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Computer Vision Services

Work with Computer Vision experts who teach machines to see.

Enable machines to interpret and understand visual data to automate tasks like image recognition and analysis.

Computer Vision services

Custom Computer Vision services

Our computer vision team has deployed production systems processing billions of images across manufacturing, healthcare, retail, and autonomous vehicles. They deliver accuracy that matches or exceeds ...

We deploy YOLO, Faster R-CNN, and custom architectures for applications from retail analytics to autonomous vehicle perception.

We build classification and segmentation models for medical imaging, satellite imagery, quality inspection, and content moderation.

We build real-time video processing pipelines that handle multiple camera feeds, tracking objects across frames with temporal consistency.

We combine OCR engines with custom ML models to achieve 99%+ accuracy on structured and semi-structured documents.

We build face analysis systems with liveness detection, age/emotion estimation, and anonymization capabilities that comply with privacy regulations.

We develop 3D perception systems for robotics, AR/VR, autonomous navigation, and spatial computing applications.

"Their computer vision system automated our quality inspection process, catching defects invisible to the human eye. Reject rates dropped by 80%."

Hans Mueller

Plant Manager, AutoParts GmbH

Tools & Technologies

We combine industry-standard frameworks with modern tooling and proven internal processes to accelerate delivery.

Detection

  • YOLOv8
  • Detectron2
  • MediaPipe
  • DETR

Frameworks

  • OpenCV
  • PyTorch Vision
  • TensorFlow
  • ONNX

Processing

  • NVIDIA DeepStream
  • GStreamer
  • FFmpeg
  • PIL

Deployment

  • TensorRT
  • Core ML
  • Edge TPU
  • OpenVINO

Frequently Asked Questions

Have more questions? Talk to an expert — we're happy to help.

Yes. With proper optimization, our models process 30-60 FPS on GPU hardware and 10-15 FPS on edge devices, suitable for real-time applications.

It varies by task complexity. Simple classification might need hundreds of images; complex detection may need thousands. We use data augmentation and transfer learning to reduce requirements.

Yes. We optimize models using TensorRT, quantization, and pruning for deployment on NVIDIA Jetson, Intel NCS, Google Coral, and mobile devices.

We implement on-device processing, face blurring, data anonymization, and privacy-by-design principles to comply with GDPR and other privacy regulations.

Team working together

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