
nao
nao is an AI-powered data editor and IDE designed specifically for data teams, offering native integration with data warehouses and an intelligent agent that writes code with data quality in mind.
https://getnao.io/?ref=producthunt

Product Information
Updated:Nov 28, 2025
What is nao
nao is a modern platform revolutionizing analytics engineering by providing a specialized IDE for data professionals. Founded in 2024 and part of Y Combinator's Spring 2025 batch, nao serves as a comprehensive solution for data teams, including data engineers, analytics engineers, and data scientists. The platform combines the functionality of a traditional code editor with AI capabilities while maintaining the highest standards of security through SOC 2 Type II certification. With pricing starting from $30 per month and a free plan available, nao aims to streamline data workflows and enhance productivity for organizations relying on modern data warehouses.
Key Features of nao
nao is an AI-powered data IDE designed specifically for data teams that integrates directly with data warehouses. It provides an intelligent code editing environment with AI assistance for SQL and Python development, native data warehouse integration, real-time data preview capabilities, and built-in data quality checks. The platform combines code editing, data visualization, and AI-powered suggestions while maintaining high security standards with SOC 2 Type II certification.
Native Data Warehouse Integration: Connects directly with multiple data warehouses including Snowflake, BigQuery, Postgres, and others, allowing users to query and preview data directly within the IDE
AI-Powered Code Assistant: Features an AI agent that understands your data schema and can write contextually aware code, provide auto-completions, and ensure data quality
Data Stack Integration: Seamlessly integrates with popular data tools like dbt, Airflow, and various BI tools, while also incorporating documentation from these tools into its context
Secure Data Handling: Maintains data privacy by processing queries locally and never sending data to external servers, with SOC 2 Type II certification
Use Cases of nao
Data Pipeline Development: Enables data engineers to build and maintain data pipelines more efficiently with AI assistance and integrated testing
Analytics Engineering: Helps analytics engineers write and validate SQL transformations with instant preview capabilities and data quality checks
Data Analysis and Exploration: Allows data analysts to quickly explore data, create visualizations, and perform deep-dive analysis with AI assistance
Pros
Purpose-built for data teams with specialized features
Strong security and privacy controls
Comprehensive integration with existing data tools
Cons
Relatively new product (founded 2024)
Limited to specific programming languages and data workflows
How to Use nao
Download and Install nao: Visit https://getnao.io/ and download the nao editor. Sign up for a free trial account to get started.
Connect Your Data Warehouse: Connect nao to your data warehouse (supports Postgres, Snowflake, BigQuery, Databricks, DuckDB, etc.). The connection is local and secure between your computer and warehouse.
Set Up Project Environment: If using dbt, nao will automatically detect dbt_project and profiles, or you can specify manually. You can also set up Python virtual environments through the terminal integration.
Configure .naorules (Optional): Create a .naorules file to personalize AI agents with custom rules around your data model, coding style, and project requirements.
Use the AI Agent: The AI agent has direct access to your codebase and warehouse. You can ask it to write queries, analyze data, and ensure data quality. Use @warehouse to reference warehouse data.
Preview and Edit Data: Use the built-in SQL worksheet to query and preview data directly. The editor provides auto-complete suggestions based on your actual data schema.
Review Changes: View code diffs and data diffs side by side to visualize how changes affect data output. Accept/reject changes with line-level widgets and use Ctrl+Z to undo.
Run Data Quality Checks: Let the agent test your data, run data diffs, detect missing values, outliers, and compare dev/production data differences.
Integrate with Data Stack Tools: Connect to tools like dbt, Airflow, GitHub etc. through MCPs (Managed Control Planes) for an end-to-end development experience.
Collaborate and Deploy: Use the unified IDE environment to collaborate with team members and deploy data projects with confidence.
nao FAQs
nao is an AI-powered data editor designed for data teams that connects to your data warehouse and business context. It functions as a replacement for your data warehouse console with additional AI features.
nao Video
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