An air-gapped, offline-first tool to generate schema-compliant synthetic tabular datasets locally via Ollama. No proprietary data ever leaks to cloud APIs (GDPR/KVKK compliant). Enforces deterministic validation, regex constraints, and cross-column rules to eliminate hallucinations. Backed by 960+ tests, with direct CSV, Parquet, and JSON export support.

Hey Product Hunt! 👋 I built AI Synthetic Data Studio because uploading internal records or test datasets to cloud LLMs poses serious compliance risks. I wanted a strictly offline tool that runs on local hardware using Ollama, while enforcing deterministic rules and cross-column validation so models can't hallucinate invalid data. The project is open-core, and I’d love to hear your feedback on the local inference workflow and validation rules!

Hey Product Hunt! 👋

I built AI Synthetic Data Studio because shipping real or mock schemas to cloud LLMs creates massive compliance and privacy risks. Meanwhile, purely probabilistic generation constantly hallucinates invalid types, broken foreign keys, and orphan records.

Before launching today, I shared the architectural foundation with technical communities on Reddit (r/ollama, r/LLMDevs). Seeing over 2,000 developers immediately dive in and validate this exact pain point convinced me that privacy-first, deterministic data generation is urgently needed.

Here is what AI Synthetic Data Studio brings to the table:

- 100% Local & Air-Gapped: Runs fully offline via Ollama. No API keys, no external telemetry, and zero schema leakage to cloud providers.

- Isolated Local Sandbox: Safe, controlled environment for continuous synthetic generation without risking production environments.

- Deterministic Validation Layer: Backed by 960+ automated unit and integration tests enforcing strict regex, type safety, boundary rules, and schema contracts.

- Community-Driven Architecture: Actively incorporating peer feedback (like topological dependency graphs and second-pass orphan resolvers) into our v2 roadmap.

I’d love to hear your thoughts, feedback, and edge cases. What’s your biggest hurdle when synthesizing local relational data?