The AI-powered command center for data teams to write, document, and optimize SQL 10x faster.
AI Data Sidekick, developed by Airops, represents the 2026 state-of-the-art in LLM-driven data engineering orchestration. It functions as a context-aware layer sitting atop organizational data warehouses like Snowflake, BigQuery, Redshift, and Databricks. Technically, the platform utilizes a sophisticated RAG (Retrieval-Augmented Generation) architecture to index database metadata, schema definitions, and historical query logs, enabling high-precision natural language-to-SQL translation and automated documentation. By 2026, it has evolved from a simple browser extension into a comprehensive workspace that integrates directly with dbt and version control systems. It doesn't just generate queries; it provides FinOps-aligned query optimization suggestions to reduce warehouse compute costs. The architecture supports multi-dialect conversion, allowing teams to migrate legacy SQL to modern cloud-native formats seamlessly. For enterprise-level deployments, it provides a secure, governed environment where metadata is processed locally or via private VPC instances to ensure data privacy while maintaining the velocity of an AI-first development workflow. It bridges the gap between raw data storage and actionable business intelligence by automating the most tedious aspects of the data development lifecycle.
Uses embeddings to index entire database schemas, allowing the LLM to understand foreign key relationships and table importance.
Turn natural language into production-grade SQL and instant visual insights with RAG-enhanced schema awareness.
Autonomous data synthesis and predictive modeling for the modern AI-driven enterprise.
Turn your databases and spreadsheets into intelligent conversational AI agents.
The conversational AI analyst that transforms your database into an interactive dialogue.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatically generates documentation and schema files for dbt projects based on SQL analysis.
Transpiles SQL from legacy systems (like Oracle or SQL Server) to cloud-native dialects (Snowflake/BigQuery).
Analyzes query structure against warehouse pricing models to estimate compute cost before execution.
Converts complex SQL logic into equivalent Python/Pandas code for data science workflows.
Identifies PII and sensitive data within columns using AI classification and applies tags for governance.
IDE extension that provides real-time SQL completions based on the specific warehouse schema currently in focus.
Moving thousands of complex Oracle SQL queries to Snowflake.
Registry Updated:2/7/2026
Commit to GitHub.
Understanding 500-line legacy queries with zero comments.
Reducing monthly Snowflake costs caused by inefficient joins.