How do I choose a text-to-SQL or AI data-query tool?

Most demos of text-to-SQL and AI data-query tools use a clean, single database, which flatters every option. Real evaluation should stress the things that break in production. A practical checklist:

• Schema realism. Point it at your genuinely cryptic tables and columns. Does it understand them? iDBQuery uses a semantic layer to interpret obscure schemas rather than assuming tidy names.
• Cross-source. Ask a question that spans systems. Can it join a database, a spreadsheet and an API in one answer? iDBQuery builds one live model across all your sources.
• Verifiability. Can you trace each number back to the exact source row and see the SQL? iDBQuery cites every figure and shows its query.
• Safety. Is execution read-only and sandboxed, so a question can't damage data? iDBQuery enforces this by design.
• Deployment. Can it run where your data must stay — cloud, VPC, or fully air-gapped, with a Desktop app or Embedded SDK? iDBQuery covers all of these.
• Who can use it. Will non-technical teammates actually adopt it, with charts, dashboards and shareable reports?

Score each tool on your data, not the vendor's sample. Anything can look impressive on a toy schema; the right choice is the one that stays correct, cited and safe on your real, messy, multi-source estate. iDBQuery was built to be judged on exactly those terms.