AI Query

AI Query is a developer tool that turns plain English into working SQL for eight database engines. AI Query generates SQL from plain English prompts and explains existing SQL in simple language. It supports eight database engines including PostgreSQL, MySQL, BigQuery, Snowflake and Oracle, with schema definition, saved queries and history in a web dashboard.

AI Query is a browser based assistant that converts plain English requests into SQL statements and translates existing queries back into readable language. The pitch is narrow and specific: you describe what you want from your data in ordinary sentences, and the tool returns a query you can run. It leans on OpenAI GPT and Google PaLM 2 models under the hood, and the homepage frames the core promise as error free SQL generated in seconds. The workflow depends on schema context. Before generating anything useful, you define your database tables through a dashboard interface, which tells the model what columns and relationships exist. From there, generated queries, saved and shared queries, and a query history all live in the same workspace. A separate Explain SQL mode reverses the direction: paste a query you inherited from someone else and get a plain English walkthrough of what it does. For analysts who read more SQL than they write, that translator is arguably the more practical half of the product. Coverage spans PostgreSQL, MySQL, MariaDB, SQL Server, Google BigQuery, Snowflake, Oracle and Supabase as available engines. MongoDB, Aiven, CockroachDB, AlloyDB, Citus, Amazon RDS, Azure Database for PostgreSQL and Athena are listed as planned, so teams standardised on those systems are waiting on roadmap items rather than using the tool today. Positioning sits between autocomplete style SQL helpers built into IDEs and full BI platforms: it is lighter than a warehouse tool and less integrated than an editor plugin, which suits people writing ad hoc queries rather than maintaining a production data layer. The audience is broad by design. The homepage testimonials include data architects, analysts, IT managers and developers who describe limited SQL experience, and the marketing explicitly claims anyone can build efficient queries without prior knowledge. That framing is honest about the tradeoff: convenience and speed for people who do not want to learn syntax, and less appeal for engineers who already write complex joins fluently and would rather not define a schema by hand before every session.