The Proof Isin the Numbers.

Explore real projects where GreenBridge has helped renewable energy teams improve performance, reduce costs, and recover financial value.

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MeasurableWhere It Matters.

Explore real deployments and the measurable outcomes behind them, from recovered value and lower operating costs to improved asset performance.

Solar panels under a clear skyCase Study

1.6 GW of wind and solar, one intelligence layer across.

An AWS-verified deployment projecting $1.2–1.8M in additional annual revenue from smarter yield and loss-factor analysis.

  • TechnologyWIND + SOLAR
  • RegionASIA
  • Capacity1,600 MW

The challenge

This customer operates 1.6 GW of wind and solar across Asia, and was seeing inconsistent production across the fleet: performance degradation nobody could fully explain, with revenue slipping as a result.

The GreenBridge.AI solution

GreenBridge.AI’s agentic workflows, built on LangGraph, establish a performance baseline for every asset, accounting for season, equipment spec, and site conditions. From there the system sorts degradation into specific failure modes: MPPT tracking issues, DC current imbalances, and thermal derating for solar; pitch-system, gearbox, and generator anomalies for wind. A multi-factor root-cause engine weighs electrical, thermal, mechanical, and environmental signals together before turning each finding into a prioritized, inventory-checked action plan.

Results & benefits

2–3%increase in specific yield
$1.2–1.8Madditional annual revenue
22–24%reduction in mean time to repair
20–28%fewer parts-related delays
15–20%lower maintenance cost per MW
$3.5–5.5Mdeferred capital replacement, 5-yr

Figures are based on pilot-site data; full-deployment measurement is ongoing.

Wind turbines on green hillsCase Study

Catching failures before they cost millions.

Predictive maintenance across a 4 GW portfolio, projecting 16–18% less unplanned downtime from inverter failures.

  • TechnologySOLAR + WIND + STORAGE
  • RegionNORTH AMERICA
  • Capacity4,000 MW

The challenge

This customer manages a 4 GW portfolio of utility-scale solar, wind, and storage, and found that 90–95% of its operational issues traced back to basic problems, with inverter and MV transformer failures chief among them, driving the bulk of the portfolio’s revenue losses.

The GreenBridge.AI solution

GreenBridge.AI layers two agentic modules together: a Specific Yield engine that flags underperforming assets against AI-generated performance models, and a Remaining Useful Life engine that forecasts when inverters and transformers will need maintenance or replacement, feeding straight into a reliability-centered maintenance plan. It all runs on AWS IAM and VPC, so it scales with the portfolio without loosening access control.

Results & benefits

16–18%less unplanned downtime from inverters
21–22%fewer MV transformer issues
1.5–2%increase in annual energy production
$1.3–1.8Madditional annual revenue
12–14%lower emergency maintenance costs
~55%failure-prediction accuracy, 1–2 months out

Early-phase deployment figures; predictive accuracy is expected to improve as the model accumulates operational data.

Operate With Decisions,Not Dashboards.

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