AI at the edge, where it matters.
ML models optimized for edge hardware. Quantization, pruning, and deployment to devices where cloud latency is not an option and connectivity can't be guaranteed.
On-device inference latency
Model accuracy after quantization
Model size reduction
Cloud dependency for inference
What we build
Edge capabilities for constrained environments.
Model optimization
Quantization (INT8, INT4), pruning, knowledge distillation, architecture search. We squeeze maximum performance from minimum compute without destroying accuracy.
On-device inference
Models running on NVIDIA Jetson, Raspberry Pi, mobile phones, industrial PLCs. We optimize for your specific hardware, not generic benchmarks.
ONNX & TensorRT
Model export and optimization for every runtime. ONNX for portability, TensorRT for NVIDIA GPUs, Core ML for Apple, TFLite for Android.
Offline-first architecture
Inference without internet. Edge-cloud sync when connected, local-first when not. Your AI works in the field, on the factory floor, and in the air.
Power-efficient inference
Models designed for battery-powered devices. Adaptive compute that scales complexity based on available power and thermal constraints.
Secure edge deployment
Model encryption, secure boot, tamper detection. Your IP stays protected even on devices you don't physically control.
Sound familiar?
Edge AI problems we solve regularly.
“Our model runs great on a GPU server but is too slow on edge hardware.”
We apply quantization, pruning, and architecture-specific optimizations. Same model, 8x smaller, 5x faster — running on a $50 device.
“Our IoT devices lose connectivity. Cloud-dependent AI is useless.”
We deploy inference directly on-device with edge-cloud sync. When connectivity drops, AI keeps working. When it returns, models update.
“We need computer vision on 500 cameras but can't afford GPU servers for each one.”
We optimize models for NVIDIA Jetson or similar edge devices. One $200 edge box per camera cluster instead of $10K GPU servers.
Tech stack
Tools we use in production.
Ready to build
Let's bring AI to the edge.
45 minutes with our edge AI engineers. We'll evaluate your hardware constraints, assess model optimization potential, and design a deployment strategy that works offline.
AI projects we delivered
