TURBINES TO AIRLINES

Whether your goal is to predict outages and increase energy production of a wind farm, or improve efficiency and reduce costs in airline operations, SparkCognition drives impact with proven AI solutions.

SparkCognition is now offering the world’s first AI-powered asset management platform for renewable energy. See how the wind industry benefits from reducing asset downtime and improving efficiency to gain a competitive edge.

Introducing the world's first AI-powered asset management platform for renewable energy

By leveraging technology and domain expertise in conjunction with AI expertise and our extensive solution portfolio, SparkCognition offers energy providers a wide range of capabilities across their operations.

AI and physics-based modeling

Optimized maintenance and asset protection

Prescriptive maintenance

Knowledge retention and process streamlining

Multi-asset renewable support

Total visibility across wind, solar, hydro, and storage

Energy trading

AI-based hedging, real-time P&L management

AI-based production forecasting

Increased revenue and bankability

Cloud-based SaaS

Rapid deployment and insight generation in days

Advanced OT cybersecurity

Zero-day thread protection for online and offline assets

Ready to start your AI journey?

We build AI solutions that unearth the insights living within our customers’ data.

We’re constantly innovating to create exponential technologies that allow our clients to optimize operations, predict future events, protect their assets, and accelerate their growth.

While others speculate and chase hype, we’re motivated by what’s proven, scalable, and impactful. 

Get in touch with us by submitting this form.

Related Wind Resources

White paper: the asset management and predictive analytics solution for clean energy

Learn how SparkCognition’s Ensemble Energy platform improves profitability by increasing energy production, reducing maintenance costs, and maximizing operational efficiency.

Case study: energy loss accounting

Read this case study to learn more about how physics-informed machine learning approaches enable wind farm owners and operators to understand the causes of energy loss and the resulting revenue loss.