VICE in practice — AI product development
We build AI that runs on the device, not in the cloud. From signal classification to adaptive control, our pipelines are designed for real-time inference under 12ms -- even in motion, even in dust, even when the network is gone.
- On-device inference
- Real-time classification
- Adaptive model updates
- Edge AI optimization
- Signal-to-action pipelines
- Low-latency rendering
- Model compression
- Field validation
- OTA update design
// AltMatter research FAQ
How we ship AI that doesn't break
01
Profile
Map latency, accuracy and power budget to the real hardware. The model must fit the chip, not the other way around.
02
Compress
Quantize, prune and distill until it fits the silicon budget -- without losing the signal that matters.
03
Deploy
On-device inference, no cloud fallback. If the network dies, the product still works.
04
Observe
Drift detection, accuracy monitoring and OTA update pathways. We close the loop after ship, not before.
Frequently asked questions
AltMatter runs phased research programs: signal characterization first, then iterative hardware-software co-design, prototyping in our Abuja lab, and transfer to precision manufacturing.
Yes. Confidential builds run in isolated program tracks in our Abuja lab, under NDA from the first conversation, with IP assigned under your ownership framework.
NDAs by default. We can operate under your IP ownership framework from day one, with program documentation and artifacts assigned to you.
Signal studies run in weeks; full programs from bench prototype to manufacturing transfer typically span months. Scope sets the timeline — every program starts with a bounded signal study.
From first signal to shipped product, we move as one integrated team -- hardware, firmware, and AI in lockstep. No fragmentation, no handoffs that lose fidelity.
From first signal to shipped product, we move as one integrated team -- hardware, firmware, and AI in lockstep. No fragmentation, no handoffs that lose fidelity.
