask a CSV question.

1. Prepare a CSV with meaningful headers and consistent types. Remove sensitive fields first.
2. Specify the measure and scope: for example, total revenue by product, excluding returned orders.
3. Inspect the generated SELECT, WHERE and GROUP BY clauses. Verify that every referenced column exists.
4. Compare the result with a manual calculation over a small sample before relying on it.

“Best products” is ambiguous: specify revenue, margin or units. For totals, decide how blanks and refunds should be treated. QueryAI can generate SQL, but the person asking the question must define the business meaning. Try the header-only demo or read whether SQL knowledge is needed.

Try it for real with your CSV

Product details checked 2026-10-26. QueryAI: current workflow and pricing. Vendor capabilities and offers can change.