Select. Benchmark. Deploy.
for any model, on any device, in any framework

We eliminate the need for costly GPU cloud servers by transforming your existing AI models into NPU-optimized, on-device runtimes in hours, not weeks, across any mobile device, any OS.

AI services shouldn't be tied to the cloud.

Melange is our flagship end-to-end on-device AI deployment platform. We help mobile developers run AI models locally, from flagship smartphones to budget devices, making AI Faster, Cheaper, Safer, and Independent.

We provide:

• Automated Model Conversion: PyTorch, ONNX, or TFLite → device-specific NPU libraries.
• Peak Performance: Up to 60× faster than mobile CPU inference, with massive energy savings.
• Cross-Platform SDKs: Swift, Kotlin, Flutter, React Native for any app stack.
• Benchmarking: Test your models across 200+ global devices with real-world hardware metrics.
• Full Privacy by Design: All inference happens locally; no data leaves the device.

While other frameworks focus on model quantization or partial device deployment, we handle the entire lifecycle:

1. Analyze model architecture and runtime requirements.
2. Convert & Optimize for heterogeneous NPUs (Qualcomm, MediaTek, Apple, etc.).
3. Benchmark on real devices for latency, accuracy, and memory.
4. Deliver drop-in SDKs ready for mobile integration.
5. Support continuous updates at scale.

No guesswork. No vendor lock-in. Just working on-device AI in hours, not weeks.

Note: Lower is better. Full dataset available on the Melange Dashboard.

• Deploying NPU-accelerated models takes just a few lines of code with the Melange SDK.

• iOS Integration (Swift)

// (1) Load Melange model
let model = try ZeticMLangeModel(tokenKey: "MLANGE_PERSONAL_KEY", "MODEL_REPO_NAME")

// (2) Prepare model inputs
let inputs: [Tensor] = [] // Prepare your inputs

// (3) Run and get output tensors of the model
let outputs = try model.run(inputs)

• Android Integration (Kotlin, Java)

// (1) Load Melange model
val model = ZeticMLangeModel(context, "MLANGE_PERSONAL_KEY", "MODEL_REPO_NAME")

// (2) Prepare model inputs
val inputs: Array<Tensor> = // Prepare your inputs

// (3) Run and get output tensors of the model
val outputs = model.run(inputs)

Don't start from scratch. We have created a repository of production-ready, open-source, on-device AI apps that you can clone, run, and modify in minutes.

• Website: zetic.ai
• Melange Dashboard: mlange.zetic.ai — Get NPU-optimized SDKs, view benchmarks, and upload custom models.
• Documentation: docs.zetic.ai — Full API reference and implementation guides.
• Discord: Join our Community — Get support, share your projects, and meet other developers.

See Melange performance in action on your own device: ZeticApp: Android | iOS