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Models that run on-device or at the edge for privacy, offline use, and predictable latency. We optimize graphs, quantize weights, and validate on real hardware so production matches the lab.
Comprehensive solutions tailored to your business requirements
Pruning, INT8/FP16 quantization, and knowledge distillation to fit models within on-device memory and compute budgets.
Deploy optimized models on mobile (Core ML, NNAPI), embedded Linux, and browsers (WASM/WebGPU) with consistent APIs.
Privacy-preserving training across distributed devices with differential privacy budgets and secure aggregation protocols.
Battery, thermal, and latency profiling on target hardware to ensure models meet real-world performance requirements.
Zero-latency inference without network round-trips
Complete data privacy—user data never leaves the device
Offline functionality in connectivity-constrained environments
Lower cloud infrastructure costs by shifting compute to the edge
Predictable performance independent of network conditions
Reduced regulatory exposure with on-device data processing
Better user experience with instant, responsive AI features
It varies by task. Typically INT8 quantization loses less than 1% accuracy for classification tasks. We run systematic evaluations on your data to quantify the tradeoff and only ship models that meet your quality bar.
Yes. We implement secure over-the-air model update pipelines with versioning, rollback capability, and A/B testing so you can improve models continuously without requiring app updates.
We support iOS (Core ML/ANE), Android (NNAPI/GPU delegate), embedded Linux (TensorRT, ONNX Runtime), and browsers (WASM/WebGPU). We profile on your target devices to ensure real-world performance.
We combine deep technical expertise with a product-first mindset to deliver solutions that work in the real world.
Seasoned engineers across blockchain, AI & web
200+ projects delivered globally
From discovery to production & beyond
