Teach machines to see.

For controlled environments -- industrial inspection on a fixed camera, document extraction from standardized forms -- we regularly achieve 94-99% accuracy. For unconstrained real-world video (pedestrian detection, retail shelf monitoring) expect 85-93% mAP. We benchmark on a held-out test set that mirrors your production distribution and report precision, recall, and F1 separately.

For fine-tuning a pretrained model like YOLOv8 or Florence-2, 500-2,000 annotated examples per class is often enough for a working baseline. Complex classes with high visual variability may need 5,000-10,000 examples. We use data augmentation via Albumentations to extend smaller datasets and synthetic data generation when real examples are scarce.

Edge deployment (Jetson, NVIDIA hardware) makes sense when you need sub-100ms latency without network dependency. Cloud inference is simpler to manage but adds 50-200ms of network latency. Many clients use a hybrid: a lightweight ONNX model on-device for real-time decisions, with heavier analysis offloaded to cloud batch processing.

Yes. We optimize models with TensorRT or ONNX Runtime, which typically cuts inference time by 2-4x. A YOLOv8n model on a Jetson Orin runs at 60+ FPS; the same model on an A10G GPU handles 8-10 camera streams simultaneously. Throughput requirements are part of the architecture conversation.

A focused detection or classification project with annotated data in hand can reach a working prototype in 3-4 weeks. End-to-end delivery including annotation pipeline setup, model training, edge deployment, and monitoring typically runs 8-14 weeks. The biggest schedule variable is annotation turnaround time.