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# 🛰️ ZETIC.ai — On-Device AI for Every Device
**Build. Deploy. Run. Anywhere.**
ZETIC.ai helps AI engineers deploy models on *any* mobile device — without cloud GPU servers.
We transform your existing AI models into **NPU-optimized, on-device runtimes** in **under 6 hours** including from global device benchmark to runtime source code generation.
---
## 🚀 What We Do
**ZETIC.MLange** — our core platform — enables **serverless AI** by:
- **Automated Conversion**: Convert your PyTorch, ONNX, or TFLite model into a device-specific NPU library.
- **Peak Performance**: Up to **60× faster** than GPU cloud inference, with zero accuracy loss.
- **Broad Compatibility**: Supports Android, iOS, Linux; MediaTek, Qualcomm, Apple NPUs — more coming soon.
- **End-to-End SDK**: From model optimization to app integration — no extra engineering required.
---
## 🛠 Key Features
- **Zero GPU Costs** — Replace expensive GPU cloud servers with *free* NPU power in devices.
- **Full Privacy & Security** — Data never leaves the device.
- **Ultra-Low Latency** — Real-time AI experiences, even offline.
- **Cross-Platform** — One model → All devices → Same performance.
---
## 📦 Example Use Cases
- 🎙 **Speech Recognition (Whisper)** — Real-time, offline transcription on mobile.
- 🦷 **Dental AI Diagnostics** — Instant tooth condition analysis via smartphone camera.
- 🏌️ **Sports AI** — On-device golf swing analytics.
- 🤖 **On-Device LLMs** — Chat & reasoning models running entirely offline.
---
## 📊 Benchmarks
| Device | Task | Cloud GPU | On-Device NPU | Speedup |
|--------|------|-----------|---------------|---------|
| iPhone 16 Pro | Whisper-Small | 1.2s | 0.07s | **×17** |
| Galaxy S24 Ultra | LLaMA-3-8B | 2.4s/token | 0.09s/token | **×26** |
[🔗 See more benchmarks »](https://mlange.zetic.ai)
### YOLOv8n — NPU Latency (ms)
| Device | Manufacturer | CPU | GPU | CPU/GPU | NPU |
|--------|--------------|-----|-----|---------|-----|
| Apple iPhone 16 | Apple | 126.27 | - | 8.98 | **2.03** |
| Apple iPhone 16 Pro | Apple | 122.23 | - | 7.54 | **1.69** |
| Samsung Galaxy S24+ | Qualcomm | 69.79 | 24.38 | 618.05 | **3.85** |
| Samsung Galaxy Tab S9 | Qualcomm | 107.78 | 30.39 | 344.42 | **5.21** |
| Samsung Galaxy S22 Ultra 5G | Qualcomm | 103.40 | 39.73 | 100.34 | **7.41** |
---
### Whisper-tiny-encoder — NPU Latency (ms)
| Device | Manufacturer | CPU | GPU | CPU/GPU | NPU |
|--------|--------------|-----|-----|---------|-----|
| Apple iPhone 16 | Apple | 552.13 | - | 44.49 | **19.01** |
| Apple iPhone 15 Pro | Apple | 527.78 | - | 43.13 | **19.40** |
} Samsung Galaxy S23 | Qualcomm | 290.62ms | 169.82ms | 2,795.18ms | **86.88** |
| Samsung Galaxy S24+ | Qualcomm | 278.78 | 133.48 | 2619.56 | **106.44** |
| Samsung Galaxy S23 Ultra | Qualcomm | 308.82 | 170.08 | 2688.97 | **68.34** |
- **You can get runtime source code and benchmark report of your model with [ZETIC.MLange](https://mlange.zetic.ai)**
---
## 👨🏻💻 Plug-and-play To Your App
- The runtime SDK is also provided for your AI model with ZETIC.MLange
- **iOS Integration** (Swift)
``` swift
// import
import ZeticMLange
// ...
// (1) Load Zetic MLange model
let model = try ZeticMLangeModel("MLANGE_PROJECT_API_KEY")
// (2) Run model after preparing model inputs
let inputs: [Data] = [] // Prepare your inputs
try model.run(inputs)
// (3) Get output data array
let outputs = model.getOutputDataArray()
```
- **Android Integration** (Kotlin, Java)
``` kotlin
// import
import com.zeticai.mlange.core.model.Target
import com.zeticai.mlange.core.model.ZeticMLangeModel
// ...
// (1) Load Zetic MLange model
val model = ZeticMLangeModel(this, "MLANGE_PROJECT_API_KEY")
// (2) Run model after preparing model inputs
val inputs: Array<ByteBuffer> = // Prepare your inputs
model.run(inputs)
// (3) Get output buffers of the model
val outputs = model.outputBuffers
```
## 📥 Try It Now
- **MLange Dashboard**: [https://mlange.zetic.ai](https://mlange.zetic.ai)
- **Demo Apps**: [App Store](https://apps.apple.com/app/zeticapp/id6739862746) / [Google Play](https://play.google.com/store/apps/details?id=com.zeticai.zeticapp)
---
## 🧭 Supported Targets
- **OS**: Android, iOS, Linux
- **NPUs**: MediaTek, Qualcomm, Apple (more coming)
- **Frameworks In**: PyTorch, ONNX, TFLite
- **Artifacts Out**: NPU-optimized runtime libraries + SDK bindings (Kotlin, Java, Swift, Flutter, React Native)
## 📬 Contact Us
- **Website**: [https://zetic.ai](https://zetic.ai)
- **Email**: [email protected]
- **LinkedIn**: [linkedin.com/company/zetic-ai](https://linkedin.com/company/zetic-ai)
---
**ZETIC.ai** — AI for All, Anytime, Anywhere.
Run your AI where it matters: **on the device.**
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