AI SQL Query Generator
Describe the data you need and your tables to get SQL with a line-by-line explanation, tried on sample data. It can also rewrite slow queries.
Example result
Write & optimize SQL
Write SQL for your need or speed up a slow query, explained clause by clause, run on sample data, with MySQL / PostgreSQL differences noted.
- Source
- Ofox
Use cases
Pull data for operations
Need the top 10 customers by spend over the last 30 days? Describe it in one sentence and get SQL with an explanation to check against your own tables.
Speed up slow queries
A report query keeps getting slower? Paste the query and schema and get a rewritten version with the reasoning behind it.
How it works
- 01
Fill in the blanks
Swap the highlighted parts for your own content, or keep the example.
- 02
Start
Hit the generate button and continue in Ofox Chat.
- 03
Keep refining
Not quite right? Say what to change and Ofox Chat keeps going.
FAQ
Which databases does it support?
The template prompt targets PostgreSQL and notes what would change for MySQL. Replace the highlighted database name with the one you use, such as MySQL, SQL Server, SQLite or BigQuery, and it writes for that dialect. Always test on your own database, because functions and syntax differ between versions.
Does it connect to my database?
No. It does not connect to your database or read your data. It works from the table and column descriptions you give it and shows the result on a few rows of sample data it makes up. Run the query on your own system to confirm the result.
What should I tell it about my tables?
Table names, the columns that matter with their types, and how the tables join, for example orders.user_id references users.id. Pasting the CREATE TABLE statements is the quickest way. The more exact the schema, the less it has to guess.
Can it optimize a slow query?
Yes. Paste the slow query, the table definitions and existing indexes, and the execution plan (EXPLAIN output) if you have it. It rewrites the query and explains the reasoning, such as a missing index or a subquery that can become a join. Compare timings before and after on real data.
Is it safe to run the generated SQL in production?
Not without review. Run it in a test environment first. For UPDATE and DELETE statements, use a transaction you can roll back and check the affected row count before you commit; schema changes such as ALTER or DROP cannot always be rolled back. Do not paste passwords, connection strings or real customer data into the conversation.
How much does it cost?
You need to sign up first, then you pay only for what you use.
About AI SQL Query Generator
The AI SQL query generator turns a plain-language request into a SQL query. Replace the highlighted parts in the box at the top of the page with what you want to find, the tables involved and your database, and OfoxAI writes the SQL, explains it clause by clause, shows the result on a few rows of sample data, and notes what would change for MySQL.
It suits people who know what data they need but do not write SQL every day: analysts pulling a one-off report, product managers checking a number, developers who want a second opinion on a join or a window function. The clause-by-clause explanation makes the query easier to review and change later.
A precise request gets a precise query. State the time range, the filters and how to handle edge cases: paid orders only, the last 30 days by order date, ties in a top 10, users with no orders. Ambiguity in the request is a common reason a query returns the wrong numbers.
For slow queries, give it the query, the table definitions and sizes, the indexes and the execution plan. It can suggest a rewrite or an index and explain the trade-off. Whether it is faster depends on your data, so measure both versions before changing anything in production.
The sample-data result is an illustration on invented rows, not proof that the query is correct. Before relying on a query, run it against your real schema, check the result against a number you already trust, and have a teammate review anything that modifies data.