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iCOR has been shortlisted for the IIRSM 2025 Research Project of the Year award, recognising its innovative approach to simplifying UK environmental legislation compliance. Designed to help organisations navigate complex regulations efficiently, iCOR is a digital audit solution that transforms legal compliance from a tedious task into an accessible and streamlined process.

Environmental legislation is often overwhelming, requiring businesses to sift through legal jargon and extensive documentation. iCOR addresses this challenge by combining expert knowledge with cutting-edge AI and natural language processing (NLP). In collaboration with the Hartree Centre North East Hub, iCOR developed a proof-of-concept that automates the categorisation and summarisation of legislation. This ensures businesses receive accurate, relevant, and easy-to-understand compliance information, aligned with ISO 14001 requirements.

This AI-driven approach has significantly improved efficiency, reducing the risk of non-compliance while enabling organisations to focus on sustainability goals. By eliminating the uncertainty surrounding legal obligations, iCOR has empowered businesses to integrate compliance seamlessly into their operations. The project’s success has also driven internal growth, leading to team expansion in legal authoring, administration, and sales.

Beyond business impact, iCOR’s innovation contributes to environmental protection, health, and risk management by improving awareness and competence. By making compliance more accessible, it supports organisations in avoiding legal penalties and reputational harm while fostering best practices.

Being shortlisted for this prestigious award highlights iCOR’s role as a disruptor in the legal register assist market. It underscores the importance of leveraging AI to modernise compliance processes and reflects iCOR’s commitment to driving industry-wide change.

External URL: https://www.hartreenortheast.uk/insights/case-study-automating-analysis-of-environmental-legislation-using-large-language-models