AI app for it and development · no coding needed
Self-hosted reasoning model operations console
Run one owned model stack for reasoning, coding and other AI tasks under the team's own license, hardware and review rules.
Made for: Engineering teams that need to run and govern a large language model for reasoning, coding and other AI tasks
What it does for you
The problem
Teams rent several hosted model subscriptions and cannot inspect, self-host, fine-tune or govern the model their workflows depend on.
What it gives you
Source-linked accepted task outputs
What you give it
Open-weight model weightscodethe team's hardware profileevaluation casesusage policy
Build your own version of Mistral Small 3, Alpie Core and more
One app with what these 3 AI tools do, yours to keep and change: Mistral Small 3, Alpie Core, Trinity-Large-Thinking by Arcee.
Everything these tools do, in one app
- Open-source or open-weight The model's weights and code are openly available for inspection, modification, and use.Found in Mistral Small 3, Alpie Core, Trinity-Large-Thinking by Arcee
- Permissive license The model is released under a license that allows free use, modification, and redistribution.Found in Mistral Small 3, Alpie Core, Trinity-Large-Thinking by Arcee
- Local deployment The model can be run on your own hardware without relying on a hosted service.Found in Mistral Small 3, Alpie Core, Trinity-Large-Thinking by Arcee
- Hosted API access The model can be accessed via a managed API service.Found in Alpie Core
- Multi-step reasoning The model can perform complex reasoning tasks that require multiple steps.Found in Mistral Small 3, Alpie Core
- Coding tasks The model can assist with programming and code-related tasks.Found in Alpie Core
- Long context window The model can process very long inputs, such as large documents or extended dialogues.Found in Alpie Core
- Low latency The model generates responses quickly, suitable for real-time applications.Found in Mistral Small 3
- High throughput The model can generate tokens at a high rate, such as 150 tokens per second.Found in Mistral Small 3
- Efficient inference The model is optimized to run efficiently on practical hardware, reducing computational cost.Found in Mistral Small 3, Alpie Core
- Quantization support The model can be quantized to reduce memory and compute requirements, enabling deployment on less powerful hardware.Found in Mistral Small 3, Alpie Core
- No synthetic data The model was trained without synthetic data, which can improve reliability on reasoning tasks.Found in Mistral Small 3
- Fine-tuning support The model can be further trained on custom data to adapt to specific tasks.Found in Trinity-Large-Thinking by Arcee
- Model distillation support The model can be used to train smaller models through distillation.Found in Trinity-Large-Thinking by Arcee
- API compatibility The model is compatible with common API interfaces and tooling, making integration easier.Found in Alpie Core
- Benchmark performance The model achieves high scores on standard benchmarks, indicating strong general capabilities.Found in Mistral Small 3, Trinity-Large-Thinking by Arcee
- Low inference cost The model offers cost-effective inference, reducing operational expenses for large-scale use.Found in Alpie Core, Trinity-Large-Thinking by Arcee
How it works, step by step
- Register open-weight model versions and their licenses
- Deploy the model locally on approved hardware
- Expose a hosted API endpoint for internal callers
- Run multi-step reasoning tasks with visible steps
- Assist with coding tasks in the team's repositories
- Handle long context inputs such as large documents and extended dialogues
- Report latency and throughput per deployment
- Apply quantization profiles for constrained hardware
- Track inference cost per accepted output
- Record whether training data included synthetic data
- Run fine-tuning jobs on approved internal data
- Produce distilled smaller models for cheaper tasks
- Keep API compatibility with common interfaces and tooling
- Store benchmark results for each registered version
- 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 accepted task outputs record 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 Self-hosted reasoning model operations 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 Self-hosted reasoning model operations 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 Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria10 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
Run one owned model stack for reasoning, coding and other AI tasks under the team's own license, hardware and review rules. For engineering teams that need to run and govern a large language model for reasoning, coding and other AI tasks, convert an open-weight model, the team's hardware profile, evaluation cases and usage policy into a source-linked assistant and administrator console. The benefit is a testable hypothesis, measured through accepted task 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 open-weight model weights and code, the team's hardware profile, evaluation cases and usage policy, then follow this sequence: 1. Register open-weight model versions and their licenses. 2. Deploy the model locally on approved hardware. 3. Expose a hosted API endpoint for internal callers. 4. Run multi-step reasoning tasks with visible steps. 5. Assist with coding tasks in the team's repositories. Resolve uncertain cases with qualified reviewers, approve source-linked accepted task outputs, and measure accepted task 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. One approved hardware profile and one licensed model version per deployment; final code review and release decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve license terms, source attribution, code provenance and usage permissions. The engineering team approves substantive changes and release scope. One approved hardware profile and one licensed model version per deployment; final code review and release decisions remain with the engineering 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 hardware profile and one licensed model version per deployment; final code review and release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: register open-weight model versions and their licenses; deploy the model locally on approved hardware. Support the remaining modules with operator review: expose a hosted API endpoint for internal callers; run multi-step reasoning tasks with visible steps; assist with coding tasks in the team's repositories. 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 repositories, authorized internal documents and permitted evaluation sources. Cloud or on-premise compute, code hosting, CI pipelines and internal API gateways. 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: Model and license register, Assistant workspace, Administrator console. Use a model list for registered weights and versions, a central chat and code workspace with source links, and a right-hand panel for context, evaluation cases and policy. Let users compare model versions and quantization settings side by side. Display draft, changes requested and approved states. Provide an admin view of access, usage caps and export logs. Make the task-specific outcome source-linked accepted task outputs visible beside its evidence, review state and value baseline.
