AI SQL Maestro

Manager types 'Show me users who signed up in March and spent over $100'

AI SQL Maestro maps terms to 'created_at' and 'order_total' columns

System generates and executes the JOIN query

Results are displayed in a formatted table with a download link

DBA pastes the query into the Optimizer

AI identifies a missing index on a join key

AI suggests refactoring subqueries into CTEs

The optimized query is benchmarked and validated

Import Oracle PL/SQL code

Select PostgreSQL as target dialect

AI rewrites logic including handling of proprietary Oracle functions

Human-in-the-loop validation of translated logic

AI scans the live schema

Generates descriptions for every table and column based on usage context

Produces an interactive ERD (Entity Relationship Diagram)

Exports to a Notion or Confluence page

Select target table schema

Define volume (e.g., 10,000 rows)

AI generates contextually accurate but fake data

Data is inserted into the staging environment

Connect repository to AI SQL Maestro

AI scans all SQL strings in the code

Flags unparameterized queries

Provides a pull request with the fixed, parameterized version

User asks: 'Graph our monthly revenue growth over the last year'

AI generates the SQL aggregation

AI selects the best chart type (Line Chart)

Renders the interactive chart directly in the UI