Data-led solutions for
Developers | Investors | Operators | Insurers
With insight into projects and assets across the world, and the largest anonymized operations and risk dataset, Clir provides actionable insights across the project lifecycle. Understand how your portfolio is performing compared to peers and the industry for improved returns, best-in-class risk mitigation, and data to support technical and financial optimization strategies.
Development
Technology selection, siting and contracting decisions are often informed by limited data and a multitude of assumptions. Clir draws intelligence from over 200 GW of operations and risk data to provide evidence to support decision making and contracting in the asset development phase. Developers gain the insight required to design, build and operate better assets.
Operations
Maximize asset performance with a data-driven approach. Using site data, market insights and advanced artificial intelligence, Clir detects areas of underperformance, and identifies opportunities for technical and financial optimization. By enriching your asset data with peer data, owners gain insights into actionable steps to maximize asset value.
Insurance
Data-evidenced identification of project risks improves asset management and minimizes insurance costs. Clir leverages artificial intelligence, and anonymous claims and operations data, for a comprehensive assessment and mitigation strategy of project-specific risks. Data-enabled insights improve risk modelling and insurance terms.
Project acquisitions
Leverage site and industry data to understand project performance and improve financial decisions. By using over 200 GW of industry data, alongside farm-specific monthly reports and SCADA data, Clir provides greater confidence in opportunities, risks and valuations for greater certainty during wind and solar asset acquisitions.
Sell-side M&A
Access deeper insights and evidence to gain a competitive edge in the sale of projects. By using industry and farm-specific data, Clir’s data and insight is used to improve technical assumptions by validating performance upgrades and improving operational energy yields.
