We leverage TensorFlow, PyTorch, and Keras for developing CNNs (Convolutional Neural Networks), R-CNNs, and GANs enabling high-accuracy image classification, object detection, and segmentation.

Using tools like OpenCV, scikit-image, and ImageAI, we enhance image quality, remove noise, and extract key visual features from raw data.

We employ YOLO, SSD, and Faster R-CNN architectures to detect and track objects in real-time, supporting use cases in security, logistics, and autonomous navigation.

Our systems integrate Dlib, FaceNet, and DeepFace for identity verification, attendance systems, and secure access control.

We utilize FFmpeg, OpenCV Video Modules, and AI-based motion analytics to analyze video streams for behavior recognition, surveillance, and event detection.

We work with LiDAR, stereo vision, and depth cameras to enable 3D object reconstruction and environment mapping critical for robotics and AR/VR systems.

Using NVIDIA Jetson, OpenVINO, and TensorRT, we deploy optimized models on edge devices for faster inference and reduced latency in real-time scenarios.

We employ LabelImg, CVAT, and Supervisely for precise dataset labeling and augmentation ensuring robust model performance.