FactoryMind

On a busy production line, small faults often go unnoticed until they cascade into costly downtime. Engineers were relying on manual inspection schedules and tribal knowledge to catch problems — reactive rather than predictive.

The brief was a tool that could flag likely faults early, using existing sensor and machine data, without requiring a data science team on staff to run it.

We built a diagnosis pipeline that ingests machine telemetry, applies anomaly detection tuned per asset type, and surfaces plain-language fault explanations rather than raw model output — so an engineer on the floor can act on it immediately, not just a data analyst.

The interface was designed around the reality of a factory floor: high contrast, large touch targets, and a triage view that ranks issues by likely impact on production.

FactoryMind is currently in pilot deployment. Early results point to faults being flagged well before they would previously have been caught by manual inspection, giving maintenance teams a genuine head start.