From the Bonnard team
How Bonnard Builds Agent-Friendly MCPs
Exposing your data is easy. Designing an MCP that agents actually use well is the hard part. Right-sized tools, governed metrics, and token-efficient results.
Turn Your dbt Project Into a Semantic Layer
Go from dbt models and a manifest to governed metrics agents can query consistently, without rebuilding your stack.
We Built a Way for Non-Technical Teams to Ship Data Pipelines
Data teams are the bottleneck. Every team needs data, nobody can get it without engineering. Loony lets anyone describe what they need and deploy a governed data pipeline in minutes.
AI Data Analysis: Why Governed Metrics Beat Raw SQL Generation
AI data analysis tools generate plausible SQL but unreliable numbers. Here's why governed metrics through a semantic layer produce trustworthy results at scale.
AI Reporting: How to Automate Reports Without Losing Trust
AI reporting tools generate reports fast but often produce inconsistent numbers. Here's how to automate reporting with governed metrics that every stakeholder trusts.
Analytics API: How to Serve Governed Metrics to Any Consumer
An analytics API exposes your metrics programmatically. Here's how to build one that serves dashboards, AI agents, and customer integrations from the same definitions.
Best Embedded Analytics Tools for SaaS in 2026
Comparing the top embedded analytics tools for B2B SaaS: Metabase, Holistics, Explo, Luzmo, Reveal, GoodData, Looker, Power BI, Tableau, and Bonnard.
How to Build Customer-Facing Analytics for B2B SaaS
Your B2B customers need access to their data. Here's how to ship governed, multi-tenant analytics using a semantic layer instead of building dashboards from scratch.
KPI Dashboards Are Broken. Here's What Replaces Them.
KPI dashboards show stale numbers that nobody trusts. Governed metrics served through a semantic layer give every consumer the same live data, from dashboards to AI agents.
Real-Time Analytics: When You Need It and When You Don't
Not every metric needs real-time data. Here's how to decide what needs sub-second freshness, what can be cached, and how pre-aggregation handles both.
Self-Service BI Is a Lie (Unless You Govern the Metrics)
Self-service BI promised to free the data team. Instead it created metric chaos. Here's how governed metrics with a semantic layer deliver real self-serve analytics.
What Is a Semantic Layer? A Practical Guide for Data Engineers
A semantic layer defines business metrics once so every consumer gets the same answer. Here's what it is, how it works, and how to build one with code examples.
What Is an Agentic Semantic Layer?
An agentic semantic layer is a metrics layer built for AI agents. It defines business logic once and exposes it via MCP or API so agents query governed definitions, not raw SQL.
Why Your AI Agents Need a Semantic Layer
AI agents querying raw SQL produce inconsistent, ungoverned results. A semantic layer fixes that. Here's what goes wrong without one, and how metrics-as-code changes the architecture.
How to Connect an AI Agent to Your Data Warehouse
Define metrics in YAML, expose them via MCP, and let AI agents query governed data instead of raw SQL. Full tutorial with Bonnard in under 30 minutes.