AI app for it and development · no coding needed
Backend code and API delivery workspace
Reduce tool sprawl and handoffs while keeping the codebase and data under the team's control.
Made for: Product teams and agencies building and maintaining backend services and APIs
What it does for you
The problem
Backend code, schemas, APIs and deployments are spread across separate tools, so teams rent several subscriptions and still hand-carry work between them.
What it gives you
Reviewed backend code, REST APIs, database schemas and deployment changes linked to pull requests
What you give it
Promptsspecificationsexisting schemasrepository contextdeployment targets
Build your own version of BackAnt, Jovu by Amplication and more
One app with what these 4 AI tools do, yours to keep and change: BackAnt, Jovu by Amplication, ob1 by Outerbase, Staff.rip.
Everything these tools do, in one app
- AI code generation Uses AI to generate backend code based on user input or prompts.Found in BackAnt, Jovu by Amplication, Staff.rip
- REST API generation Automatically creates RESTful APIs from prompts or specifications.Found in BackAnt, Jovu by Amplication
- Database schema creation Automatically generates database schemas for backend services.Found in Jovu by Amplication
- Browser-based interface Allows development directly in a web browser without local setup.Found in BackAnt
- Deployment and scaling Provides integrated deployment and scaling of backend services.Found in BackAnt
- Backend management tools Offers tools to oversee and manage backend operations.Found in BackAnt
- Customizable templates Provides templates that can be customized for different project needs.Found in Jovu by Amplication
- Database and framework integration Supports integration with popular databases and frameworks.Found in Jovu by Amplication
- Real-time collaboration Enables multiple developers to collaborate in real time.Found in Jovu by Amplication
- Advanced search Filters through various data sources to find relevant information.Found in ob1 by Outerbase
- Multi-platform integration Integrates with multiple platforms for seamless data collection.Found in ob1 by Outerbase
- Customizable dashboards Allows users to organize and visualize information with customizable dashboards.Found in ob1 by Outerbase
- Real-time data updates Keeps data fresh and relevant with real-time updates.Found in ob1 by Outerbase
- Simplified query interface Provides a user-friendly interface that simplifies complex queries.Found in ob1 by Outerbase
- Cross-stack AI agents AI agents operate across frontend, backend, microservices, infrastructure, data, and tests.Found in Staff.rip
- Local agent deployment Allows the AI agent to run locally so code remains on your machine.Found in Staff.rip
- Chat and click-to-edit Enables team members to interact with a running app via chat and click-to-edit.Found in Staff.rip
- PR workflow integration Integrates with standard engineering workflows by writing changes into pull requests.Found in Staff.rip
How it works, step by step
- Generate backend code from prompts or specifications
- Generate REST APIs from prompts or specifications
- Create database schemas for backend services
- Work in a browser-based interface without local setup
- Deploy and scale backend services
- Manage backend operations from one console
- Apply customizable project templates
- Integrate supported databases and frameworks
- Support real-time collaboration between developers
- Search across connected data sources
- Integrate multiple platforms for data collection
- Build customizable dashboards
- Keep data current with real-time updates
- Simplify complex queries through a guided interface
- Run cross-stack AI agents over frontend, backend, microservices, infrastructure, data and tests
- Run the agent locally so code stays on the machine
- Interact with a running app through chat and click-to-edit
- Write changes into pull requests for standard review
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed backend change set with source references and unresolved questions
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 Backend code and API delivery workspace 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 Backend code and API delivery workspace 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 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 tool sprawl and handoffs while keeping the codebase and data under the team's control. For product teams and agencies building and maintaining backend services and APIs, convert prompts, specifications, existing schemas and repository context into reviewed backend code, REST APIs, database schemas and deployment changes linked to pull requests. The benefit is a testable hypothesis, measured through accepted pull requests per developer hour and rework after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect prompts, specifications, existing schemas, repository context and deployment targets, then follow this sequence: 1. Generate backend code from prompts or specifications. 2. Generate REST APIs from prompts or specifications. 3. Create database schemas for backend services. 4. Run cross-stack AI agents over frontend, backend, microservices, infrastructure, data and tests. 5. Write changes into pull requests for standard review. Resolve uncertain cases with qualified reviewers, approve reviewed backend code, REST APIs, database schemas and deployment changes linked to pull requests, and measure accepted pull requests per developer hour and rework after merge 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 supported stack and database set; security review, data migration and production release remain engineering decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license compliance and usage permissions. Engineering owners approve substantive changes and production scope. One supported stack and database set; security review, data migration and production release remain engineering decisions. 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 supported stack and database set; security review, data migration and production release remain engineering decisions. Implement one approved input format, a bounded representative case set and the first three task modules: generate backend code from prompts or specifications; generate REST APIs from prompts or specifications; create database schemas for backend services. Support the remaining modules with operator review: run cross-stack AI agents over frontend, backend, microservices, infrastructure, data and tests; write changes into pull requests for standard review. 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
Customer-owned repositories, authorized specifications and permitted data sources. Cloud source control, CI pipelines, database engines and deployment targets. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Project and repository setup, Generation and review workspace, Deployment and operations. Use a project list with environment and branch state, a large central editor and diff canvas, and a right-hand panel for prompts, schema, templates and comments. Let users compare generated code against the current branch side by side. Display draft, changes requested and merged states. Provide a client preview link with comments anchored to the relevant file or endpoint. Make the task-specific outcome reviewed backend code, REST APIs, database schemas and deployment changes linked to pull requests visible beside its evidence, review state and value baseline.
