Complete AI Training

AI app for operations · no coding needed

Agent-run administrative work coordination portal

Reduce tool switching and rework while keeping every agent action reviewable.

Made for: Operations leads and administrative teams coordinating research, analysis and finished deliverables across departments

What Agent-run administrative work coordination portal looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Administrative knowledge work is split across several rented agent tools, so context, files and approvals do not carry from research to finished deliverable.

What it gives you

Reviewed finished deliverables with a full session record

What you give it

Plain-language task requestsshared filesapproved sources

Build your own version of Raccoon AI, Saidar 2.0 and more

One app with what these 4 AI tools do, yours to keep and change: Raccoon AI, Saidar 2.0, Bika.ai, MagiCrew.

Everything these tools do, in one app

  • Natural language task automation Automate repetitive administrative tasks by giving commands in plain language.Found in Saidar 2.0
  • Multi-agent coordination Chat with and manage multiple AI agents simultaneously to handle different tasks.Found in Bika.ai, MagiCrew
  • Specialized agents for functions Use agents built for specific functions like research, presentations, or data analysis.Found in MagiCrew
  • Live agent with isolated computer Watch and interact with an agent running in an isolated environment with terminal, browser, and file system.Found in Raccoon AI
  • Unified workspace and memory Agents share files, context, and output so work from one feeds directly into another.Found in MagiCrew
  • Multi-step workflows Support chained tasks like research, analysis, and deliverable creation without switching tools.Found in Raccoon AI
  • Automation workflows Coordinate tasks across business areas such as sales, marketing, research, and design.Found in Bika.ai
  • Deliverable-ready outputs Produce finished work like polished decks and formatted reports ready for use.Found in MagiCrew
  • Multiple output modalities Generate research, data analysis with charts, pitch decks, web apps, images, and video.Found in Raccoon AI
  • File, image, and article generation Create files, images, and articles based on user requests.Found in Saidar 2.0
  • Scheduled reports and reminders Schedule and send automated reports and reminders.Found in Saidar 2.0
  • Deep research with updates Conduct deep research on specific topics and deliver regular updates.Found in Saidar 2.0
  • Wide app integrations Connect with many popular software platforms to extend functionality.Found in Raccoon AI, Saidar 2.0, Bika.ai
  • Custom MCP servers Use user-controlled custom MCP servers for sensitive systems.Found in Raccoon AI
  • Rewind and session history Inspect, restore, or audit past steps and recover deleted files.Found in Raccoon AI
  • Transparent session visibility See every action, file, and decision in the session for trust and debugging.Found in Raccoon AI
  • Sandboxed sessions Isolate agent activity to reduce accidental access to unrelated systems.Found in Raccoon AI
  • Integrated databases and documents Centralize organization with databases, dashboards, forms, and documents.Found in Bika.ai
  • No-code workflow building Build and customize AI-powered workflows without programming knowledge.Found in Bika.ai
  • Open-source codebase Inspect, modify, or self-host the software with publicly available source code.Found in MagiCrew

How it works, step by step

  1. Accept plain-language task commands
  2. Assign and manage multiple agents at once
  3. Route work to function-specific agents for research, analysis or presentations
  4. Run a live agent in an isolated environment with terminal, browser and file system
  5. Share files, context and output between agents
  6. Chain research, analysis and deliverable steps in one workflow
  7. Coordinate tasks across sales, marketing, research and design
  8. Produce polished decks and formatted reports
  9. Generate research, charts, decks, web apps, images and video
  10. Create files, images and articles on request
  11. Schedule automated reports and reminders
  12. Run deep research and deliver regular updates
  13. Connect to approved external software platforms
  14. Use customer-controlled MCP servers for sensitive systems
  15. Rewind, restore and audit past session steps
  16. Show every action, file and decision in the session
  17. Isolate agent activity in sandboxed sessions
  18. Centralize databases, dashboards, forms and documents
  19. Build workflows without programming knowledge
  20. Keep the codebase inspectable, modifiable and self-hostable

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 Agent-run administrative work coordination portal 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.

Sign in Become a member

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 Agent-run administrative work coordination portal with you.

Have Nexibeo build it

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 data196 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 switching and rework while keeping every agent action reviewable. For operations leads and administrative teams coordinating research, analysis and finished deliverables across departments, convert plain-language task requests, shared files and approved sources into reviewed finished deliverables with a full session record. The benefit is a testable hypothesis, measured through accepted deliverables per administrative hour and rework after approval; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect plain-language task requests, shared files and approved sources, then follow this sequence: 1. Accept plain-language task commands. 2. Assign and manage multiple agents at once. 3. Route work to function-specific agents for research, analysis or presentations. 4. Run a live agent in an isolated environment with terminal, browser and file system. 5. Share files, context and output between agents. 6. Chain research, analysis and deliverable steps in one workflow. Resolve uncertain cases with qualified reviewers, approve reviewed finished deliverables with a full session record, and measure accepted deliverables per administrative hour and rework after approval 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 task category and one deliverable format; final accuracy, compliance and external-send checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external sends. One approved task category and one deliverable format; final accuracy, compliance and external-send checks remain human. 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 task category and one deliverable format; final accuracy, compliance and external-send checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-language task commands; assign and manage multiple agents at once. Support the third module with operator review: route work to function-specific agents for research, analysis or presentations. 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 files, approved internal systems and permitted research sources. Cloud file storage, office and design-file import/export, and approved delivery 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: Task intake and agent assignment, Live session view, Deliverable review and delivery. Use a task board for requests, a central session canvas showing agent steps, files and decisions, and a right-hand panel for sources, approvals and comments. Let users compare draft and approved versions side by side. Display queued, running, changes requested and approved states. Provide a client preview link with comments anchored to the relevant deliverable. Make the task-specific outcome reviewed finished deliverables with a full session record visible beside its evidence, review state and value baseline.