AI, React Native
On-Device AI Beats Cloud for TTS – Here’s Why
Mateusz Kopciński• On-Device AI & Local AI Development
Run AI models locally
If your AI feature depends on a network connection, it might fail at the worst possible moment. We build the alternative: inference that runs on the device, owned by the app, independent of any external service.
Latency, privacy, cost, and offline reliability change the product equation
Teams pick local AI because it removes the network from the critical path, keeps sensitive data on the device, and changes the economics of features that run all day.
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Latency that cloud APIs cannot match
Whisper-tiny encodes 30 seconds of audio in 89ms on-device. A cloud API round trip alone typically takes 200-800ms, before processing starts.
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No data leaves the device
Audio, text, and video are processed locally, which matters for healthcare, legal, finance, and products handling regulated or confidential data.
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Works offline
Field tools, low-connectivity markets, and mobile-first apps can keep AI features working anywhere users are, with no internet required.
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No inference costs at scale
Cloud inference fees scale with usage, while on-device models run on hardware the user already owns.
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Independent of third-party uptime
An API outage does not have to take your product feature down with it.
From model selection to a working local AI feature in your app
We handle the full stack: picking the right model, integrating it with your codebase, and tuning performance on real devices.
On-device inference pipelines
We integrate quantized and distilled models into mobile apps using ExecuTorch, Core ML, and XNNPACK. We have shipped LLaMA 3.2, Qwen, Whisper, Kokoro, custom vision architectures, and adapt to whatever model your product requires.
React Native ExecuTorch integration
We built and maintain react-native-executorch, an open-source library that brings PyTorch Edge runtime to React Native with typed hooks for language models, speech recognition, text-to-speech, OCR, and image segmentation.
Quantization, distillation, fine-tuning
Smaller, faster models require compression work. We apply quantization, weight distillation, and task-specific fine-tuning to hit your latency and accuracy targets.
On-device RAG
We build retrieval-augmented generation that runs on the device: local embeddings, local vector search, and local generation using our react-native-rag library.
Private LLM apps
We build fully offline LLM chat interfaces using the same approach as our open-source Private Mind demo.
Agentic workflows on the edge
We build agent pipelines using LangChain and LangGraph, with local model execution and selective remote fallback where needed.
On-device AI in React Native is close to our daily work
Most teams integrating on-device AI are working with tools someone else built. We wrote react-native-executorch from scratch, benchmark on real hardware, and maintain the libraries that a significant part of the React Native ecosystem depends on.
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Authors of react-native-executorch
We built and open-sourced the library that brings Meta's production PyTorch Edge runtime to React Native: a direct path to on-device LLM, ASR, TTS, and computer vision without writing native code.
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Creators of react-native-rag
We created an on-device RAG library for local embeddings, vector search, and LLM augmentation with no cloud dependency.
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2.5M+ weekly npm downloads
Our libraries, including Reanimated, Gesture Handler, and Screens, run in production apps used by hundreds of millions of users.
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Open source is part of our job
About 30% of our engineering goes into open source. Our on-device AI libraries are maintained by the same engineers who build with them commercially.
Read how on-device AI changes the cost and latency tradeoffs
Our engineers write about the production tradeoffs behind edge AI, including where on-device execution wins and what it cannot do yet.
Some teams come with a prototype to improve. Others come before any model has been selected.
We work across all stages, from technical review to production implementation and hands-on pairing with your engineering team.
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Technical audit
We review your AI architecture, model selection, runtime configuration, and latency profile. You get a prioritized set of changes with the most impact on performance and cost.
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Full implementation
We design and build the on-device AI pipeline end to end, including model selection, optimization, runtime integration, and a working feature in your codebase.
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Expert pairing
We work alongside your engineering team, bringing our tooling, benchmarks, and knowledge of how ExecuTorch and mobile runtimes behave in production.
Running Small Language Models on Your Phone
Mateusz Kopcinski spoke at App.js Conf 2025 about why privacy, latency, and offline support change the calculus for mobile teams.
You do not need a fully defined local AI project to reach out
Share your current stack and what you want to make possible. We will tell you what is realistic for private, offline AI in your product.
Contact Let's connect
Tell us, or just come to chat. You do not need a fully defined project to reach out. Share your current stack and we will tell you what is realistic.