About the service
I create artificial intelligence that runs right on the device, not in the cloud. My specialization is deploying computer vision and machine learning models on embedded hardware: NVIDIA Jetson, Google Coral and Raspberry Pi. This way your product responds with minimal latency, fully preserves data privacy and does not depend on network availability. When a model lives on the device itself, frames never leave the camera, response time is measured in milliseconds and cloud and traffic costs fall nearly to zero.
I run the entire pipeline. I take a trained model and optimize it for a specific board: TensorRT, INT8 or FP16 quantization, pruning and layer fusion — so the model fits in limited memory and reaches real framerate. I assemble camera capture, preprocessing, inference and post-processing into a stable pipeline, tune it to the board's thermal and power budget and achieve stability through reboots, power loss and days of autonomous operation without an operator. Offline work is the baseline: the box does its job on a shelf, in transport or on a production line with no internet at all.
Typical tasks I close: people and object counting, defect detection on conveyors, license plate and barcode recognition, presence and safety monitoring, gesture and pose recognition — all at measurable FPS and accuracy on your real hardware, not on paper. Name the board, camera and task — and before we start I will honestly tell you what framerate and accuracy are really achievable.
What you get:
— model conversion and TensorRT / quantization setup for your board;
— full inference pipeline from camera to results with logging;
— benchmarks (FPS, latency, accuracy) on your device;
— offline operation, auto-restart and deployment scripts.
I run the entire pipeline. I take a trained model and optimize it for a specific board: TensorRT, INT8 or FP16 quantization, pruning and layer fusion — so the model fits in limited memory and reaches real framerate. I assemble camera capture, preprocessing, inference and post-processing into a stable pipeline, tune it to the board's thermal and power budget and achieve stability through reboots, power loss and days of autonomous operation without an operator. Offline work is the baseline: the box does its job on a shelf, in transport or on a production line with no internet at all.
Typical tasks I close: people and object counting, defect detection on conveyors, license plate and barcode recognition, presence and safety monitoring, gesture and pose recognition — all at measurable FPS and accuracy on your real hardware, not on paper. Name the board, camera and task — and before we start I will honestly tell you what framerate and accuracy are really achievable.
What you get:
— model conversion and TensorRT / quantization setup for your board;
— full inference pipeline from camera to results with logging;
— benchmarks (FPS, latency, accuracy) on your device;
— offline operation, auto-restart and deployment scripts.
