The difficult problem
Edge teams had to trade accuracy, model size and latency across disconnected optimization toolchains.
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Model compression and edge deployment
EdgeOpt
A reproducible optimization pipeline that compresses heavy models and benchmarks them against real target hardware.
EdgeInfrastructureResearch
Built around the hard part
The constraint shaped the intelligence.
The intelligence built
Quantization, pruning, distillation, ONNX conversion and runtime compilation are evaluated as one governed workflow.
How the system works.
The model is one layer. The value comes from connecting inputs, intelligence and production action as one accountable system.
Compression workflow
Acquire and structure the operating signal.
Runtime optimization
Transform it through the model, rules and control layer.
Benchmark and export
Deliver a decision, artifact or action into production.
Where it runs
Exportable artifacts for TensorRT, OpenVINO and ONNX Runtime targets.
What it changes
Teams can select an edge artifact with measured rather than assumed trade-offs.
Production stack
PyTorchONNXTensorRTOpenVINOCUDADocker
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