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Data & Warehouse
Snowflake integration
The Snowflake integration is two-way. Rivane pushes ledger data into Snowflake, including journal entries, balances, dimensions, and derived finance metrics, and Rivane AI can query Snowflake through the Model Context Protocol (MCP). Finance and product data end up in one place for unified analytics and BI.
Request a demoWhat syncs
GL data
Journal entries and balances
Dimensions
Entity, department, class
Metrics
Derived finance metrics
Where Snowflake data lands in Rivane
| Data | Where it lands in Rivane |
|---|---|
| GL data | Journal entries and balances landed in Snowflake for BI |
| Dimensions | Entity, department, and class, kept with every entry |
| Metrics | Derived finance metrics available next to product data |
| Snowflake tables | Queried by Rivane AI through MCP |
A typical workflow
An analyst wants revenue by product cohort. Ledger data already sits in Snowflake next to product usage, so the query joins the two directly, and a finance lead can ask Rivane AI the same question in plain English.
Key features
Warehouse sync
Ledger data lands in Snowflake for BI.
MCP access
Rivane AI can query Snowflake through MCP.
Unified analytics
Finance and product data in one place.
Frequently asked questions
Yes. Ledger data syncs to Snowflake, and Rivane AI can query Snowflake via MCP.
The Model Context Protocol lets Rivane AI reach external tools and warehouses securely.
No. Data connectors are included in every Rivane plan, with unlimited users.