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Lucid Axon: A Local Futures Partner

Wednesday, December 3, 2025

We are pleased to announce a new partnership with Lucid Axon, a Montreal based start-up that has pioneered an Artificial Intelligence (AI) platform to analyze and evaluate organizational performance in terms of ESG, the SDGs, and sustainability impact. Our partnership with Lucid Axon will focus on the use of their AI platform to complete municipal SDGs assessments for each of our five partner municipalities. 

The AI platform allows us to aggregate multiple publicly available municipal strategies, policies, programs, and budgets to generate an understanding on how current municipal practice aligns with the targets of the SDGs. This work will produce:

  • An SDGs alignment summary by SDG target
  • Alignment scoring for each SDG with supporting evidence
  • Gap identification for SDG targets not well represented in the analysis
  • Source document citations for each evidence point to support verification and trust in the results.

A summary of these results will be included in the Voluntary Local Reviews for each partner municipality to provide a snapshot of the trajectory of the municipality towards achieving the SDGs.

The use of AI has the potential to be a powerful tool in how we solve complex problems. While much remains to be seen on the benefits to humanity in adopting and implementing the use of AI in a widespread way, we are excited about its potential to help accelerate progress across all 17 of the Sustainable Development Goals.

Platform showing alignment with the SDGs

Example of the platform that maps activities to specific SDGs, showing alignment and highlighting gaps.

Learn more about Lucid Axon's methodology

Lucid Axon Methodology

For each official SDG target defined by the UN, the methodology establishes qualitative data points that reflect tangible municipal actions, funding decisions, and strategic initiatives. Using a Retrieval-Augmented Generation approach, the system automatically extracts the most relevant text segments from municipal documents containing this information. Then, leveraging GPT-5, it generates structured answers that identify areas of alignment and provide direct evidence by quoting specific passages that support each data point.

Importantly, SDG alignment is not treated as a binary yes/no result. Instead, the methodology applies a graded evaluation framework, classifying each SDG and overall performance into one of four categories: Strong, Moderate, Partial, or None, depending on the depth of contribution to the SDG targets. A “Partial” classification is given when an initiative meaningfully contributes to a single SDG target, while “Moderate” and “Strong” categories reflect broader or more substantial contributions. All classifications are aggregated and averaged across relevant SDGs to provide a comprehensive picture of municipal alignment, creating a balanced view of how municipal actions contribute to the SDGs.