Learning across multiple sites within 12-16 weeks
12-16 weeks
Learning across multiple sites
Zero
Data movement costs
50%+
Reduction in data governance complexity
40%+
Faster model improvement vs. single-site
What We Do
Klyff federated learning enables each site to train locally on its own data, then aggregate models without sharing raw data.
Klyff designs a distributed system where each manufacturing site runs a local inference and training environment on its own edge hardware or on-prem servers, connected to a secure central orchestrator that coordinates model updates without centralizing raw data.
The architecture accounts for network reliability, latency, and bandwidth constraints typical in industrial settings, utilizing an asynchronous model aggregation approach so that slow or offline sites do not impede the fleet's ability to learn.
This design enables scalability across dozens or hundreds of plants without building a costly centralized data lake.
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.
