Dr Adam William Chalmers

Senior Lecturer (Associate Professor), University of Edinburgh

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Projects

Founder & CEO of Resonate AI Ltd., an AI-driven platform that assesses and improves corporate sustainability reporting, with NLP and retrieval-augmented generation pipelines applied at scale to corporate disclosures.

  • Compliance and benchmarking applications across major reporting frameworks
  • Analysis of large corpora of corporate sustainability reports (200,000+ reports; 15,000+ companies)
  • Partnerships including GRI, UN Global Compact, King's College London, KPMG, and FTSE 350 teams

Founder of KouLi, an AI-powered interview and communication training platform that combines real-time voice interaction with retrieval-augmented generation to deliver highly tailored, high-pressure practice environments.

  • Multimodal analysis of user responses (text + audio) to assess clarity, structure, and delivery
  • Dynamic interview generation using CV and job description grounding
  • Real-time feedback on confidence, coherence, and answer quality using structured scoring models
  • Applications across job interviews, executive communication, and public speaking training

Co-founder of x.Machina, a global AI governance intelligence platform designed to map, analyze, and interpret national AI strategies, regulatory frameworks, and policy instruments.

  • Curated corpus of AI governance documents across jurisdictions, with structured metadata and version tracking
  • Comparative analytics of national strategies, regulatory approaches, and policy diffusion
  • Integrated “Ask AI” interface for querying governance trends, risks, and institutional design
  • Bridging academic research and applied policy intelligence for governments, firms, and researchers

Co-lead of Carrots & Sticks, a global database of corporate sustainability policy, maintained with NLP and machine learning methods to track regulatory and voluntary instruments worldwide.

Parliament & consultation analytics

Scottish Parliament Academic Fellowship (2023–2024): development of machine learning tools to trace how public consultation responses feed into speeches, motions, and legislative scrutiny.