Rill lets you define BI workflows—models, metrics, dashboards and access policies—in YAML and SQL, and runs queries on engines like ClickHouse or DuckDB. It adds conversational and agent-driven interfaces for natural-language querying and automated dashboard generation, with real-time performance, embeddable dashboards, and Git-backed deployments.

• Full BI stack (models, metrics, dashboards, agent config) spins up from three prompts via `rill init`, then `rill start` gives an instant local UI.
• Metrics-as-code approach: SQL models plus YAML-defined dimensions/measures let Rill generate queries on the fly, returning sub-second results even at billions of rows.
• Same semantic layer powers both natural-language conversational BI and traditional SQL-based analyst workflows.
• Deployment is Git/CI-CD native via `rill deploy`, with dashboards embeddable and metrics exposed through a REST API.

Rill’s Metrics SQL lets you define business metrics once and query them using plain SQL across dashboards, notebooks, and AI agents. It compiles metric views into optimized OLAP queries, handling grouping, filters, time functions, and security automatically.

• Define a metric once in a YAML-backed metrics view and query it as plain SQL from dashboards, notebooks, and AI agents alike, instead of redefining it in dbt, Looker, Python, etc.
• A parser/compiler/executor pipeline rewrites simple queries like "SELECT country, revenue FROM revenue_metrics" into full OLAP SQL with correct GROUP BY, joins, and filters automatically.
• Security (row-level and column permissions) and SQL-injection-safe parameter binding are enforced consistently at the executor stage for every consumer.
• Works across multiple backends (ClickHouse, Snowflake, DuckDB, Druid) and can leverage existing database optimizations like materialized views and indexes for performance.