AI app for it and development · no coding needed
Visual backend API and AI workflow delivery workspace
Reduce tool sprawl while keeping backend logic, data and AI calls in one owned workspace.
Made for: Product and platform teams building backend APIs and AI workflows without a full engineering squad
What it does for you
The problem
Backend APIs and AI workflows are assembled across several rented tools, so logic, credentials and logs are split and hard to own.
What it gives you
Reviewed, deployable backend API and AI workflow
What you give it
Approved workflow specsdata schemasmodel choicesaccess rules
Build your own version of BuildShip V2, Xano 2.0 and more
One app with what these 8 AI tools do, yours to keep and change: BuildShip V2, Xano 2.0, Prompteus, Tersa, Buildship, Alice 3.0, Logic, Inc., OneNode.
Everything these tools do, in one app
- Visual workflow builder Provides a drag-and-drop or visual canvas to create and manage workflows without coding.Found in BuildShip V2, Xano 2.0, Prompteus and 3 more
- Backend API generation Enables the creation of backend APIs through a visual or AI-assisted interface.Found in BuildShip V2, Xano 2.0, Buildship and 1 more
- AI model integration Connects to multiple AI models and services for tasks like language, image, and video processing.Found in Buildship, Prompteus, Tersa and 1 more
- Multi-LLM orchestration Allows orchestration and switching between multiple large language models within a single workflow.Found in Prompteus, Alice 3.0
- Database connectivity Connects to built-in or external databases for data management and retrieval.Found in Xano 2.0, Buildship
- Authentication Provides user authentication mechanisms for securing APIs and applications.Found in Xano 2.0, Prompteus
- Static hosting Offers hosting for static assets as part of the backend platform.Found in Xano 2.0
- Version control and Git sync Integrates with version control systems like Git for code synchronization and collaboration.Found in BuildShip V2, Xano 2.0, Buildship
- Testing and error reporting Provides tools to test workflows and identify errors early in development.Found in BuildShip V2
- Real-time monitoring Offers proactive alerts and insights into workflow performance.Found in BuildShip V2
- Task scheduling Allows scheduling of tasks to run automatically at specified times.Found in BuildShip V2, Buildship
- Pre-built nodes and templates Provides ready-made components and templates to accelerate development.Found in Buildship
- Code editing and scripting Allows editing of backend code directly within the platform or in external IDEs.Found in BuildShip V2, Xano 2.0, Buildship
- Caching Implements caching to reduce redundant calls and improve performance.Found in Prompteus
- Guardrails and compliance Enforces input/output validation and compliance rules to filter sensitive information.Found in Prompteus
- Logging and cost tracking Provides detailed logging and cost tracking for API usage.Found in Prompteus
- Document and video upload Supports uploading documents and videos as inputs for workflows.Found in Tersa
- Offline model usage Allows running AI models offline to ensure privacy and data security.Found in Alice 3.0
- Custom AI assistants Enables creation of tailored AI assistants with specific skills.Found in Alice 3.0
- Plain-English to automation Converts plain-English decision documents into runnable automations.Found in Logic, Inc.
- Zero-setup backend Provides a backend-as-a-service that requires minimal configuration.Found in OneNode
How it works, step by step
- Build workflows on a visual canvas
- Generate backend APIs from the canvas
- Connect multiple AI models and services
- Orchestrate and switch between LLMs in one workflow
- Connect built-in and external databases
- Add user authentication to APIs
- Host static assets
- Sync versions with Git
- Test workflows and report errors early
- Monitor runs with alerts and insights
- Schedule tasks at set times
- Reuse pre-built nodes and templates
- Edit backend code in the platform or an external IDE
- Cache repeated calls
- Enforce input and output guardrails
- Log usage and track cost per call
- Accept document and video uploads as inputs
- Run selected models offline
- Create custom AI assistants
- Convert plain-English decision documents into runnable automations
- Start from a zero-setup backend
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, deployable backend API and AI workflow 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 Visual backend API and AI workflow 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 Visual backend API and AI workflow 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 links5 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 while keeping backend logic, data and AI calls in one owned workspace. For product and platform teams building backend APIs and AI workflows without a full engineering squad, convert approved workflow specs, data schemas, model choices and access rules into a reviewed, deployable backend API and AI workflow. The benefit is a testable hypothesis, measured through deployed workflows per delivery week and rework after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect approved workflow specs, data schemas, model choices and access rules, then follow this sequence: 1. Build workflows on a visual canvas. 2. Generate backend APIs from the canvas. 3. Connect multiple AI models and services. Resolve uncertain cases with qualified reviewers, approve reviewed, deployable backend API and AI workflow, and measure deployed workflows per delivery week and rework after release against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 deployment target and credential set; final security and data-handling checks remain with the owning team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve access boundaries, credential handling, source attribution and usage permissions. The owning team approves substantive changes and deployment scope. One approved deployment target and credential set; final security and data-handling checks remain with the owning team. 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 deployment target and credential set; final security and data-handling checks remain with the owning team. Implement one approved input format, a bounded representative case set and the first two task modules: build workflows on a visual canvas; generate backend APIs from the canvas. Support the third module with operator review: connect multiple AI models and services. 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, approved data sources and permitted model providers. Cloud storage, Git providers, database connectors and deployment destinations. 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: Workflow canvas, API and data console, Run and monitoring view. Use a project list, a large central visual canvas, and a right-hand panel for nodes, schemas, credentials and comments. Let users compare workflow versions side by side. Display draft, in review and deployed states. Provide a run log with inputs, outputs and cost per execution. Make the task-specific outcome reviewed, deployable backend API and AI workflow visible beside its evidence, review state and value baseline.
