Our Work
Real projects. Real constraints. Real solutions for edge computing and low-latency AI deployment.
Real-Time IoT Inference Pipeline
A local manufacturing facility needed to process sensor data with sub-100ms latency. We designed a hybrid architecture that runs feature extraction on-device while deferring complex analysis to edge servers, eliminating cloud roundtrips and reducing decision time by 85%.
Model Compression for Mobile Vision
A startup had a 450MB object detection model that wouldn't fit on target mobile devices. Through quantization, pruning, and knowledge distillation, we reduced the model to 32MB while maintaining 94% of the original accuracy—enabling real-time processing on mid-range phones.
Multi-Runtime Inference Platform
Built an abstraction layer allowing teams to deploy the same trained model across TensorFlow Lite, ONNX Runtime, and CoreML without retraining. Automated benchmarking and A/B testing helped identify optimal runtime choices per hardware configuration.
Hardware Selection Framework
A team building autonomous inspection robots faced dozens of hardware options with conflicting tradeoffs. We created a decision framework balancing inference latency, power draw, thermal constraints, and cost—helping them select and validate hardware before prototype build.
Ready to solve your edge AI challenge?
Whether you're optimizing a model, designing an architecture, or selecting hardware—let's talk through your constraints and find the right approach.
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