Edge AI Deployments

AI where the data lives

Some tasks can't wait for a cloud round-trip. We deploy AI models directly to devices, factories, and local sites where latency, privacy, or bandwidth are critical.

• Model compression: We optimize models for specific hardware—GPUs, NPUs, and CPUs—using techniques like quantization and pruning.
• Runtime engineering: We use ONNX, TensorRT, Core ML, and llama.cpp to ensure models run within strict memory and power limits.
• Fleet management: We handle remote updates, device health monitoring, and rollback across large groups of devices.
• Hybrid systems: We build setups that run locally but can use the cloud as a fallback or for data processing.

Performance targets

Edge AI starts with a hardware budget. We design for your specific devices and measure every watt and millisecond.

• Latency: We aim for single-digit millisecond response times on standard edge hardware.
• Efficiency: We size models to fit your device's memory while maintaining high accuracy.
• Reliability: All deployments include full monitoring and verified updates from day one.