To solve this, I built SeedSQL—a free tool to generate referentially accurate synthetic data in seconds.

Why I Built

ItMocking local database schemas usually causes three main headaches:

Broken Foreign Keys: Standard generators treat tables independently, creating orphaned relationships (Users \(\rightarrow\) Orders).

AI Limits: Prompting LLMs for large datasets gets slow, hits token caps, and yields non-deterministic outputs.

Formatting Hassles: Cleaning up raw text or CSVs into usable SQL takes extra time.

Key Features

Preserves Foreign Keys: Automatically maps parent-child primary keys across connected tables.

Flexible Data Types: Supports UUIDs, timestamps, custom regex patterns, localized names, and ranges.

Instant Export: Outputs ready-to-run INSERT statements (PostgreSQL, MySQL, SQLite) or structured JSON.

Deterministic Output: Re-run identical seeds across team dev setups and test pipelines.

I’d love to get feedback from developers using it:

• Try the tool: https://seed-sql.ai.studio/

• Feedback: What database engines or features would make your seeding workflow easier? Drop a comment below!

Testing web applications against realistic database states is a constant headache for developers. Standard options usually boil down to two bad choices: writing heavy custom mock scripts using libraries like Faker.js, or copying production database dumps locally—risking severe PII data leaks and compliance breaches.

To solve this exact problem, I built SeedSQL, an AI-powered synthetic data engine designed to generate relational, foreign-key-compliant mock datasets in seconds.

The Core Problem with Standard Mock Generators

Most traditional mock data tools treat database tables as isolated entities. While generating 1,000 random user names or fake email addresses is straightforward, real-world applications rely on strict relational structures:

Foreign Key Dependencies: Inserting an order row with an invalid user_id breaks constraint integrity immediately.

Realistic Business Logic: Mocking payment amounts or dates requires context; standard generators output nonsensical ranges.

Manual Configuration Time: Configuring schemas column-by-column across dozens of tables eats up valuable development hours.

How SeedSQL Solves Relational Seeding

SeedSQL approaches database seeding by first understanding your underlying database schema structure before generating a single row.

Automatic Schema Parsing: Upload raw DDL/SQL files or schema scripts directly. SeedSQL automatically maps tables, datatypes, and foreign key relations.

Preserved Relational Integrity: Primary and foreign keys line up automatically across tables (users, orders, payments), preventing FOREIGN KEY constraint errors during INSERT.

Zero Production Data Leakage: Keeps staging and development environments completely PII-free while maintaining production-like data distribution.

Instant SQL Exports: Generates standard, clean SQL INSERT scripts ready to run against PostgreSQL, MySQL, or SQLite databases.

Getting Started in 3 Steps

Import Your Schema: Paste your CREATE TABLE SQL statements into the web tool.

Configure Row Counts: Specify how many rows you need for each entity.

Export Clean SQL: Download your ready-to-execute .sql insert script and seed your local or staging database immediately.

Try SeedSQL Today

Whether you are running local integration tests, setting up staging environments, or preparing realistic database mocks for frontend demos, SeedSQL cuts out manual data generation scripts entirely.

Check out https://seed-sql.ai.studio and let me know your feedback in the comments!