Equipment failures caught before they happen.
Downtime, engineered out.

Failures only became visible after the equipment had already stopped.

Equipment failures were being caught after the fact, not before it. Error logs and technical documentation existed, but nothing was reading them together to work out when a machine was heading for a breakdown.

Maintenance was reactive by default. Engineers responded to failures as they happened rather than acting on warning signs buried in the data they already had, which meant unplanned downtime kept eating into operational efficiency.

A prediction system that reads the same logs engineers already had, and acts on them.

Head of AI built a failure prediction system on top of the manufacturer’s own error logs, paired with an LLM and RAG setup trained on manufacturer-specific technical documents. Instead of engineers manually cross-referencing logs against documentation, the model does it continuously.

The system gives engineers real-time support: it generates suggestions and flags potential failures as data comes in, rather than waiting for a scheduled review or a breakdown to trigger action.

The result is a tool that tells engineers not just that a failure might happen, but when and why, turning historical log data into a live early-warning system for maintenance teams.

Maintenance moved from reactive to predictive.

By connecting error logs and manufacturer documentation through LLM and RAG, the firm turned data it already had into a working early-warning system. Engineers get real-time, actionable predictions instead of after-the-fact diagnostics, and maintenance has shifted from reacting to failures to preventing them.