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The DinoAI copilot helps you tackle a wide range of analytics engineering tasks from inside the Code IDE, automating repetitive work and keeping your dbt™ project consistent. These use cases show what it can do, end to end. For the full copilot reference (chat, ask, agent, and god modes), see the DinoAI product docs.

Build your project

🏭 Create sources from your warehouse

Generate complete, accurate sources.yml files directly from your warehouse metadata.

🧩 Generate models

Turn raw source tables into readable SQL that follows your team’s patterns.

🏛️ Build marts models

Create intermediate and marts models with the right joins, CTEs, and business logic.

📝 Generate documentation

Auto-generate model and column descriptions with tests across an entire folder.

⚙️ Configure data pipelines

Update dbt_project.yml with the right materializations, schemas, and tags for your folder structure.

Fix, research, and ship

🔧 Fix errors with Jira

Pull ticket details into DinoAI to analyze production errors and suggest fixes.

🖥️ Debug with terminal context

Feed failed dbt™ command output back to DinoAI to diagnose and fix the issue.

🔍 Research with Perplexity

Access the latest technical information and best practices without leaving the IDE.

📄 Add context with PDFs

Enrich DinoAI’s context with specs, documentation, or reports from PDF files.

🔀 Generate PR descriptions

Draft clear pull request descriptions from your changes automatically.

📏 Set up .dinorules from your project

Codify your team’s conventions by pointing DinoAI at your existing project files.
Getting started tipFor best results, start with Create sources from your warehouse. It gives your project an accurate foundation you can then reference when generating models.