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
Case Studies
Real results from real projects. See how we've delivered transformative computer vision solutions.
Automated Quality Inspection Reducing Defect Rate by 80%
Deployed real-time visual inspection on production lines processing 500 units per minute.
Built a Retail Analytics Platform Tracking 10M+ Shoppers
Implemented privacy-preserving foot traffic analysis, heatmaps, and conversion analytics.
Developed Medical Image Analysis for Pathology Labs
Built AI-assisted diagnosis tool analyzing tissue samples with 97% accuracy, reducing analysis time by 60%.
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.
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