stclarke details how a Microsoft Garage Hackathon team leveraged generative AI, Azure OpenAI, and Semantic Kernel to revolutionize permitting for clean energy projects, making regulatory compliance faster and more efficient.

Generative AI Powers Faster Clean Energy Permitting via Microsoft Garage Hackathon

Introduction

Microsoft’s Project GreenLight emerged from the company’s Garage Hackathon, driven by the mission to accelerate the deployment of clean energy through streamlined permitting processes—a known bottleneck in the industry. The effort, involving 53 team members, utilized generative AI and Microsoft cloud technologies to transform permitting for nuclear, renewable, and mining sectors.

Background and Motivation

Permitting delays can hold up new energy projects for over a decade and cost millions, making the need for a faster, more transparent process existential for achieving decarbonization goals. Project GreenLight began as a 2024 Hackathon project and has since become a commercial workstream, generating real-world impact and revenue.

Hackathon Approach

  • Collaborative Kickoff: The Repowering Coal Consortium and Microsoft brought together industry experts to identify permitting as the core challenge.
  • Team Formation: 53 hackers from across Microsoft joined forces, planning roles and workstreams in advance for the Hackathon.

Key Solutions

  • Automated Document Creation: Generative AI models draft permitting documents from both historic and project-specific data.
  • Copilot for Permitting Engineers: An AI assistant built on Azure, running privately within the customer’s tenant, helps engineers query vast regulatory datasets and avoids external data exposures.
  • Pre-Submission Review: AI reviews applications for missing information or inconsistencies before formal regulator submission, reducing delays.
  • Agentic AI Workflows: Multiple AI agents now draft, review, and refine documents collaboratively and in real time.

Technical Stack

  • Azure OpenAI Service: Supplies generative AI capabilities for flexible document creation and regulatory analysis.
  • Semantic Kernel and Kernel Memory: Used to intelligently select and use data sources, structuring outputs for specific regulatory requirements.
  • Modular, Scalable Architecture: Allows extension into various energy, mining, and renewables scenarios.

Results and Impact

  • Realized 25–75% productivity gains in permitting workflows.
  • Expanded to a commercial workstream in MCAPS Energy & Resources, serving energy, mining, and regulatory customers globally.
  • Supported by Microsoft leadership and active collaboration with governments/regulators.

Lessons Learned

  • Gen AI enabled solutions traditional software approaches couldn’t handle, due to its ability to flexibly process massive and varied datasets.
  • Hackathon environment accelerated innovation, encouraging rapid iteration and team collaboration.

Next Steps

The system is being expanded to new scenarios including mining and offshore wind, with a focus on modularity and regulatory collaboration. The core AI permitting layer will allow different industries to add custom data and workflows.

Team

  • Key Contributors: Conor Kelly, Henning Kilset, plus numerous Microsoft engineers and innovators.

Conclusion

Project GreenLight demonstrates how Microsoft’s Garage Hackathon culture, cloud AI technology, and industry collaboration can drive meaningful impact—not only for Microsoft, but for the broader energy and regulatory landscape.

For more, visit Microsoft Garage Wall of Fame.

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