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AI Synthetic Data Studio
Local, air-gapped synthetic data generator via Ollama
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!
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?
About AI Synthetic Data Studio on Product Hunt
“Local, air-gapped synthetic data generator via Ollama”
AI Synthetic Data Studio was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #136 on the daily leaderboard. 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.
AI Synthetic Data Studio was featured in Open Source (68.9k followers), Developer Tools (520.1k followers), Artificial Intelligence (479.5k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 254.4k products, making this a competitive space to launch in.
Who hunted AI Synthetic Data Studio?
AI Synthetic Data Studio was hunted by Burak Yıldız. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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