Querio

The platform's AI translates business questions into optimized SQL queries, executes them against connected data sources, and returns results in the most appropriate format tables, charts, or narrative summaries based on the nature of the question and the structure of the retrieved data.

Querio's semantic layer builds an understanding of the specific data model, business terminology, and metric definitions relevant to each organization, enabling the AI to correctly interpret business questions using the organization's own language rather than making generic assumptions about what terms like 'revenue,' 'active customer,' or 'conversion rate' mean in the context of a specific company's data model.

Administrators annotate this semantic layer defining which tables contain which business concepts, how metrics should be calculated, and what time periods standard terms like 'last quarter' refer to in the organization's fiscal calendar improving the accuracy and trustworthiness of AI-generated query results over time.

Querio supports collaborative analytics through a shared question library where frequently asked business questions and their verified SQL translations are saved and searchable, allowing teams to build on each other's analytical work rather than starting from scratch for recurring questions.

The platform integrates with Snowflake, BigQuery, Redshift, PostgreSQL, and other major data infrastructure systems.

For organizations where a small data team is overwhelmed by ad hoc analytics requests from business stakeholders and where the latency of request-queue-response cycles is slowing decision-making, Querio provides the self-service analytics layer that makes data access available when business decisions require it rather than when analyst bandwidth permits.