Run AI directly on the device for speed, privacy, and control
We help you deploy models that run locally on iOS, Android, and other platforms. Your users get instant responses, sensitive data stays on device, and your app works offline.
Why run AI on the device instead of the cloud?
Cloud-based AI adds latency, requires connectivity, and sends user data over the network. On-device inference removes these constraints.
Faster responses, better UX
Local inference delivers predictable response times and smoother interactions, especially for real-time and highly interactive features where latency is immediately visible.
Faster responses, better UX
Local inference delivers predictable response times and smoother interactions, especially for real-time and highly interactive features where latency is immediately visible.
Privacy by default
Keeping inference on the device reduces data exposure and enables privacy-first features without relying on network calls or complex compliance.
Privacy by default
Keeping inference on the device reduces data exposure and enables privacy-first features without relying on network calls or complex compliance.
Works offline
Core AI experiences remain usable in airplane mode, poor reception, or unreliable networks, making the product more resilient in real-world conditions.
Works offline
Core AI experiences remain usable in airplane mode, poor reception, or unreliable networks, making the product more resilient in real-world conditions.
On-device titles and summaries for a multi-model chat app
ChatXOS is a chat app that lets users talk to and compare multiple AI models in one place. To improve everyday usability, the team uses our open source on-device AI library to generate chat titles and summaries locally, without sending this data to the cloud.
This keeps small but frequent interactions fast and helps make comparing model outputs feel smoother.
AI that runs locally, optimized for your devices
A locally running model optimized for real iOS and Android constraints, designed to deliver stable performance and unlock device-native capabilities without depending on the cloud.
Performance benchmarks
Measured latency, memory, and battery impact across target devices, so model behavior is predictable on real hardware.
Optimized runtime
A runtime and model format selected for your hardware constraints, keeping inference fast, stable, and memory-safe in real usage.
Model deployment
A model download flow built for mobile constraints, including background delivery, versioning, and safe rollbacks without requiring a full app release.
Fallback to built-in models
Platform-native models used as a baseline while larger models download or initialize, so AI works from first launch.
Hybrid execution
More complex requests can be routed to server-driven models when additional compute or context is required.
Offline-capable intelligence
On-device tool calling that can safely use local device capabilities and private data, such as calendar, health signals, files, and photo libraries.
Run on-device AI in React Native without locking yourself in
React Native AI is an open source toolkit for running AI models directly inside React Native apps. It supports multiple runtimes, and lets you switch between them with minimal effort, depending on your use case.
This makes it easier to experiment, compare approaches, and ship on-device features without locking yourself into a single stack.
Deploy AI models directly on iOS, Android, and other platforms
Deploy models on-device
We compile and deploy models across iOS, Android, and other platforms using runtimes like ExecuTorch, MediaPipe, Llama.cpp and MLX. Your AI features run locally, with consistent performance across devices.
Integrate built-in LLMs
We integrate Apple’s Foundation Models and other platform-native LLMs as privacy-first offline fallbacks. Your users get instant, local responses; no network required.
Fine-tune with LoRA adapters
We fine-tune large models for your app’s domain using LoRA (Low-Rank Adaptation) without full retraining required. Fast, cost-effective customization for your use case.
Profile and optimize models
We benchmark latency, memory, and battery impact per device tier, then apply the necessary optimizations, such as runtime, model format, or compression, to hit your performance targets.
Why leading companies work with Callstack
Meta Official Partners
We are official Meta partner for React Native, co-organizers of React Conf and hosts of React Universe Conf.
Working at scale
We know how to make React Native work at enterprise scale, both technically and organizationally.
React Native Core Contributors
We don’t just use React Native to build cross-platform apps, we help shape it for millions of developers.
Team at scale
We’re a team of 200+ engineers ready to start fast and scale with your needs.
Enterprise-friendly
We hold ISO certifications and follow strict security standards to make onboarding smooth and compliant.
Wide range of services
As a partner for your full application lifecycle, we offer a full range of services around React and React Native.
FAQs
On-device AI raises questions about compatibility, performance, and tradeoffs. Here are answers to the ones we hear most.
Most modern iOS and Android devices support on-device inference. We benchmark your target devices early to identify any constraints.
It depends on the device and use case. Small models (under 1GB) work well on most devices; larger models may require newer hardware or compression.
It can if not optimized. We profile power consumption and apply optimizations to keep inference efficient for real-world usage.
Yes. We design update strategies that let you push new models without requiring a full app release.
We design fallback strategies, such as using smaller built-in models or routing to the cloud when needed.
Yes. We choose runtimes optimized for each platform while ensuring consistent behavior across your user base.
Yes. We use techniques like LoRA fine-tuning to adapt models to your use case without full retraining.
We benchmark across device tiers and recommend which models and runtimes work for your minimum supported hardware.
Need AI that runs directly on your users’ devices?
We help teams deploy on-device models for privacy, speed, and offline capability. From runtime selection to optimization and rollout, we get your AI running locally across multiple platforms.
We’ve helped dozens of teams go cross-platform without rewriting everything. Let's see how we can help you.
