Major Aerospace OEM

The Challenge

A major aerospace OEM needed machine learning systems that could run on-device in environments where cloud connectivity isn’t guaranteed and reliability is non-negotiable. This required a fundamentally different approach than typical ML engineering — models that are small, fast, and bulletproof.

What We Built

Our team designed and built several production ML systems:

• On-device machine learning models optimized for edge deployment
• Computer vision systems for manufacturing and inspection workflows
• Recommendation engines for maintenance and operational decision-making
• Edge infrastructure that meets aerospace-grade reliability requirements

The Result

• 3 production ML systems deployed to edge devices in manufacturing environments
• Models running on-device with no cloud dependency — zero-downtime inference
• Sub-second inference latency on constrained hardware
• Production systems operating where failure isn’t an option and cloud connectivity can’t be assumed — the kind of work that separates real ML engineering from demo-day prototypes.