dbt Copilot
Data engineering for the AI era
Accelerate every part of your workflow with context-aware AI to build high quality pipelines and AI systems faster than ever before.
Trusted by the best in data & AI
Deliver AI-ready data, faster
With dbt Copilot, you're not just injecting GenAI into your workflows. You're harnessing your data's full context—its relationships, metadata, and lineage—to automate routine tasks, helping you enforce analytics development best practices with the click of a button. The result is refined, governed data products that fuel high-quality analytics and AI systems.
Auto generate SQL using everyday language
Create simple or complex SQL queries using everyday language in just one click. Skip tedious coding, reduce errors, and help your data team ship trusted products faster.
Fast-track your semantic layer
Skip the manual effort of drafting YAML files. Accelerate your semantic layer strategy with AI that automatically generates and maintains consistent semantic models directly from your metadata.
Ask your data anything — get accurate, trustworthy answers
Turn everyday questions like, ‘What is ARR growth over time?’ into instant insights — no SQL required. dbt Copilot leverages trusted, well-defined metrics from the dbt Semantic Layer to turn your questions into queries, giving stakeholders the answers they need while freeing you from dashboard duty — everybody wins.
Achieve higher test coverage
Catch problems before they hit production with dbt Copilot’s automated data tests. It ensures your DAG stays error-free and your maintains the integrity of your data quality.
Deliver documentation in minutes, not hours
Build high-quality data products faster. With a single click, dbt Copilot generates documentation from your project’s metadata to streamline development, ensure consistency, and maintain best practices.
"Copilot has completely changed how we approach documentation and query optimization. Instead of spending hours manually updating models, I can use natural language to generate tests, infer metadata, and enrich our data models with valuable context. The more metadata we add, the better our entire team benefits, from analysts to executives."
Cody McLeanSenior Data Engineer, Hard Rock Digital
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