Querio

Querio is built around a blunt observation: modern analytics stacks have become expensive, fragmented, and hard to trust. Data lives in a warehouse, transformations in dbt, definitions in a BI tool, and context in people's heads — and now AI agents need all of it. Querio pulls those pieces back into a single git repository that your team owns, so metrics, business rules, and instructions are defined once and read by everyone, including agents.

The product spans the whole path from raw data to decision. Datasources and a catalog connect your warehouse; a knowledge base, memory, extensions, and skills encode how your business actually works. From there, teams ask questions in AI-assisted notebooks, inspect the queries and the returned rows, build dashboards, and schedule automations that send reports or run prompts on a trigger. Because the context layer is framework-agnostic, Querio stays natively version controlled: every change is reviewed, diffed, and reversible, and dbt models update alongside the context that describes them.

For agents, Querio exposes an MCP server, CLI, and API. Any MCP-compatible client — Claude, Codex, or a custom agent — can query governed data through the same context layer, with per-user authentication and a runtime the platform controls. Querio says this saves roughly half of the token cost compared with unmanaged agent access, and shared outputs keep questions auditable. The same machinery can be white-labelled and embedded in your own product with SSO, so your customers get an analytics experience backed by the definitions you already maintain.