Deploy production computer vision at line speed — defect detection, medical diagnostics, video analytics, and edge AI that processes the visual world with superhuman precision.
99.2%
Defect Detection Accuracy
<50ms
Edge Inference Latency
80%
Reduction in Manual Inspection
High-speed defect detection on production lines — scratches, dents, misalignments, color deviations — at line speed with zero false negatives.
99.2% detection accuracyFDA-compliant diagnostic AI for radiology (CT, MRI, X-ray), pathology, ophthalmology, and dermatology — specialist-level accuracy.
94% diagnostic accuracyReal-time crowd density, anomaly detection, object tracking, and behavioral analysis for smart cities and enterprise security.
Real-time at 60fpsPlanogram compliance monitoring, shelf analytics, checkout-free retail, customer behavior analysis, and footfall heatmaps.
45% faster shelf auditsPerception systems for autonomous vehicles — 3D object detection, lane detection, depth estimation, and sensor fusion with LiDAR.
<10ms inferenceIntelligent document processing — form extraction, table parsing, handwriting recognition, and document classification at scale.
98.5% extraction accuracyCrop disease detection, yield estimation, drone-based field scouting, and precision agriculture via satellite and UAV imagery.
30% yield improvementPPE compliance detection, unsafe behavior recognition, progress monitoring from site cameras, and equipment tracking.
70% safety incident reductionDetection Models
Foundation Models
Frameworks
Edge Deployment
Edge Hardware
Annotation Tools
For industrial defect detection with sufficient training data, we regularly achieve 98–99.5% accuracy. Medical imaging models reach specialist-level performance on well-defined diagnostic tasks. Accuracy depends heavily on data quality, annotation quality, and the variability of your specific use case. We always benchmark against human-level baselines.
Yes. We specialize in edge AI deployment using NVIDIA Jetson, Intel Movidius, and custom FPGA solutions. Our edge-optimized models use TensorRT and ONNX quantization to achieve <50ms inference latency on edge hardware without cloud dependency.
It varies by task. For defect detection, 500–2,000 labeled images per defect class is often sufficient with transfer learning. For medical imaging, we work with domain specialists to define minimum datasets. We also offer synthetic data generation and data augmentation services to reduce labeling costs.
Request a free proof-of-concept on your own image dataset — our engineers will run it and share the results within 5 business days.
Request Free Vision AI PoC