Teaching wind farms to ask for maintenance before they fail.
BluePeak operated 340 turbines reactively — fixing what broke, when it broke. We built a predictive maintenance platform on their existing sensor data that schedules intervention before failure, not after.
Challenge
Every unplanned turbine stop cost BluePeak an average of €11,000 and three days. Their turbines emitted seventeen thousand sensor streams that nobody looked at until something failed. Maintenance crews drove four hours to sites for issues a model could have flagged two weeks earlier — and field knowledge lived in the heads of six senior engineers nearing retirement.
Approach
The data already existed; the organization around it didn't. We started with a four-week AI opportunity assessment that priced every failure mode, then built models only where prediction genuinely changed a decision — and paired every model with a workflow the field crews actually wanted to use.
Solution
A predictive operations platform that scores component health across the fleet daily, bundles predicted interventions into efficient site visits, and explains every recommendation in the language of vibration patterns and temperature curves the engineers already trust. Crews plan weeks ahead instead of reacting overnight.
Outcome
“We'd been promised AI magic by a dozen vendors. Gilva was the first to start by asking what a failure actually costs us.”