Itura – Build and use database-backed apps in chat

Hacker News

I built Itura, a way to define an app in plain English and get a working application with its own secure database and an AI that enforces your rules. You then use the app through conversation (create/query/update data with role-based permissions). How Itura differs from no-code/low-code: - No drag-and-drop UI, no workflow builders. You write a plain-English spec; Itura generates the app and database. - The final app is chat-native, not a form-based UI. - Real RBAC + row-level security; every action respects roles and constraints. How Itura differs from chatbots (e.g., ChatGPT): - Itura creates a persistent, multi-user, role-aware application with its own database. - The assistant is bound by your app’s schema, permissions, and constraints; it executes allowed CRUD operations instead of giving generic answers. - End-users collaborate inside that app rather than a single general-purpose chat. Example use cases: - Lightweight CRM: log leads, notes, next steps; managers see team-wide views with RBAC. - Applicant tracking: candidates, stages, feedback; hiring leads control access. - Inventory/asset tracker: add items, update stock, audit changes; staff vs admin roles. - Content pipeline: ideas → drafts → published; editors approve and schedule. Try it: Open beta with a free tier; Pro is $20/mo. Feedback on the builder flow, permissions model, and ambiguous-query handling is very welcome.

Visit product View on Hacker NewsAug 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, answers, users · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: builder, users · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, collaborate · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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