AI app for it and development · no coding needed
Prompt-to-app delivery workspace with managed implementation
Reduce the number of tools and manual steps needed to turn a described application into a deployed, reviewable build.
Made for: Product teams and internal developers building custom web applications under delivery pressure
What it does for you
The problem
Assembling a working web application from prompts, components, data sources and deployment steps requires stitching together several rented tools and manual handoffs.
What it gives you
Reviewed, deployable application build with source references
What you give it
Natural-language descriptionscomponent choicesdata schemasdeployment targets
Build your own version of Dropbase, UBOS and more
One app with what these 6 AI tools do, yours to keep and change: Dropbase, UBOS, GPTEngineer, Pico, UI Bakery, Toolpad.
Everything these tools do, in one app
- AI-Powered App Generation Generates functional web applications from natural language descriptions or text prompts.Found in Dropbase, UBOS, GPTEngineer and 2 more
- Drag-and-Drop Interface Provides a visual builder to assemble applications by dragging and dropping components.Found in Dropbase, UBOS, Toolpad
- Pre-built Components Offers a library of ready-to-use UI components and templates to speed up development.Found in Dropbase, UBOS, Pico and 1 more
- Custom Code Injection Allows developers to add custom code to extend functionality and implement business logic.Found in Dropbase
- Self-Hosting Enables hosting the platform on your own infrastructure for control and security.Found in Dropbase, Toolpad
- Python Integration Supports importing and using Python packages (PyPI) within the application.Found in Dropbase
- Version Control Integrates with Git to track changes and manage code versions.Found in UBOS, GPTEngineer
- Database Support Connects to popular databases like MySQL, Postgres, and MongoDB for data management.Found in UBOS
- AI Agent Orchestration Allows building and coordinating AI agents to create intelligent workflows.Found in UBOS
- Live Rendering and Undo Provides real-time preview of changes and the ability to instantly undo actions.Found in GPTEngineer
- Collaborative Branching Supports multiple users working on different branches of the same project.Found in GPTEngineer
- Instant Deployment Deploys the application immediately to a unique URL upon creation.Found in Pico
- Custom Domain Support Allows using a custom domain for the deployed application.Found in Pico
- Full Lifecycle Support Covers the entire app lifecycle from prototyping to launch and usage tracking.Found in Pico
- Embedding and Integration Enables embedding the app into existing websites and integrating with external services.Found in Pico, UBOS, Toolpad
- Conversational Search Allows users to perform searches using natural language queries.Found in UI Bakery
- Backend Integration Connects seamlessly with existing backend services and APIs.Found in Toolpad
How it works, step by step
- Generate a functional web application from a natural-language description
- Assemble screens by dragging and dropping components
- Insert pre-built UI components and templates
- Add custom code for business logic
- Host the platform on buyer-controlled infrastructure
- Import and use Python packages from PyPI
- Track changes with Git-based version control
- Connect to MySQL, Postgres and MongoDB databases
- Orchestrate AI agents for multi-step workflows
- Preview changes live and undo actions instantly
- Support multiple users on separate project branches
- Deploy to a unique URL on creation
- Attach a custom domain to the deployment
- Track the app from prototype through launch and usage
- Embed the app into existing sites and connect external services
- Search projects and components with natural-language queries
- Connect to existing backend services and APIs
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Prompt-to-app delivery workspace with managed implementation with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Prompt-to-app delivery workspace with managed implementation with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the number of tools and manual steps needed to turn a described application into a deployed, reviewable build. For product teams and internal developers building custom web applications under delivery pressure, convert natural-language descriptions, component choices, data schemas and deployment targets into a reviewed, deployable application build with source references. The benefit is a testable hypothesis, measured through accepted builds per delivery hour and post-deployment corrections; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect natural-language descriptions, component choices, data schemas and deployment targets, then follow this sequence: 1. Generate a functional web application from a natural-language description. 2. Assemble screens by dragging and dropping components. 3. Insert pre-built UI components and templates. 4. Add custom code for business logic. 5. Connect to MySQL, Postgres and MongoDB databases. 6. Orchestrate AI agents for multi-step workflows. 7. Preview changes live and undo actions instantly. 8. Track changes with Git-based version control. 9. Support multiple users on separate project branches. 10. Deploy to a unique URL on creation. 11. Attach a custom domain to the deployment. 12. Embed the app into existing sites and connect external services. 13. Connect to existing backend services and APIs. 14. Track the app from prototype through launch and usage. 15. Search projects and components with natural-language queries. 16. Host the platform on buyer-controlled infrastructure. 17. Import and use Python packages from PyPI. Resolve uncertain cases with qualified reviewers, approve reviewed, deployable application build, and measure accepted builds per delivery hour and post-deployment corrections against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved component library and deployment target; final architecture, security and production readiness checks remain with qualified engineers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code accuracy and usage permissions. Named engineers approve substantive changes and deployment scope. One approved component library and deployment target; final architecture, security and production readiness checks remain with qualified engineers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved component library and deployment target; final architecture, security and production readiness checks remain with qualified engineers. Implement one approved input format, a bounded representative case set and the first two task modules: generate a functional web application from a natural-language description; assemble screens by dragging and dropping components. Support the third module with operator review: insert pre-built UI components and templates. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned repositories, authorized data sources and permitted deployment targets. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Application brief and references, Editable build preview, Review and deployment. Use a thumbnail gallery for projects, a large central canvas for drag-and-drop assembly and live preview, and a right-hand panel for components, data connections and comments. Let users compare generated and manual versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant screen or component. Make the task-specific outcome reviewed, deployable application build visible beside its evidence, review state and value baseline.
