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On-Device AI for
Everything

for any model, on any device, in any framework

Try for free

Built by AI Engineers & Researchers from

Streamlined Workflow

3 Simple Steps to Deploy On-Device

Cut deployment time from months to hours with automated, hardware-aware optimization

Select or Upload
your own model

Bring your own model by uploading the raw model files or sharing the Hugging Face link. Or you may select a model from our own model library.

Benchmark
across 100+ mobile devices

Compare model performance metrics including Latency, SNR, Memory and TPS across 100+ real mobile devices to find the best deployment setting for each device.

Deploy
by copying our SDK code block

Copy & Paste our Melange SDK code block into your IDE environment to deploy the AI model in your project.

The ZETIC Advantage

Why Melange?

Replace months of manual CPU tuning with an automated, NPU-accelerated pipeline that deploys in hours

Feature

Others

Optimization Workflow

Manual

Processor Chip Compatibility

Limited (CPU focus)

Deployment Complexity

Complex Manual Coding

Deployment Duration

12+ months

Device Testing

Unavailable to verify on global devices

Import Model from

Model Library only

Melange

Fully automated pipeline

CPU + GPU + NPU hybrid acceleration

Simple 3-line code deployment

Ready under 6 hours

Tested on 100+ devices

Model Library + Own Model + HuggingFace Models

Use Cases
Made with Melange

View what our builders have come up with using Melange

See All

Scrubs

Healthcare

Physiopal

Healthcare

Cropt

Agriculture

KNOVA

Education

Testimonials
Voices of our users

From individual developers to enterprise customers

“Melange was incredibly important to our project's success. Without it, we wouldn't have been able to demonstrate our model's feasibility in real-world situations running on local hardware. At the same time, Melange was very intuitive to use and deploy, which made the entire process a lot more straightforward than we anticipated. Finally, Melange did an excellent job at optimizing our model, and it achieved comparable performance even to our full model hosted on an external server–something we genuinely didn't expect.”
William Wang
Stanford Student, LA Hacks Participant
“Using ZETIC Melange, I built SumiSense, an on-device behavioral health assistant focused on privacy-preserving self-reflection and relapse-risk awareness. For me, the most important part was simple: sensitive behavioral health data should not have to leave the user’s phone for the product to be useful. Melange made that possible by helping me run multiple models locally in one mobile workflow. It let me spend more time thinking about the actual clinical and privacy problem, and less time fighting deployment issues. For health AI, especially behavioral health, that matters a lot.”
Abhishek Dhadwal
ASU Grad Student, Melange Hackathon Participant
“ZETIC Melange made it surprisingly straightforward to ship real on-device AI in a healthcare setting where privacy and offline reliability are non-negotiable. We were able to run multiple local models across different Android NPUs without maintaining separate hardware-specific inference pipelines, which dramatically accelerated development. Melange enabled a patient companion experience where encounter summaries, insurance-document understanding, and medical Q&A all happen fully on-device, keeping sensitive health data off the cloud entirely.”
Akash Ingle
USC Grad Student, LA Hacks Participant

FAQ

Can't find an answer to your question here? Contact us using the link below.

Do I need to retrain my model to use Melange?

No. We support TorchScript, TensorFlow and ONNX models directly. Our platform automatically handles conversion and quantization for on-device execution without needing your training data or altering weights

Why use Melange instead of free open-source tools like TFLite or CoreML?

How much cost savings can be achieved by using Melange?

Is on-device AI actually faster than a powerful cloud GPU server?

What happens if a user’s phone is old and doesn't have an NPU?

How difficult is the integration into my existing mobile app?

On-Device AI for Everything