AI app for it and development · no coding needed
Source-linked model workspace and admin console
Reduce the number of rented tools and keep model use inside one owned, auditable workspace.
Made for: Product and platform teams building AI features who need a language model they can run, inspect and administer themselves
What it does for you
The problem
Teams rent several model APIs and data tools, cannot see how outputs are produced, and cannot tune the workflow to their own review and data rules.
What it gives you
Source-linked drafts, code, analyses and reports
What you give it
Permitted documentscodedatasetsimages
Build your own version of GLM-4.5, DBRX and more
One app with what these 7 AI tools do, yours to keep and change: GLM-4.5, DBRX, GPT-4.1 in the API, QWQ-Max, OpenAI Open Models, LongCat-2.0, Qwen 1.5 MoE.
Everything these tools do, in one app
- Text generation and understanding Generates and comprehends natural language text for diverse tasks.Found in GLM-4.5, GPT-4.1 in the API, OpenAI Open Models and 1 more
- Coding and instruction following Writes code and follows detailed instructions accurately.Found in GLM-4.5, GPT-4.1 in the API, LongCat-2.0
- Reasoning tasks Handles complex reasoning and problem-solving tasks.Found in GLM-4.5, OpenAI Open Models
- Agentic functions Performs goal-oriented tasks autonomously.Found in GLM-4.5, OpenAI Open Models, LongCat-2.0
- Large context window Processes very long documents and extended interactions.Found in GLM-4.5, GPT-4.1 in the API, LongCat-2.0
- Open-source licensing Allows free use, modification, and redistribution under open licenses.Found in GLM-4.5, OpenAI Open Models, LongCat-2.0
- Multiple model variants Offers different model sizes to balance speed, cost, and capability.Found in GLM-4.5, GPT-4.1 in the API, OpenAI Open Models
- Mixture-of-experts architecture Activates subsets of parameters for efficient computation.Found in LongCat-2.0, Qwen 1.5 MoE
- Automated data processing Cleans, transforms, and processes data automatically.Found in DBRX, QWQ-Max
- Data visualization Creates customizable charts and dashboards for insights.Found in DBRX, QWQ-Max
- Collaborative workspace Enables team members to work on projects simultaneously.Found in DBRX
- Predictive analytics Uses built-in AI models for trend detection and predictions.Found in DBRX
- Third-party integrations Connects with databases, cloud storage, and other applications.Found in DBRX, QWQ-Max, LongCat-2.0
- Interactive dashboards Provides real-time insights through interactive interfaces.Found in QWQ-Max
- Advanced reporting Generates reports in multiple formats.Found in QWQ-Max
- Drag-and-drop interface Simplifies usage with a user-friendly drag-and-drop design.Found in QWQ-Max
- Vision and image understanding Processes and understands images alongside text.Found in GPT-4.1 in the API
- API integration Supports deployment and scalability through APIs.Found in GPT-4.1 in the API, OpenAI Open Models
How it works, step by step
- Generate and understand natural language text
- Write code and follow detailed instructions
- Handle multi-step reasoning and problem-solving tasks
- Run goal-oriented agentic steps under named-owner approval
- Process long documents and extended interactions in a large context window
- Offer open-source licensing for use, modification and redistribution
- Provide multiple model variants for speed, cost and capability
- Use mixture-of-experts routing for efficient computation
- Clean, transform and process supplied data automatically
- Build customizable charts and dashboards
- Support a collaborative workspace for simultaneous project work
- Run predictive analytics for trend detection
- Connect databases, cloud storage and third-party applications
- Provide interactive dashboards with real-time insights
- Generate reports in multiple formats
- Offer a drag-and-drop builder for non-specialists
- Process and understand images alongside text
- Expose API access for deployment and scaling
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned source-linked output 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 Source-linked model workspace and admin console 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 Source-linked model workspace and admin console 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 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 the number of rented tools and keep model use inside one owned, auditable workspace. For product and platform teams building AI features, convert permitted documents, code, datasets and images into source-linked drafts, code, analyses and reports under named-owner review. The benefit is a testable hypothesis, measured through accepted outputs per reviewer hour and corrections after approval; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted documents, code, datasets and images, then follow this sequence: 1. Generate and understand natural language text. 2. Write code and follow detailed instructions. 3. Handle multi-step reasoning and problem-solving tasks. 4. Run goal-oriented agentic steps under named-owner approval. Resolve uncertain cases with qualified reviewers, approve source-linked drafts, code, analyses and reports, and measure accepted outputs per reviewer hour and corrections 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. Model variant, context limits and routing are configuration choices; final code, data and report checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code provenance, data permissions and usage rights. Named owners approve substantive changes and deployment scope. One model variant set, one approved input format and one bounded representative case set. 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 model variant set, one approved input format and one bounded representative case set. Implement the first three task modules: generate and understand natural language text; write code and follow detailed instructions; handle multi-step reasoning and problem-solving tasks. Support agentic steps and data processing with operator 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, databases, cloud storage and permitted document sources. 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: Workspace and model selection, Source-linked assistant, Data and dashboard builder, Admin console. Use a project list, a large central assistant and editor canvas, and a right-hand panel for sources, model variant, constraints and comments. Let users compare model variants and versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source. Make the task-specific outcome source-linked drafts, code, analyses and reports visible beside its evidence, review state and value baseline.
