Low‑latency On‑Device AI
Deploy lightweight, high‑performance AI models on smartphones, IoT devices, and edge systems with millisecond latency.
Techniques & Tooling
Optimized models and pipelines for the edge
Real‑time Pose & Tracking
MediaPipe Pose, MoveNet, OpenPose
- • Accurate human motion tracking at 30–60 FPS
- • Fitness, safety monitoring, AR try‑ons
Model Optimization
TFLite, Core ML, Quantization, Pruning
- • INT8/FP16 quantization for low power devices
- • On‑device acceleration via NNAPI/Metal
Ready to ship edge AI?
We design, optimize, and deploy on‑device models for real‑time performance.
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Frequently Asked Questions
Common questions about on-device and edge AI deployment.
What are the advantages of running AI on-device instead of in the cloud?
On-device inference removes network round-trips, so you get millisecond latency, and it keeps data on the device for better privacy and offline availability. It also lowers ongoing cloud inference costs. We help you decide which parts of a workload belong on-device versus in the cloud.
Which frameworks and runtimes do you use for mobile and edge deployment?
We deploy with TensorFlow Lite, Core ML, ONNX Runtime, and PyTorch Mobile, and accelerate inference through platform APIs like Android NNAPI, Apple Metal, and vendor NPUs/GPUs. The right stack depends on your target devices and models.
How do you shrink models to run on constrained devices without losing accuracy?
We apply quantization (INT8/FP16), pruning, and knowledge distillation, then benchmark accuracy against the original model. The goal is the smallest, fastest model that still meets your accuracy and latency targets.
Which devices and platforms can you target?
Smartphones (iOS and Android), embedded and IoT boards such as Raspberry Pi and NVIDIA Jetson, wearables, and custom edge hardware. We tailor the model and runtime to each device's compute, memory, and power budget.