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Onisin OS
Natural language meets your domain — locally
Onisin OS turns your own database into a chat. You model your domain — persons, claims, tickets, cases — in a small text language; Onisin OS builds the GraphQL schema, the forms and the semantic index from it automatically. A ReAct agent translates plain-language questions into queries, returns tables, detail forms and pipeline reports. Everything runs locally: your data, your LLM, your network. No byte leaves the building.
Hey Product Hunt 👋
I'm Frank, and Onisin OS is what happens when you spend a few years building case-analysis software for insurance and police and keep running into the same wall: every project rebuilds the same things — a data model, a CRUD UI, a search box, some reports. So I stopped rebuilding and started describing.
In Onisin OS, you model your domain in a small text language — persons, cases, tickets, whatever you work with. From that one source of truth, the system derives the GraphQL schema, the forms users see, the semantic index, and the event streams. A ReAct agent turns natural-language questions into actual queries against your data. Pipelines combine structured data, documents and LLM steps into reports.
Everything runs locally. Your data stays on your network. Bring your own LLM — OpenAI, Anthropic, Ollama, vLLM, anything that speaks the OpenAI API.
What I'd love to hear: where does this break for your use case? Which domain would you try to model first? And — because this is the question that keeps me up at night — does the chat-first interaction actually work for your users, or do they still want the buttons?
Happy to answer anything in the comments.
About Onisin OS on Product Hunt
“Natural language meets your domain — locally”
Onisin OS was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #90 on the daily leaderboard. Onisin OS turns your own database into a chat. You model your domain — persons, claims, tickets, cases — in a small text language; Onisin OS builds the GraphQL schema, the forms and the semantic index from it automatically. A ReAct agent translates plain-language questions into queries, returns tables, detail forms and pipeline reports. Everything runs locally: your data, your LLM, your network. No byte leaves the building.
On the analytics side, Onisin OS competes within Productivity, Developer Tools and GitHub — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Onisin OS performed against the three products that launched closest to it on the same day.
Who hunted Onisin OS?
Onisin OS was hunted by Onisin. 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.
For a complete overview of Onisin OS including community comment highlights and product details, visit the product overview.
Onisin