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Success story

Scalable, fully automated climate risk assessments using generative AI and scientific frameworks

Climate-related risk assessments are often manual, resource-heavy, and built around bespoke methodologies, making them costly for large companies and nearly out of reach for SMEs. On top of that, the lack of standardized, quantifiable frameworks makes consistent evaluation difficult. Ekimetrics set out to build a scalable, data-driven solution that uses large language models to automate risk assessments—ensuring outputs are not only tailored and robust, but also explainable for audit and regulatory review.

200+
data points consolidated into each risk assessment
1,023
industries mapped using ISIC-compliant classifications
100%
automation achieved—no sustainability expertise required to complete the assessment
Discover

What we did

01

Challenge

  • No standardized method for measuring climate risk at the company level, making comparisons difficult and inconsistent
  • Assessments often relied on in-house ESG expertise or external ESG specialists, especially for large corporations with complex operations
  • Collecting company-specific data for analysis was time-consuming and required significant manual effort
02

Our approach

  • Developed a bottom-up, data-driven methodology factoring in asset location, type, and sector to evaluate climate risk more precisely at the company level
  • Anchored physical risk scoring in the INFORM Risk Index, a widely recognized standard for country-level risk data
  • Used large language models to generate structured climate vulnerability assessments for ISIC-aligned industries, with expert validation prior to deployment
03

Outcome

  • Replaced fragmented, manual processes with a fully automated risk assessment engine, making science-based evaluation accessible at scale and without sustainability expertise Evidence-backed insights combined LLM-generated reasoning with expert-reviewed content to support transparency and auditability
  • Scalable assessment framework built for reuse across other qualitative risk domains, using GenAI to streamline workflows and boost efficiency

    

Challenge

Our approach

Outcome

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