On-device multimodal model runtime console

For

Mobile app teams shipping AI features that must run privately on the device

Solves

Cloud inference sends user data off-device, fails offline, and forces separate builds per platform and model format.

Delivers

Reviewed on-device runtime configuration

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Run AI models directly on mobile devices for fast, private processing.

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.
8. Load GGUF, ONNX, CoreML and MLX model formats.
9. Convert and quantize models for target devices.
10. Support LLMs, ASR, embeddings and OCR out of the box.
11. Offer model sizes that trade speed against capability.
12. Keep the runtime open for community review and customization.
13. Minimize memory footprint on phones and laptops.
14. Generate text from prompts and style preferences.
15. Produce articles, summaries and creative writing in set formats.
16. Suggest text in real time while the user writes.
17. Learn from user feedback to improve later outputs.
18. Connect to content management and collaboration platforms.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed on-device runtime configuration with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   App requirements, target devices, model files and routing rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect app requirements

4. 3

   Target devices

5. 4

   Model files and routing rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed on-device runtime configuration

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, device profiles, model versions, routing rules, approval states, usage allowances, release limits, download history and a rights record for supplied model files. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: run models locally on the device without sending data to the cloud; support iOS and Android through one consistent API. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will mobile app teams shipping AI features that must run privately on the device use it to solve "cloud inference sends user data off-device, fails offline, and forces separate builds per platform and model format"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: On-device inference latency, offline completion rate and cloud calls avoided.
4. Measure, then decide. Track on-device inference latency and offline completion rate and cloud calls avoided; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported devices and model formats only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed on-device runtime configuration. Retain the explicit scope boundary: One target platform pair and one model family; final release and privacy decisions remain with the app team.

What the build depends on. Model upload and preview, asynchronous conversion jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist mobile QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One target platform pair and one model family; final release and privacy decisions remain with the app team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined device and model package. Offer a monthly runtime allowance after repeat demand. Quote complex multi-platform or specialist model work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed on-device runtime configuration. Recurring fees must specify device count, model volume and support depth. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run AI models directly on mobile devices for fast, private processing. Demonstrate a concrete reviewed on-device runtime configuration using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Mobile app teams shipping AI features that must run privately on the device professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed on-device runtime configuration from a small authorized input set, with a transparent calculation of on-device inference latency, offline completion rate and cloud calls avoided and no promised savings.

The first 30 days

1. Week 1: interview five mobile app teams shipping AI features that must run privately on the device and inspect a recent example of cloud inference sending user data off-device, failing offline, and forcing separate builds per platform and model format.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure on-device inference latency, offline completion rate and cloud calls avoided, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: On-device inference latency, offline completion rate and cloud calls avoided. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

On-device inference latency, offline completion rate and cloud calls avoided; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed on-device runtime configuration. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved device profiles, conversion settings and review examples, together with reliable delivery for a narrow mobile niche. Build a permissioned library of representative device cases, reviewer corrections and verified operating constraints for mobile app teams shipping AI features that must run privately on the device. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Lora, RunAnywhere, NexaSDK for Mobile and Gemma 3n. Compare this product with the buyer's present method on on-device inference latency, offline completion rate and cloud calls avoided. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model conversion runs, device test hours, storage, reviewer hours, client revision rounds and licensed model files. Additional initial validation requires representative authorized device samples, buyer interviews, buyer-side evaluation and bounded validation of reviewed on-device runtime configuration. Track cost per accepted configuration, including correction work, unsuccessful cases and support.

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.