Natural Language Queries allow you to interact with complex datasets using everyday English instead of technical code like SQL. The system uses advanced AI to interpret the "intent" behind your question, automatically translates it into a database query, and retrieves the relevant insights instantly.

No. The primary goal of NLQ is to democratize data access. It is designed specifically for non-technical stakeholders—such as policy makers, urban planners, or business managers—allowing them to ask questions like "Which London borough has the highest risk of diabetes?" without needing to understand the underlying database structure.

The system utilizes Semantic Parsing and Natural Language Understanding (NLU) to navigate nuances in language. If a query is broad, the AI looks for "correlated attributes" within the synthetic population to provide the most logically accurate answer. For example, it can distinguish between a request for the "total count" of a demographic versus the "percentage distribution" based on the context of your question.

Yes. Because the Natural Language Query engine is built on top of Synthetic Gen’s privacy-preserving datasets, any answer you receive is based on artificial, "look-alike" data. This means you can query sensitive topics—like health risks or financial status—and get high-fidelity insights without ever accessing or exposing real Personal Identifiable Information (PII).

While dashboards are great for viewing pre-set metrics, NLQ provides on-demand flexibility. Instead of waiting for a data analyst to build a new report, you can ask unique, ad-hoc questions and receive immediate visualizations or data tables. This removes the "data gatekeeper" bottleneck and significantly accelerates the decision-making process.