AI app for it and development · no coding needed
Plain-language multi-app automation delivery workspace
Reduce the technical effort to turn a described task into a running multi-step automation across apps.
Made for: Operations and IT teams who need multi-step automations across the apps they already use
What it does for you
The problem
Building multi-step automations across several apps needs technical work, and the tools that generate them are rented separately.
What it gives you
A reviewed, running automation the team owns
What you give it
Plain-language descriptionsapp credentialsapproved service access
Build your own version of Hipocap, Lutra and more
One app with what these 3 AI tools do, yours to keep and change: Hipocap, Lutra, TaskWand.
Everything these tools do, in one app
- Plain-language workflow creation Lets users describe the automation they want in everyday language instead of building it manually.Found in Hipocap, Lutra, TaskWand
- AI workflow generation Uses AI to turn the user's description into a complete automation workflow.Found in Hipocap, Lutra, TaskWand
- Multi-step automation Creates workflows that run several steps in sequence to complete a task.Found in Hipocap, TaskWand
- App integrations Connects with other applications so the automation can work across platforms.Found in Hipocap, Lutra
- Automatic task execution Runs the generated workflow automatically once it has been created.Found in Hipocap
- Custom API services Allows users to add their own API-based services to extend what can be automated.Found in Hipocap
- Ready-made workflows Provides pre-built automations that users can deploy quickly for common tasks.Found in Lutra
- Workflow scheduling Lets users schedule automations to run automatically at set times.Found in Lutra
- n8n workflow generation Produces ready-to-run n8n workflows from a text description.Found in TaskWand
- Conditions and loops Supports workflow logic such as conditional branches and repeated steps.Found in TaskWand
- Error handling Includes error handling in generated workflows so failures can be managed.Found in TaskWand
- Prompt improvement Refines the user's prompt to help produce more accurate workflow results.Found in TaskWand
- Workflow explanations Explains the generated workflow so users can understand what it does.Found in TaskWand
- Free trial tokens Gives free tokens on signup so users can try the service immediately.Found in TaskWand
How it works, step by step
- Capture the automation in plain language
- Refine the prompt before generation
- Generate a complete multi-step workflow
- Produce a ready-to-run n8n workflow
- Add conditions and loops
- Include error handling in generated steps
- Explain the generated workflow in plain terms
- Connect the apps the workflow must work across
- Add custom API-based services
- Offer ready-made workflows for common tasks
- Schedule automations to run at set times
- Execute the workflow automatically once approved
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, running automation the team owns 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 Plain-language multi-app automation 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 Plain-language multi-app automation 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 links3 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data198 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 technical effort to turn a described task into a running multi-step automation across apps. For operations and IT teams who need multi-step automations across the apps they already use, convert plain-language descriptions, app credentials and approved service access into a reviewed, running automation the team owns. The benefit is a testable hypothesis, measured through accepted automations per delivery hour and manual steps removed per workflow; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect plain-language descriptions, app credentials and approved service access, then follow this sequence: 1. Capture the automation in plain language. 2. Refine the prompt before generation. 3. Generate a complete multi-step workflow. 4. Produce a ready-to-run n8n workflow. 5. Add conditions and loops. 6. Include error handling in generated steps. 7. Explain the generated workflow in plain terms. 8. Connect the apps the workflow must work across. 9. Add custom API-based services. 10. Offer ready-made workflows for common tasks. 11. Schedule automations to run at set times. 12. Execute the workflow automatically once approved. Resolve uncertain cases with qualified reviewers, approve a reviewed, running automation the team owns, and measure accepted automations per delivery hour and manual steps removed per workflow against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate workflows 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 app set and one workflow runtime; final connection permissions and production runs remain under team control. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve app permissions, source attribution, credential handling and usage permissions. The team approves substantive changes and production scope. One approved app set and one workflow runtime; final connection permissions and production runs remain under team control. 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 app set and one workflow runtime; final connection permissions and production runs remain under team control. Implement one approved input format, a bounded representative case set and the first two task modules: capture the automation in plain language; refine the prompt before generation. Support the remaining modules with operator review: generate a complete multi-step workflow; produce a ready-to-run n8n workflow; add conditions and loops; include error handling in generated steps; explain the generated workflow in plain terms; connect the apps the workflow must work across; add custom API-based services; offer ready-made workflows for common tasks; schedule automations to run at set times; execute the workflow automatically once approved. 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
Team-owned app accounts, approved API credentials and permitted service endpoints. Cloud workflow storage, app connectors and execution destinations. Start with file exchange and validate destination specifications before promising direct execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Description and app access, Editable workflow preview, Run history and delivery. Use a list of automations, a central step-by-step workflow canvas, and a right-hand panel for connections, conditions and errors. Let users compare generated versions side by side. Display draft, changes requested and approved states. Provide a run log with each step's input, output and failure reason. Make the task-specific outcome a reviewed, running automation the team owns visible beside its evidence, review state and value baseline.
