Lovable Insights

A handful of people on your data team connect the warehouse once, and from then on anyone in the company can ask a question in plain language and build a live dashboard, where every number comes from definitions the data team approved. To the data team it's a governed semantic layer. To everyone else it's a data analyst that already knows your business.

Today, when employees build dashboards on raw data, the AI guesses definitions from table and column names, the numbers come back "close but not exact," and every question routes back to a small data team. This template closes that gap.

Your data team sets it up once. Connect the warehouse and scope exactly which datasets and fields are in play. The agent drafts metric definitions from that context, and each one has to pass a live warehouse dry-run before you can approve it. Define evals to measure accuracy, then publish. Publishing creates a chat connector that serves your curated, governed schema.

From there, anyone in the company works over that connector: in Lovable, or by asking questions in Slack, ChatGPT, or Claude. Employees ask in natural language and build live, customizable dashboards where every chart is grounded in the same approved definitions. Dashboards are real Lovable apps: add filters, an AI chatbot, whatever the team needs.

The data team keeps control the whole way. Each company's copy is a fully isolated app with its own database, so nothing is shared. Warehouse access is read-only and least-privilege, credentials stay server-side, cost caps protect your bill, and Disconnect is a true kill switch that clears the connection, your selections, and the schema the connector serves.

Scheduled alerts in Slack, and inherited row- and role-level permissions, are coming soon.

Best Use Cases

Self-Serve Analytics on a Governed Layer

Give the whole company a way to ask questions and build dashboards that always resolve to the data team's approved definitions. Employees move fast; the data team keeps the numbers canonical.

Deflecting Ad-Hoc Data Requests

Turn the steady stream of "can you pull this number?" into self-serve. The data team curates the layer once, and routine questions get answered without a ticket.

Replacing "Close But Not Exact" AI Dashboards

If employees already build dashboards on raw data and the numbers don't match the official ones, the template grounds every answer in validated metrics so what people see matches the source of truth.

Standing Up a Semantic Layer Without Modeling Weeks

Teams without a mature BI setup can build a curated, governed layer directly in the product: business context in, agent-drafted and dry-run-validated metrics out, in hours.

Getting Started

1. Remix This Template

Create your own copy. You'll get the full onboarding flow and Control Panel, isolated in your own workspace. If this template isn't the right fit, you can remix a different one — no wasted work.

2. Connect Your Data Warehouse

Connect to Snowflake, Databricks, Fabric, Redshift, or BigQuery through the guided read-only setup, or import an existing semantic layer with the dbt connector. Then scope the datasets and fields you want exposed. Datasets the connection can't query are locked automatically.

3. Add Your Business Context

Describe what your company does and what you measure, or upload a document (PDF supported). This grounds every metric the agent drafts.

4. Review and Approve Metrics

The agent drafts metric definitions from your context and scope. Each must pass a live warehouse dry-run before you approve it. Edit definitions and date ranges as needed.

5. Run Evals and Publish

Define or import a test set, run accuracy checks, then publish to create your chat connector.

6. Go Live and Iterate

Employees build dashboards and ask questions in Lovable over the connector. Keep tuning the layer, add metrics, and extend the app to fit your team.

Conclusion

Lovable Insights is for companies that want AI-built analytics with the numbers under the data team's control. The data team connects a warehouse, curates a governed semantic layer, and publishes it once. Everyone else builds trusted dashboards and asks questions in plain language, in Lovable, Slack, ChatGPT, or Claude. It's a template: remix it today and make it yours.