OnSpace AI: Build a Mobile App Without Code, End to End
OnSpace AI lets you build, test and publish AI-powered mobile apps by chatting in plain English. Here's how the workflow actually works.
What is OnSpace AI?
OnSpace AI is a no-code platform for building mobile apps, web apps, and AI agents through a chat interface. Instead of dragging components onto a canvas like older no-code tools, you describe what you want in plain language and the platform generates the working app, including the frontend, backend, database, and AI features. What sets it apart from generic no-code builders is that it’s built specifically for apps that rely on AI models. It ships with a built-in library of AI models you can plug into an app directly, without creating your own API keys or standing up a backend to protect them.
TL;DR
- Chat-driven app building lets you describe an app in natural language and get a working preview with multiple screens, navigation, and logic already wired together.
- Built-in AI model access means features like an AI chat tutor can be added without ever touching an OpenAI or Anthropic API key or configuring a server to hide it.
- A runtime scanner catches integration bugs between different parts of the generated app and offers one-click fixes instead of requiring you to describe the error yourself.
- A self-developed database handles user accounts, authentication, and data storage in the same dashboard, so there’s no separate Supabase or Firebase setup.
- Monetization is built in, with in-app purchase support through RevenueCat and an alternative Stripe integration for paywalls and subscriptions.
- Publishing is largely automated, with app store submission handled by the platform and instant APK or AAB downloads available for Android testing and Google Play.
- Support resources include 24/7 AI customer support, human follow-up within a business day, and a weekly tutorial series on OnSpace’s YouTube channel.
Remy doesn't write the code. It manages the agents who do.
Remy runs the project. The specialists do the work. You work with the PM, not the implementers.
How does OnSpace turn a prompt into an app?
The workflow starts with a single descriptive prompt. In a demonstrated example, a prompt described an AI language tutor app called LingoPal, specifying a chat tab where users practice Spanish with an AI that corrects mistakes and explains them, a practice tab with quizzes generated from past errors, and a progress tab with stats. After submitting that prompt, OnSpace took a few minutes to generate the app, producing a live preview with all three tabs styled in a modern layout.
This is the core loop: you describe a feature or app in a sentence or paragraph, OnSpace generates the code and interface, and you review the result in a live preview panel. Follow-up prompts add or adjust features. In the demo, additional prompts added sign-up and login with email authentication, and later added a subscription paywall, each building on top of what was already generated.
What makes it “AI native” instead of just another no-code tool?
The distinguishing feature is direct access to AI models inside the generated app, without the usual setup work. Normally, adding an AI chat feature to an app means creating an account with a model provider, generating an API key, building a backend to keep that key secure, handling the responses, and managing rate limits. In the OnSpace demo, none of that was necessary. A single prompt asking for an AI tutor in the chat tab was enough, and the resulting app used a real language model to generate responses, correct grammar mistakes in Spanish sentences, and explain the corrections in real time.
This matters because AI-powered features are usually the hardest part of building an app that isn’t purely code-based work, it’s infrastructure work: key management, security, and backend routing. By folding model access directly into the builder, OnSpace removes a layer of technical setup that would otherwise require real backend development experience.
How does the runtime scanner catch bugs?
AI-generated code tends to work well feature by feature, but problems show up when different parts of an app need to interact. A quiz screen might depend on data structures the chat screen produces slightly differently, for example. This kind of integration bug is common in human-written software too, but it’s usually where no-code AI tools fall apart, because the user has no way to diagnose or describe the problem to the AI that generated it.
OnSpace addresses this with a runtime scanner. You turn it on and manually click through each screen of your app. The scanner monitors what happens during that navigation and flags errors it detects, in the demonstrated case, an issue on the quiz screen. Rather than asking the user to paste error logs or describe symptoms, it offers a “fast fix” option that patches the code automatically. This shifts debugging from a technical, log-reading task to a click-through, click-to-fix task.
How does authentication and the database work?
- ✕a coding agent
- ✕no-code
- ✕vibe coding
- ✕a faster Cursor
The one that tells the coding agents what to build.
Adding user accounts is handled the same way as any other feature: a prompt asking for sign-up and login with email authentication, with each user’s chat history and progress kept private to their account. OnSpace builds this using its own database rather than requiring a connection to an external service like Supabase or Firebase.
In practice, this looked like a standard authentication flow: signing up with an email address triggered a verification code sent to the inbox, and entering that code logged the user in. A dashboard view let the builder inspect the backend directly, showing the new user record alongside database tables for chat history and quiz results. Everything related to data, users, and app logic lived in one interface, without switching between a builder tool and a separate database console.
Is OnSpace AI worth it for monetizing an app?
For anyone planning to charge for an app, OnSpace includes native support for in-app purchases through a RevenueCat integration, letting a builder lock specific features (like a quiz tab in the demo) behind a premium subscription. Testing the purchase flow triggers the same kind of payment prompt users would see in any commercial app. Stripe is also supported as an alternative payment method, with dedicated tutorials covering that setup on OnSpace’s YouTube channel.
Whether this is “worth it” depends on how far you want to go without hiring a developer. The value proposition is clearest for solo builders, indie hackers, or small teams who want to validate an app idea, including one with real AI features and real payments, without committing to a traditional development cycle. It’s less clear how it holds up for apps needing highly custom logic, complex integrations outside the platform’s built-in model library, or scale beyond what a no-code backend can handle. Those tradeoffs are common to no-code platforms generally and aren’t unique to OnSpace.
How does publishing to the app store actually work?
Publishing is typically the step that stalls no-code projects. Submitting a React Native app to the Apple App Store manually involves Expo’s build process, provisioning profiles, certificates, and Apple’s submission requirements, a process that’s difficult even with documentation in hand if you don’t have a technical background.
In the OnSpace workflow, submission is handled by the platform itself, with the demonstrated process taking roughly 10 to 15 minutes to process before the app appears in the app store. For Android, the platform offers instant APK downloads for side-loading and testing on a physical device, along with AAB file generation for direct publishing to Google Play.
Frequently Asked Questions
What is OnSpace AI used for?
It’s used to build mobile apps, web apps, and AI agents by describing them in natural language chat prompts, rather than writing code or using traditional drag-and-drop no-code editors.
Do I need my own API keys for AI features in OnSpace?
No. OnSpace provides direct access to a library of AI models inside the apps it builds, so features like AI chat don’t require setting up your own API key or backend to secure it.
Can OnSpace apps have logins and a database?
Yes. You can add email-based sign-up and login through a prompt, and OnSpace stores user data, chat history, and other records in its own built-in database, viewable from the same dashboard.
Built like a system. Not vibe-coded.
Remy manages the project — every layer architected, not stitched together at the last second.
Does OnSpace support charging users for an app?
Yes. It integrates with RevenueCat for in-app purchases and subscriptions, and also supports Stripe as an alternative payment method, with paywalls generated from a prompt.
How long does it take to publish an app built with OnSpace?
App store submission was shown taking roughly 10 to 15 minutes to process. Android APK and AAB files are available for immediate testing or direct Google Play publishing.
