On-device AI for mobile apps —
built for production, not proof-of-concept.
We design and ship React Native applications with embedded AI inference: ONNX models, local LLMs, and hybrid cloud/edge architectures. Your model runs on the device — fast, private, and offline-capable.
You have a model — or a clear AI use case
And you need a mobile app that runs it reliably in production. You may be:
Product team with a model
You have a PyTorch or TensorFlow model and need it running in Android and iOS.
Privacy-bound enterprise
Evaluating on-device AI for privacy or compliance reasons — PDPA, HIPAA, GDPR.
Mobile-first AI startup
Building a mobile-first AI product and looking for a technical delivery partner.
Japanese or regional enterprise
Looking for a reliable delivery team with bilingual capability.
Three patterns we ship to production
On-Device Inference Apps
We integrate ONNX Runtime into React Native, handling model bundling, preprocessing pipelines, tensor management, and postprocessing — end to end.
- Image classification and object detection (vision models)
- OCR post-processing and document understanding
- Text classification and intent detection
- Small/quantized LLMs for on-device chat, summarization, and form assistance
Hybrid AI Mobile Apps
Not everything needs to run on-device. We architect hybrid pipelines that use local inference for fast, private, everyday tasks — and fall back to a cloud LLM only when needed.
This balances cost, capability, and privacy.
Full-Stack Mobile AI
Beyond inference, we build the surrounding product:
- Camera and sensor integration for real-time AI input
- Offline data sync and local database (SQLite, MMKV)
- Secure model update and deployment pipelines
- REST/GraphQL backend integration
Five advantages of running AI on the device
Data privacy
No user data leaves the device.
Latency
No network round-trip — responses feel instant.
Offline use
Works in factories, farms, remote areas.
Cost
No per-request cloud inference fees.
Compliance
Simplifies PDPA, HIPAA, GDPR exposure.
Where on-device AI earns its keep
Manufacturing & Industrial
Fault code explanation, maintenance checklists, shift handover summaries — deployed on factory floors where connectivity is restricted and company data must stay on-premise.
Smart Farming & Field Inspection
Leaf disease detection, sensor reading summarization, treatment recommendations — combining vision models with LLM reasoning, designed for farms with unreliable internet.
Enterprise Internal Tools
Offline SOPs, incident report summarization, smart form autofill — where employees need AI assistance but IT policy prohibits cloud data transmission.
Healthcare & GovTech
OCR post-processing, form field explanation, eligibility decision reasoning — where PHI and citizen data must not reach external servers.
We work with what your project needs
Not a fixed framework. These are the patterns we reach for most often — on-device runtimes, model formats we know how to export and quantize, and accelerator paths that actually deliver mobile performance.
- Runtime: onnxruntime-react-native, ONNX Runtime iOS/Android
- Model formats: ONNX (from PyTorch, TensorFlow, Keras, scikit-learn)
- Quantization: INT8, dynamic quantization for mobile-optimized models
- Accelerators: NNAPI (Android), CoreML (iOS)
- Frameworks: React Native CLI, Expo (with custom dev client / prebuild)
- Backend (optional): FastAPI, Django, PostgreSQL, pgvector for hybrid pipelines
Discovery → architecture → build → handover
Technical Discovery (Free)
We review your model, use case, and target devices. We tell you what's feasible, what's not, and what tradeoffs exist — before any commitment.
Architecture & Scoping
We define the inference pipeline, preprocessing requirements, performance targets, and app architecture. You get a clear scope and timeline.
Build & Integrate
We build the mobile app with the AI pipeline integrated. We handle the hard parts: input/output tensor shapes, normalization mismatches, RGB/BGR issues, and mobile-specific performance tuning.
Testing & Handover
We test on real devices across Android and iOS. We document the model configuration, integration code, and update strategy. Your team can own it after delivery.
Frequently asked questions
Background reading
Field notes and engineering deep-dives from our work shipping AI to production — published on Simplico's blog.
The Production Gap: Why 80% of Enterprise AI Pilots Never Ship
What separates a working demo from a deployed system — the engineering, integration, and operational realities that kill most enterprise AI projects.
Choosing Hardware for Local LLMs in 2026: A Practical Sizing Guide
How to pick the right hardware for running LLMs locally — RAM, VRAM, quantization tradeoffs, and which models actually fit where.
Understanding YOLO: How It Works + Sample Code
A walkthrough of the YOLO object-detection model — architecture, training flow, and a working code sample to get started.
Introducing OCR Document Manager: Extract Text from Documents with Ease
A look at our OCR document manager — how it extracts text from images and PDFs, and where post-processing makes the difference.
Tell us about your model and use case
We'll respond within one business day.