🛰️ 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

🔗 See more benchmarks »

YOLOv8n — NPU Latency (ms)

Whisper-tiny-encoder — NPU Latency (ms)

• You can get runtime source code and benchmark report of your model with ZETIC.MLange

👨🏻‍💻 Plug-and-play To Your App

• The runtime SDK is also provided for your AI model with ZETIC.MLange

• iOS Integration (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)

// 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
• Demo Apps: App Store / Google Play

🧭 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
• Email: [email protected]
• LinkedIn: linkedin.com/company/zetic-ai

ZETIC.ai — AI for All, Anytime, Anywhere.
Run your AI where it matters: on the device.