Production-ready Computer Vision Solutions That Stop Defects at Scale

Choose a pre-trained platform (YOLO, AWS Rekognition, Roboflow) for standard defects, OCR, or object counting; faster to deploy, lower cost. Go custom when your materials or defect types aren't well covered by public training data; general models fine-tuned on niche domains rarely match purpose-built ones. Key question: how far does your real-world data stray from public datasets?

Choose edge when you need low latency (<100ms), have poor connectivity, or can't send data offsite. Choose cloud for batch processing, non-real-time analytics, or centralized model updates. Most production systems use both; edge for real-time detection, cloud for monitoring and retraining.

ROI spans four areas: fewer defect escapes, reduced inspection labour, faster line throughput, and lower compliance risk. Most deployments break even in 6-18 months. Common mistake: counting only labour savings; undetected defects that now get caught are usually the bigger ROI driver.

Standard surface defects with clean training data: 95-99% is achievable. Complex or variable environments: 90-95% with human escalation is more realistic. Be skeptical of any vendor claiming 99%+ without specifying conditions and failure modes; accuracy without context is a marketing number, not an operational guarantee.

Every system includes accuracy monitoring, confidence tracking, drift detection, and a retraining pipeline with clear trigger criteria. When accuracy drops, the system alerts, logs evidence, and kicks off retraining. Rollback procedures are tested before go-live, not built after something breaks.

Feasibility and scoping: 2-4 weeks. Focused pilot (dataset, training, validation, controlled production test): 4-8 weeks. Full production integration: 4-12 weeks. In total, most companies we work with have computer vision solutions running as a live, monitored production system within 90-120 days of the first conversation.