Quality teams that want defect detection within 8-12 weeks
8-12 weeks
Defect detection implementation window
60%+
Reduction in inspection labor
95%+
Defect detection accuracy
3 Months
Payback period thus resulting in immediate ROI
What We Do
Custom model training on your defect images, dataset preparation and annotation, edge deployment, integration with existing quality systems, and team training.
Klyff takes your proprietary defect images - surface scratches, solder joint failures, paint imperfections, packaging damage - and fine-tunes pre-trained vision models (YOLO, ResNet, EfficientNet) on your specific defect classes, achieving 95%+ accuracy in 2-4 weeks.
Transfer learning from public datasets accelerates training and reduces the need for massive labelled datasets; your domain-specific images dramatically improve precision over generic models.
The result is a production-ready model tailored to your exact manufacturing process, defect types, and image conditions.
Our Engagement
A clear path from kickoff to production operation, shaped around the service outcome.
Selected Customer Success Stories
Explore how teams are using Klyff to improve quality, safety, and operational performance in the field.
Case Study
Learn how Klyff deployed an on-device continual learning loop on ARM Cortex-M7 gateways, achieving a 40% reduction in false positives and automatically adapting to machine aging without cloud retraining.
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Case Study
Discover how Klyff deployed quantized CNNs on Hailo-8 edge accelerators at the glass level, dropping inspection latency from 120ms to 8ms and saving an estimated $12,000/month per line in rework costs.
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Case Study
See how Klyff deployed an ATEX-certified, privacy-by-design safety agent that localizes and scrubs human faces at the source, reducing safety incidents by 34% with zero PII leaving the edge.
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Insights
Connect your first site, deploy your first model, and see measurable ROI in weeks, not months.
