Why I Built SeedSQL: Generating Schema-Aware Synthetic Data Without Leaking PII

Dhanush Gowda — Mon, 17 Aug 2026 09:28:15 +0000

Setting up realistic test data for local development and staging environments is a constant headache for developers.

When populating database environments, teams usually face a frustrating tradeoff:

Writing heavy mock scripts using libraries like Faker.js, which takes hours to configure and breaks whenever database schemas change.
Copying production database dumps, which carries massive risks of leaking real PII (Personally Identifiable Information) and violating strict compliance regulations.

To bridge this gap, I built SeedSQL—an AI-powered data engine designed to make seeding relational databases fast, realistic, and completely privacy-safe.

The Challenge with Generic Data Generators

Most generic mock generators treat database tables as isolated islands. Generating 1,000 fake user names or random emails is easy, but real production databases depend on tight relational rules:

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

• Contextual Data Types: Payment amounts need realistic decimal ranges, while order dates must follow logical user signup timelines.

• Schema Drift: Modifying your schema requires updating mock scripts across your entire test suite.

SeedSQL approaches data generation by analyzing your entire database schema upfront before creating a single row.

Key Features:

• Instant Schema Parsing: Paste your raw SQL DDL (CREATE TABLE statements) or upload schema files. SeedSQL automatically maps tables, datatypes, and foreign key relations.

• Foreign Key Integrity: Primary and foreign keys line up across dependent tables (users, orders, payments), preventing FOREIGN KEY constraint errors during INSERT.

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

• Clean SQL Exports: Generates standard, ready-to-run .sql insert scripts compatible with PostgreSQL, MySQL, and SQLite.

Getting Started in 3 Steps

Import Your Schema: Paste your DDL SQL statements into the editor.
Set Row Counts: Specify how many mock records you need per table.
Export & Run: Download your ready-to-execute .sql 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 demos, SeedSQL cuts out manual data generation scripts entirely.

Check out SeedSQL at seedsql.ai.studio and let me know your thoughts, questions, or feedback in the comments!

DEV Community: Dhanush Gowda

Why I Built SeedSQL: Generating Schema-Aware Synthetic Data Without Leaking PII