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<p>Investors and analysts spend an average of three minutes and forty-four seconds reviewing startup pitch decks, a process that requires significant manual effort to extract key performance metrics. This challenge is particularly acute for sustainability startups, where both financial and impact indicators aligned with the United Nations Sustainable Development Goals (SDGs) must be evaluated. Addressing this gap, the present study introduces a Generative AI (GenAI)-driven framework that automates the retrieval of financial and sustainability indicators from unstructured pitch deck data. The system integrates OpenAI’s embedding models, FAISS (Facebook AI Similarity Search), and LlamaIndex to enable semantic retrieval and contextual extraction of financial metrics such as revenue and profit/loss, alongside sustainability-related impact narratives. A dataset of 94 Indian sustainability startup pitch decks was analysed to test the framework’s accuracy and efficiency across diverse sectors including clean energy, agritech, and circular economy ventures. Results demonstrate that the model maintains analytical precision and interpretability significantly reducing manual screening time. Furthermore, the framework successfully maps extracted impact indicators to relevant SDGs, allowing for a holistic assessment of financial performance and sustainability outcomes. The findings underscore the transformative role of GenAI and natural language processing in automating due diligence and investment analysis, offering a scalable, transparent, and data-driven approach to evaluating startups. By bridging financial analytics with impact measurement, this study contributes to advancing the use of AI in sustainable finance and provides a foundation for more consistent, efficient, and equitable startup evaluations that align investment decisions with broader societal goals.</p>

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Generative AI-Based Framework for Advancing Pitch Deck Evaluation of Sustainability Ventures

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Sruthi Kannan,
  • Divya Eshwar,
  • Guhan Chandrasekaran,
  • Bhaskar Bhowmick,
  • Kumar CS

摘要

Investors and analysts spend an average of three minutes and forty-four seconds reviewing startup pitch decks, a process that requires significant manual effort to extract key performance metrics. This challenge is particularly acute for sustainability startups, where both financial and impact indicators aligned with the United Nations Sustainable Development Goals (SDGs) must be evaluated. Addressing this gap, the present study introduces a Generative AI (GenAI)-driven framework that automates the retrieval of financial and sustainability indicators from unstructured pitch deck data. The system integrates OpenAI’s embedding models, FAISS (Facebook AI Similarity Search), and LlamaIndex to enable semantic retrieval and contextual extraction of financial metrics such as revenue and profit/loss, alongside sustainability-related impact narratives. A dataset of 94 Indian sustainability startup pitch decks was analysed to test the framework’s accuracy and efficiency across diverse sectors including clean energy, agritech, and circular economy ventures. Results demonstrate that the model maintains analytical precision and interpretability significantly reducing manual screening time. Furthermore, the framework successfully maps extracted impact indicators to relevant SDGs, allowing for a holistic assessment of financial performance and sustainability outcomes. The findings underscore the transformative role of GenAI and natural language processing in automating due diligence and investment analysis, offering a scalable, transparent, and data-driven approach to evaluating startups. By bridging financial analytics with impact measurement, this study contributes to advancing the use of AI in sustainable finance and provides a foundation for more consistent, efficient, and equitable startup evaluations that align investment decisions with broader societal goals.