When generating realistic test datasets or staging environments, developers often hit a major compliance barrier: GDPR, HIPAA, and KVKK regulations.
Using production data for internal development carries enormous legal risk, while sending database schemas or sample rows to cloud-hosted LLM APIs frequently breaches enterprise data boundaries. To solve this, I built AI Synthetic Data Studio—an open-source, air-gapped synthetic data generator that runs completely offline on consumer hardware using local models via Ollama.
The Problem: Cloud APIs and Tabular Hallucinations
Generating realistic relational data with raw LLMs presents two core bottlenecks:
Schema & Data Leakage: Cloud APIs require ingesting your schema definitions, business logic, and prompt context over external servers. 2. Stochastic Failures: LLMs are non-deterministic. Under complex constraints (e.g., matching foreign keys, numeric bounds, custom regex patterns, or interdependent columns), raw LLM prompts hallucinate invalid types and broken integrity constraints.
The Architecture: Local LLMs + Deterministic Validation Layer
Instead of relying purely on prompt instructions, AI Synthetic Data Studio enforces a strict separation of concerns:
Local Semantic Generation: An air-gapped local model (via Ollama) handles natural language semantics, realistic naming, and context generation. 2. Deterministic Verification: Every generated record passes through an automated validation layer before writing to disk. This engine enforces data types, range bounds, and custom regex checks deterministically. 3. Automated Test Coverage: The project is backed by a 960+ test suite verifying schema parsers, constraint checkers, and export pipelines.
Running Fully Air-Gapped
The entire setup requires zero external network calls: