Overview

Define your metrics once. Query governed data from any AI tool, dashboard, or application.

Bonnard is a semantic layer platform. Your data team defines metrics once, and everyone else gets reliable answers from the AI tools they already use — Claude, ChatGPT, Cursor, Copilot — in whatever form they need. No new interface to learn.

Architecture

Data Warehouse → Cubes (metrics) → Views (interfaces) → Query Surfaces
                                                          ├── MCP (AI agents)
                                                          ├── REST API
                                                          └── SDK (custom apps)

Cubes map to your database tables and define measures (revenue, count) and dimensions (status, date). Views compose cubes into focused interfaces for specific teams or use cases. Once deployed, your semantic layer is queryable through MCP, REST API, or the TypeScript SDK.

Multi-warehouse

Connect any combination of warehouses through a single semantic layer:

PostgreSQL — Direct TCP connection
Redshift — Cluster or serverless endpoint
Snowflake — Account-based authentication
BigQuery — GCP service account
Databricks — Token-based workspace connection

Metrics from different warehouses are queried through the same API. Your consumers never need to know where the data lives.

One source of truth for every AI

Bonnard exposes your semantic layer as a remote MCP server. Add one URL to any MCP-compatible client — Claude, ChatGPT, Cursor, VS Code, Windsurf, Gemini — and it can explore your data model, run queries, and render charts. Every query is governed and scoped to the user's permissions automatically.

Governed by default

Metrics are version-controlled and deployed from the terminal. Access, roles, and row-level security are managed by admins from the dashboard. Every query — whether from an AI agent, the API, or the SDK — is scoped to the user's permissions. No ungoverned access.

Your data stays where it is

Your data stays in your warehouse. Bonnard adds a governed semantic layer on top — hosted, queryable, and managed. Each organization gets isolated query execution. Deploy from your terminal in minutes, not quarters.

Where to go next

Getting Started — Install the CLI and build your first semantic layer
Modeling — Define cubes, views, and pre-aggregations
Querying — Query via MCP, REST API, or SDK
CLI — Commands, deployment, and validation
Governance — Control access to views, columns, and rows
Catalog — Browse your data model in the browser