AI app for it and development · no coding needed
Multi-model comparison and clarification workspace
Reduce tool sprawl while keeping model choice, cost evidence and team context in one owned workspace.
Made for: Product teams, developers and analysts who compare and build with several AI models
What it does for you
The problem
Model access, comparison, cost tracking and team context are split across separate subscriptions and tools.
What it gives you
Reviewed model comparisons, routed answers and visual query results
What you give it
Permitted promptsfilesdata sourcesteam notes
Build your own version of AICamp, Mammouth and more
One app with what these 10 AI tools do, yours to keep and change: AICamp, Mammouth, Generatech AI, CheatGPT, Webdraw Beta, Okara, Airtrain.ai LLM Playground, Merlio, GMTech, LensQuery.
Everything these tools do, in one app
- Multi-model access Lets users use many different AI models from one platform instead of separate accounts.Found in Mammouth, Generatech AI, CheatGPT and 5 more
- Model switching Lets users change which AI model they are using on the fly.Found in CheatGPT, Okara, GMTech
- Side-by-side comparison Shows outputs from multiple AI models next to each other so users can compare them.Found in Airtrain.ai LLM Playground, GMTech
- No-code interface Lets users work with AI models without writing code.Found in Airtrain.ai LLM Playground, Webdraw Beta
- Single login Gives access to all included AI tools with one account and no extra authentication steps.Found in Generatech AI, Webdraw Beta
- Image generation Creates images from text prompts inside the platform.Found in CheatGPT, Okara, Merlio and 1 more
- File analysis Lets users upload and analyze documents and files within chats or apps.Found in Okara, Merlio, Webdraw Beta
- Integrated web search Searches the web and social platforms directly inside chats.Found in Okara
- Custom chat modes Provides task-specific chat setups for things like coding, essays, or marketing.Found in CheatGPT
- Automatic model routing Automatically picks the best AI model for each prompt.Found in Merlio
- Inference metrics Shows token counts, throughput, and cost for each model response.Found in Airtrain.ai LLM Playground
- Persisted chat sessions Saves chat sessions so users can review or resume them later.Found in Airtrain.ai LLM Playground
- Team collaboration Lets teams share context, memory, and a knowledge base.Found in Okara
- Data privacy Encrypts chats and states it does not train on user data.Found in Okara
- Project-based learning Teaches AI through hands-on real-world projects.Found in AICamp
- Mentorship Provides personalized guidance and feedback from mentors.Found in AICamp
- Natural language data querying Lets users ask questions about data in everyday language.Found in LensQuery
- Real-time data visualization Turns query results into visual charts and dashboards instantly.Found in LensQuery
How it works, step by step
- Access many AI models from one platform
- Switch the active model on the fly
- Show outputs from multiple models side by side
- Work without writing code
- Sign in once for all included tools
- Generate images from text prompts
- Upload and analyze documents and files
- Search the web and social platforms inside chats
- Provide task-specific chat modes
- Route prompts to a suitable model automatically
- Show token counts, throughput and cost per response
- Save chat sessions for later review
- Share team context, memory and a knowledge base
- Encrypt chats and exclude user data from training
- Teach AI through hands-on projects
- Provide mentor guidance and feedback
- Query data in everyday language
- Render query results as charts and dashboards
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed model comparisons, routed answers and visual query results 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 Multi-model comparison and clarification 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 Multi-model comparison and clarification 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 build3 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 criteria13 KB
- demo/index.htmlThe working demo on sample data194 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 model choice, cost evidence and team context in one owned workspace. For product teams, developers and analysts who compare and build with several AI models, convert permitted prompts, files, data sources and team notes into reviewed model comparisons, routed answers and visual query results. The benefit is a testable hypothesis, measured through accepted outputs per reviewer hour and cost per accepted output; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted prompts, files, data sources and team notes, then follow this sequence: 1. Access many AI models from one platform. 2. Switch the active model on the fly. 3. Show outputs from multiple models side by side. 4. Work without writing code. 5. Sign in once for all included tools. 6. Generate images from text prompts. 7. Upload and analyze documents and files. 8. Search the web and social platforms inside chats. 9. Provide task-specific chat modes. 10. Route prompts to a suitable model automatically. 11. Show token counts, throughput and cost per response. 12. Save chat sessions for later review. 13. Share team context, memory and a knowledge base. 14. Encrypt chats and exclude user data from training. 15. Teach AI through hands-on projects. 16. Provide mentor guidance and feedback. 17. Query data in everyday language. 18. Render query results as charts and dashboards. Resolve uncertain cases with qualified reviewers, approve reviewed model comparisons, routed answers and visual query results, and measure accepted outputs per reviewer hour and cost per accepted output 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. Model access depends on current provider terms; final model choice, cost decisions and data interpretations remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, data permissions and usage rights. Named owners approve substantive changes and external actions. One team workspace with a fixed set of permitted models; final model choice, cost decisions and data interpretations 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 team workspace with a fixed set of permitted models; final model choice, cost decisions and data interpretations remain human. Implement one approved input format, a bounded representative case set and the first two task modules: access many AI models from one platform; switch the active model on the fly. Support the remaining modules with operator review: show outputs from multiple models side by side; work without writing code; sign in once for all included tools; generate images from text prompts; upload and analyze documents and files; search the web and social platforms inside chats; provide task-specific chat modes; route prompts to a suitable model automatically; show token counts, throughput and cost per response; save chat sessions for later review; share team context, memory and a knowledge base; encrypt chats and exclude user data from training; teach AI through hands-on projects; provide mentor guidance and feedback; query data in everyday language; render query results as charts and dashboards. 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 prompts, files and data sources, plus permitted model provider APIs. 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: Workspace setup and model access, Comparison and clarification canvas, Team library and delivery. Use a thumbnail gallery for projects, a large central comparison canvas, and a right-hand panel for models, metrics, sources and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewed model comparisons, routed answers and visual query results visible beside its evidence, review state and value baseline.
