Architected
For Mobility
Full-spectrum neural network optimization from preliminary architecture audit to high-performance edge deployment. We translate complex model weights into efficient mobile-native execution layers.
Optimization
Tiers
Our methodology prioritizes hardware-level efficiency. Select the tier that matches your integration lifecycle.
Model Audit
For teams with existing weights needing a bottleneck analysis before production commit.
- Layer-wise profiling of latency bottlenecks.
- Inference cost estimation for target hardware.
- Quantization feasibility study.
Deliverable: PDF Report + Profile Logs
Submit ArchitectureNPU Quantization
Precision-aware weight reduction for high-performance silicon including ARM v9, Apple A-series, and Snapdragon chipsets.
- Layer-wise manual tuning
- Custom kernel generation
- ONNX/TFLite export
Custom Ops
Direct bridge between R&D models and production hardware drivers.
Optimal JNI/Objective-C bindings.
Reducing DRAM pressure.
Who We
Solve For
High-Latency Models
Teams struggling with networks that take >150ms per inference on modern mobile silicon.
Battery-Sensitive Apps
Solutions requiring sustainable performance without thermal throttling during sustained usage.
Who We
Are Not
Basic Model Training
We do not provide data labeling or original architecture research. We optimize existing weights.
Cloud Inferencing
Our focus is 100% on-device. If your strategy relies on REST API latency, our tools are not the match.
WE STRIP AWAY THE ABSTRACTION
Ready to audit
your network?
Our technical team performs a feasibility probe against target mobile hardware constraints. Prepare your model export (ONNX or TFLite) for review.
IP protection through NDA-first model handling and secure weight analysis.
Updated service availability for ARM v9 chipsets and latest NPU architectures.