nao

What is nao

nao is an AI-powered data editor designed for data teams. It acts as a data IDE, connecting directly to your data warehouse and understanding business context. It is powered by an AI agent that writes code with data quality in mind, aiming to make data work faster and more efficiently.

How to use nao

1. Connect your data warehouse: nao supports connections to various data warehouses including Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift.
2. Query your data: Use nao as a replacement for your data warehouse console. Preview data, run SQL queries, and benefit from AI auto-complete on your data schema.
3. Utilize the AI Agent: The AI agent has direct access to your data schema and can write code to match your data, query it, analyze it, and ensure data quality. It can create data pipelines, run analytics, explore data, and check data quality.
4. Integrate with your data stack: nao integrates with tools like dbt for creating data models, running analytics, and viewing lineage. It can also index documentation from data stack tools like Airflow, Dagster, and dbt to inform code generation. MCPs (Managed Cloud Platforms) for tools like Notion, GitHub, Airbyte, DLTHub, Tableau, Metabase, and Looker can be added.
5. Personalize AI agents: Use .naorules to customize AI agents with rules related to your data model, coding style, and project requirements.

Features of nao

• AI-powered data editor
• Connects to data warehouses (Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, Redshift)
• AI agent for code generation, data analysis, and quality checks
• Direct SQL querying and data preview
• AI auto-complete for data schema
• dbt integration for data pipelines and lineage viewing
• Contextual AI based on data stack documentation
• .naorules for AI agent personalization
• Local data connection for enhanced security
• SOC 2 Type II certified

Use Cases of nao

• Accelerating data pipeline creation and deployment.
• Performing complex data analysis and generating insights quickly.
• Exploring and understanding data within the warehouse environment.
• Ensuring data quality through automated checks and tests.
• Developing data models using AI assistance.
• Integrating various data stack tools for a unified workflow.

Features of nao

• AI-powered data editor
• Connects to data warehouses (Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, Redshift)
• AI agent for code generation, data analysis, and quality checks
• Direct SQL querying and data preview
• AI auto-complete for data schema
• dbt integration for data pipelines and lineage viewing
• Contextual AI based on data stack documentation
• .naorules for AI agent personalization
• Local data connection for enhanced security
• SOC 2 Type II certified

Use Cases of nao

• Accelerating data pipeline creation and deployment.
• Performing complex data analysis and generating insights quickly.
• Exploring and understanding data within the warehouse environment.
• Ensuring data quality through automated checks and tests.
• Developing data models using AI assistance.
• Integrating various data stack tools for a unified workflow.