iDBQuery vs a custom RAG chatbot over my data: which should I build?
A custom RAG chatbot retrieves passages of text and generates a prose answer, which is great for documents but unreliable for precise numbers; iDBQuery writes and runs real SQL against your live data and cites each figure to its source row, so metrics are exact and auditable rather than approximated from retrieved text.
Retrieval-augmented generation (RAG) chatbots embed your documents, retrieve the closest passages to a question, and let a language model write an answer from them. That's a strong pattern for policy documents and knowledge bases. It's a weak pattern for analytics, because a number summarised from retrieved text is not the same as a number computed from your data.
- Computed, not retrieved. iDBQuery generates SQL and runs it against a live model of your sources, so counts, sums and trends are calculated exactly — not paraphrased from a paragraph.
- Cited to rows, not chunks. Answers trace to the specific source rows behind them, which you can open and check.
- No pipeline to build or maintain. A custom RAG stack means an embedding store, retrieval tuning, prompt engineering, evaluation and ongoing upkeep. iDBQuery is a finished product — connect a source and ask.
- Documents too. For PDFs and scanned files iDBQuery ingests them with OCR and makes them queryable alongside your structured data, so you don't need a separate document bot.
Build a custom RAG chatbot when the job is open-ended search over unstructured text. Choose iDBQuery when people need exact, current, verifiable numbers from your databases and files — with none of the build, evaluation and maintenance burden of a home-grown system.
Updated 2026-06-22