🇸🇬 DESIGNED IN SINGAPORE
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AI & EDGE COMPUTING DESIGN

Models that run on the device,
not on a server bill.

We pick the silicon, train and shrink the model, and build the runtime around it — so recognition, grading and navigation happen on your product, in real time, with no cloud dependency and no per-inference cost.

What we do

From a dataset to a model that fits the chip.

A model that scores well on a workstation is not a product. It has to fit the memory, the power budget and the latency window of the device it ships on — that gap is the work.

What you get

A model you can actually ship.

Deliverables your team can build on, measure and update — not a notebook and a promise.

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Quantised model

Optimised weights in the format your target runtime loads.

Inference SDK

The runtime pipeline and API, integrated on the target board.

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Benchmark report

Accuracy, latency and power measured on the real hardware.

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Integration manual

How to build, deploy and update the model in production.

How we work

Prove it early, optimise it late.

Feasibility is settled on real data before hardware is committed — so nobody discovers at DVT that the chip is too small.

1
Feasibility

Target accuracy and latency proven on sample data.

2
Data

Collection and labelling of your domain dataset.

3
Train

Model trained and validated against held-out data.

4
Optimise

Quantised and tuned to the device budget.

5
Deploy

Runtime integrated, benchmarked and handed over.

One complete product

The model is one of three — you need all of them.

Intelligence decides what to do, but a finished product also needs the board it runs on and the firmware that drives it. We own all three end-to-end, so nothing is lost between vendors.

Leave out any one and you don’t have a product yet — all three are essential, and one senior team delivers them together.

Need AI that runs on the device?

Tell us what has to be recognised, how fast, and on what power budget. We will propose the platform, the model approach and a feasibility plan.