AI app for it and development · no coding needed
Managed custom model fine-tuning and deployment workspace
Reduce the engineering effort to reach a deployed, evaluated custom model.
Made for: Product and platform teams that need a tuned model on their own data but lack ML infrastructure staff
What it does for you
The problem
Teams cannot fine-tune, evaluate and deploy a model on their own data without renting several tools and hiring scarce ML engineers.
What it gives you
Deployed, evaluated custom model with recorded training runs and review gates
What you give it
Approved datasetsbase-model choicesevaluation criteria
Build your own version of TuneTrain.ai, Maruti.io and more
One app with what these 7 AI tools do, yours to keep and change: TuneTrain.ai, Maruti.io, BilberryDB, Vertical AI, Soup CLI, Unsloth, Humanloop.
Everything these tools do, in one app
- No-code fine-tuning Lets users fine-tune AI models without writing code.Found in TuneTrain.ai, Vertical AI
- No-code app building Enables building and deploying AI apps without coding.Found in BilberryDB
- Data augmentation Expands a small set of examples into a larger training dataset automatically.Found in TuneTrain.ai
- Small dataset efficiency Reduces the need for large labeled datasets by working well with few samples.Found in BilberryDB
- Multimodal data support Handles embeddings and search across 3D, images, video, audio, tabular, text, and IoT sensor data.Found in BilberryDB
- Vector embedding search Provides fast similarity queries across different data types.Found in BilberryDB
- Multiple base models Supports a range of open-source base models for fine-tuning.Found in TuneTrain.ai, Unsloth
- Flexible output formats Exports trained models in merged, LoRA, or QLoRA formats for deployment.Found in TuneTrain.ai
- Multiple training methods Offers various training objectives like SFT, DPO, GRPO, and KTO in one configuration.Found in Soup CLI
- Memory-efficient training Reduces GPU memory usage to enable fine-tuning on less powerful hardware.Found in Soup CLI, Unsloth
- Faster fine-tuning Speeds up the fine-tuning process compared to conventional methods.Found in Unsloth
- Built-in evaluation Includes evaluation and gating functionality to assess model performance.Found in Soup CLI
- Correctness verification Verifies that streamed training runs match resident runs exactly.Found in Soup CLI
- Reasoning capabilities Enhances model performance on complex tasks with reasoning-inspired training.Found in Unsloth
- Free Colab notebooks Provides ready-to-use notebooks for immediate access without costly setup.Found in Unsloth
- Prompt version control Tracks changes to prompts, datasets, and evaluators in a collaborative environment.Found in Humanloop
- Automated evaluations Integrates automatic evaluations into CI/CD pipelines to prevent regressions.Found in Humanloop
- Observability and feedback Captures user feedback and provides real-time alerting to identify issues.Found in Humanloop
- Model agnosticism Works with any AI provider, avoiding vendor lock-in.Found in Humanloop
- One-click fine-tuning Allows fine-tuning models with a single click.Found in Humanloop
- Tone and style customization Tailors the tone and style of language models to align with brand voice.Found in Humanloop
- Access to multiple models Provides access to a variety of AI models from different providers.Found in Vertical AI, Humanloop
- Decentralized computing Uses decentralized computing power for AI model training and operation.Found in Vertical AI
- Monetization marketplace Offers an integrated marketplace for monetizing AI creations.Found in Vertical AI
- Pay-as-you-go credits Uses a credit-based system for flexible payment instead of subscriptions.Found in Vertical AI
- Code generation Generates context-aware code snippets and boilerplate code.Found in Maruti.io
- Editor integration Integrates with popular code editors and development environments.Found in Maruti.io
- Real-time suggestions Provides real-time suggestions to improve code efficiency and readability.Found in Maruti.io
- Collaborative coding Supports collaborative coding to facilitate team workflows.Found in Maruti.io
How it works, step by step
- Configure fine-tuning without writing code
- Build and deploy the model-backed app without coding
- Augment a small example set into a larger training dataset
- Train usefully on small labeled datasets
- Handle embeddings and search across 3D, images, video, audio, tabular, text and sensor data
- Run fast similarity queries across those data types
- Select from multiple open-source base models
- Export merged, LoRA or QLoRA output formats
- Configure SFT, DPO, GRPO and KTO training objectives in one place
- Reduce GPU memory use for smaller hardware
- Speed up fine-tuning runs
- Evaluate and gate model versions before release
- Verify streamed training runs match resident runs exactly
- Apply reasoning-oriented training for complex tasks
- Provide ready Colab notebooks for immediate setup
- Version prompts, datasets and evaluators with review history
- Run automated evaluations in CI/CD to catch regressions
- Capture user feedback and alert on live issues
- Stay model-agnostic across providers
- Trigger one-click fine-tuning on an approved dataset
- Tune tone and style to the brand voice
- Access models from several providers
- Use decentralized compute for training and operation
- List and monetize approved model apps in a marketplace
- Bill through pay-as-you-go credits
- Generate context-aware code snippets and boilerplate
- Integrate with common editors and development environments
- Offer real-time code suggestions
- Support collaborative coding for team workflows
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned deployed, evaluated custom model 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 Managed custom model fine-tuning and deployment 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 Managed custom model fine-tuning and deployment 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 links5 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 data199 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 engineering effort to reach a deployed, evaluated custom model. For product and platform teams that need a tuned model on their own data but lack ML infrastructure staff, convert approved datasets, base-model choices and evaluation criteria into a deployed, evaluated custom model with recorded training runs and review gates. The benefit is a testable hypothesis, measured through accepted evaluation cases per engineering hour and regressions caught before release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect approved datasets, base-model choices and evaluation criteria, then follow this sequence: 1. Configure fine-tuning without writing code. 2. Augment a small example set into a larger training dataset. 3. Train with the chosen objective and memory-efficient settings. 4. Evaluate and gate the run against held-out cases. 5. Deploy the approved model and monitor live feedback. Resolve uncertain cases with qualified reviewers, approve the deployed, evaluated custom model, and measure accepted evaluation cases per engineering hour and regressions caught before release 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. Final model release, data rights and production deployment remain human decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve data rights, source attribution, license terms and usage permissions. Named owners approve model release and deployment scope. One approved dataset, one base model and one evaluation set; final release and data-rights 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 dataset, one base model and one evaluation set; final release and data-rights checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: configure fine-tuning without writing code; augment a small example set into a larger training dataset. Support the remaining modules with operator review: train, evaluate, gate and deploy. 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 datasets, code repositories, CI/CD pipelines, editors and development environments, model providers and cloud storage. 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: Dataset and base-model setup, Training run monitor, Evaluation and release gate. Use a project list, a central run view with loss and cost curves, and a right-hand panel for datasets, prompts, evaluators and comments. Let users compare runs and model versions side by side. Display draft, training, evaluated and released states. Provide a client preview link with comments anchored to the relevant run or evaluation case. Make the task-specific outcome a deployed, evaluated custom model visible beside its evidence, review state and value baseline.
