On-Device and Edge AI
Sometimes the right place for AI is the device itself: a factory camera with no reliable network, a phone app where nothing may leave the handset, a form that must work in a lift. We shrink models to fit and make them behave.
In short. On-Device and Edge AI is one of Matrida Infotech's AI services. Small language, speech and vision models running on phones, laptops and edge hardware, with no cloud round trip and no data leaving the device. Typical stack: llama.cpp · ONNX Runtime · TensorFlow Lite · Core ML · ExecuTorch · edge GPUs and NPUs.
Sound familiar?
- The network is slow, costly or missing where the work happens
- Data must not leave the device or the site
- Cloud calls make a feature too slow or too expensive at scale
- Users expect the feature to work offline
Capabilities
- Small language models
- On-device speech and vision
- Model compression
- Hybrid cloud fallback
- Offline operation
- Edge hardware deployment
What we build
- Models compressed by quantisation and distillation to fit the memory and power of the target device
- Speech, vision and language features packaged inside your app or on edge hardware
- A hybrid path: the device handles the common case and the cloud takes the hard ones, with a clear rule for when
- Updates over the air, with version control and rollback
- Tests on the real devices, not only on a development machine
What you get
- Compressed model and runtime packaged for your devices
- Device test report: speed, memory, battery
- Hybrid routing rules
- Update and rollback process
Tools and frameworks
- llama.cpp
- ONNX Runtime
- TensorFlow Lite
- Core ML
- ExecuTorch
- MediaPipe
Where it fits: Manufacturing, Logistics & Supply Chain.
Questions
How good can a model that runs on a phone be?
For a narrow task such as recognising a part, transcribing short speech or classifying a message, very good. For open-ended reasoning it is weaker than a large cloud model, which is why a hybrid design is common.
Does it work with no internet at all?
Yes, for the features built to run on the device. We list which features work offline and which need a connection.
Which devices can you target?
Phones and tablets, laptops, and edge boards with GPUs or NPUs. We confirm the hardware in the first week because it decides the model size.
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Learn more →Planning On-Device AI? Tell us the workflow.
Say what is going wrong today. We reply with how on-device and edge ai would handle it, a rough scope and the first thing to build.
- You would get: compressed model and runtime packaged for your devices
- You would get: device test report: speed, memory, battery
- You would get: hybrid routing rules