Lovable Insights

Every data team knows where their week goes: one-off SQL queries, "just one more dashboard," and hours of repetitive requests before the real work even starts. Everyone else knows the other side of it: waiting hours or days for an answer or report, or asking AI to build one from raw tables and getting numbers that are close but not exact, so nobody fully trusts them. That's today's analytics trade-off: governed but slow, or fast but wrong.

Lovable Insights ends the trade-off. Your data team connects a data warehouse, scopes exactly what's exposed, and approves metric definitions. No more weeks of modeling: you're live in hours. From then on, anyone can ask a question in plain language in Lovable, Slack, Claude, or ChatGPT, and build dashboards in Lovable where every number is based on an approved definition. And because dashboards are Lovable apps, teams can add AI chatbots, dynamic filters, and visualizations without filing a ticket. To the data team, it's a governed metrics layer with read-only access, cost caps, and a one-click kill switch. To everyone else, it's a data analyst that already knows your business.

Use cases

Ask it like you'd ask a colleague

"What was Q2 pipeline by region vs. target?" from a sales rep. "Which campaigns drove the most signups last month?" from a marketer. "How did ticket volume trend after the pricing change?" from a customer support lead. Every answer in Lovable, Slack, Claude, and ChatGPT references the definitions your data team approved.

Build the dashboard the moment you need it

A finance analyst adds region and segment filters to the revenue dashboard, an ops manager tracks this week's throughput against last month's, a team lead turns the quarterly review into a live report instead of screenshots pasted into slides. Add an AI chatbot to any dashboard and ask follow-ups right there. Visualizations in minutes, no SQL.

Deflect ad-hoc data requests

Turn the steady stream of "can you pull this number?" into self-serve. Routine questions answer themselves, and the data team gets its time back for the hardest problems.

Set up your metrics layer without modeling weeks

Teams can build a curated, governed layer directly in Lovable, in hours: describe your business, the agent drafts the metrics and tests each one against your real warehouse data, and you approve what gets served.

Getting started

Step 1: Remix this template

Create your own copy: a fully isolated app in your company's workspace. The onboarding flow guides you from there.

Step 2: Connect your data warehouse

Connect your data warehouse (Snowflake, Databricks, Fabric, Redshift, or BigQuery) to set up your metrics layer or import an existing one (dbt).

Step 3: Scope your data

Select the exact datasets, tables, and fields you want exposed for read-only access.

Step 4: Add your business context

Describe what your company does and what you want to measure, or upload a document. This grounds every metric the agent drafts.

Step 5: Review and approve metrics

The agent drafts metric definitions from your scoped data and business context for your review and approval. You can also upload your existing metrics.

Step 6: Publish as a chat connector and go live

Publish your app and turn it into an MCP chat connector that employees can use to build dashboards in Lovable and chat with data in Lovable, Slack, Claude, and ChatGPT.

Step 7: Refine your metrics layer

Watch how it's actually used: the Control Panel shows usage and query logs. Edit your metrics and run evals whenever you need to. And since the Control Panel is a Lovable app, you can customize it to fit your team's use cases.

FAQ

Does Lovable store or copy data?

No. Access is read-only: the template can query only the datasets you scope, and every query runs live against your warehouse. Results aren't stored, credentials stay server-side, and the app's own database only holds your configuration, such as metric definitions and evals. Learn more about how Lovable connects to your data.

Is our data used to train AI models?

No. Your warehouse data is used to answer your questions, nothing else. It is not used to train Lovable's models or external models. Learn more at the Lovable Trust Center.

How does the app access our data warehouse?

Through a read-only connection. Users query only the datasets you scope, nothing more. Credentials stay server-side and never touch the browser. Learn more about how Lovable connects to your data.

Who can access Lovable Insights?

Only people you invite. The app is published as an MCP chat connector inside your Lovable workspace, protected by authentication. Inherited row- and role-level permissions are coming soon; until then, to give different teams access to different data, create a copy of the template per use case.

How much will this cost?

The template is free. Usage is pay-as-you-go, using your workspace's Lovable credits. You pay for what people actually ask and build, not per seat. You stay in control with cost caps per query and per day in the Control Panel.

Do I need a semantic layer before I start?

No. The guided setup builds one with you. Already have one? Import it with the dbt connector, or paste your existing definitions and Lovable turns them into metrics.

Is Lovable certified?

Lovable is SOC 2 Type II, ISO 27001:2022, and AIUC-1 certified, and GDPR-aligned. AIUC-1 is the security standard built for AI agents, and Lovable is the first coding agent platform to earn it. Learn more at the Lovable Trust Center.

Conclusion

Lovable Insights is for companies that want AI analytics with the numbers under the data team's control. Your data team connects the warehouse, curates a governed metrics layer, and decides who sees what. Everyone else asks questions in plain language and builds trusted dashboards in Lovable, Slack, Claude, or ChatGPT.

Remix this template today and make it yours.