AI app for it and development · no coding needed
On-device multimodal model runtime console
Run AI models directly on mobile devices for fast, private processing.
Made for: Mobile app teams shipping AI features that must run privately on the device
What it does for you
The problem
Cloud inference sends user data off-device, fails offline, and forces separate builds per platform and model format.
What it gives you
Reviewed on-device runtime configuration
What you give it
App requirementstarget devicesmodel filesrouting rules
Build your own version of Lora, RunAnywhere and more
One app with what these 4 AI tools do, yours to keep and change: Lora, RunAnywhere, NexaSDK for Mobile, Gemma 3n.
Everything these tools do, in one app
- On-device processing Runs AI models locally on the device without sending data to the cloud, ensuring privacy and offline operation.Found in RunAnywhere, NexaSDK for Mobile, Gemma 3n
- Cross-platform support Works on both iOS and Android with consistent APIs, simplifying development for multiple platforms.Found in RunAnywhere, NexaSDK for Mobile
- Multimodal input handling Processes multiple types of input such as text, images, audio, and video within a single model.Found in NexaSDK for Mobile, Gemma 3n
- Hardware acceleration Automatically uses available hardware like NPUs, GPUs, and CPUs to speed up inference and improve energy efficiency.Found in NexaSDK for Mobile
- Cloud fallback routing Decides per request whether to process on-device or via cloud services to optimize privacy, speed, and cost.Found in RunAnywhere
- Dynamic model updates Allows updating models, prompts, and routing rules without requiring app updates.Found in RunAnywhere
- Real-time analytics Provides analytics and A/B testing to monitor performance and user engagement.Found in RunAnywhere
- Multiple model formats Supports various model formats such as GGUF, ONNX, CoreML, and MLX for flexibility.Found in RunAnywhere
- Model conversion pipeline Converts and quantizes models to make them compatible across different devices.Found in NexaSDK for Mobile
- Built-in model support Includes out-of-the-box support for a wide range of model types like LLMs, ASR, embeddings, and OCR.Found in NexaSDK for Mobile
- Flexible model sizes Offers different model sizes to balance speed and capability based on device resources.Found in Gemma 3n
- Open source Encourages community involvement and customization through open development.Found in Gemma 3n
- Efficient memory usage Uses techniques to minimize memory footprint, making it suitable for phones and laptops.Found in Gemma 3n
- Customizable content generation Generates text based on user prompts and style preferences, allowing personalized content.Found in Lora
- Multiple content formats Supports generating articles, summaries, and creative writing in various formats.Found in Lora
- Real-time text suggestions Provides suggestions to enhance writing flow and reduce effort.Found in Lora
- Learning from feedback Improves future outputs by learning from user feedback.Found in Lora
- Integration with platforms Integrates with popular content management and collaboration platforms.Found in Lora
How it works, step by step
- Run models locally on the device without sending data to the cloud
- Support iOS and Android through one consistent API
- Handle text, image, audio and video input in a single model
- Use NPUs, GPUs and CPUs automatically for speed and energy efficiency
- Route each request to device or cloud by privacy, speed and cost rules
- Update models, prompts and routing rules without app releases
- Report analytics and A/B test results per device and model version
- Load GGUF, ONNX, CoreML and MLX model formats
- Convert and quantize models for target devices
- Support LLMs, ASR, embeddings and OCR out of the box
- Offer model sizes that trade speed against capability
- Keep the runtime open for community review and customization
- Minimize memory footprint on phones and laptops
- Generate text from prompts and style preferences
- Produce articles, summaries and creative writing in set formats
- Suggest text in real time while the user writes
- Learn from user feedback to improve later outputs
- Connect to content management and collaboration platforms
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed on-device runtime configuration 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 On-device multimodal model runtime 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 On-device multimodal model runtime 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 data197 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 AI models directly on mobile devices for fast, private processing. For mobile app teams shipping AI features that must run privately on the device, convert app requirements, target devices, model files and routing rules into a reviewed on-device runtime configuration with source-linked evidence. The benefit is a testable hypothesis, measured through on-device inference latency, offline completion rate and cloud calls avoided; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect app requirements, target devices, model files and routing rules, then follow this sequence: 1. Run models locally on the device without sending data to the cloud. 2. Support iOS and Android through one consistent API. 3. Handle text, image, audio and video input in a single model. 4. Use NPUs, GPUs and CPUs automatically for speed and energy efficiency. 5. Route each request to device or cloud by privacy, speed and cost rules. 6. Update models, prompts and routing rules without app releases. 7. Report analytics and A/B test results per device and model version. Resolve uncertain cases with qualified reviewers, approve reviewed on-device runtime configuration, and measure on-device inference latency, offline completion rate and cloud calls avoided 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 conversion, quantization and routing rules stay under named owner review; final release decisions remain with the app team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve user data on-device, source attribution, model licenses and usage permissions. The app team approves substantive changes and release scope. One target platform pair and one model family; final release and privacy decisions remain with the app 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 target platform pair and one model family; final release and privacy decisions remain with the app team. Implement one approved model format, a bounded representative device set and the first two task modules: run models locally on the device without sending data to the cloud; support iOS and Android through one consistent API. Support the third module with operator review: handle text, image, audio and video input in a single model. 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
App-owned model files, authorized device test farms and permitted model sources. Cloud asset storage, app build pipelines and analytics 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: Device and model registry, Runtime configuration, Evaluation and rollout console. Use a list of target devices and installed models, a central panel for routing rules and conversion settings, and a right-hand panel for logs, latency and privacy flags. Let users compare on-device and cloud-fallback runs side by side. Display draft, tested and released states. Provide a source-linked trace for each request decision. Make the task-specific outcome reviewed on-device runtime configuration visible beside its evidence, review state and value baseline.
