Tracking financial investments in climate adaptation is complex and expertise-intensive, particularly for Early Warning Systems (EWS), where multilateral development bank (MDB) and fund reports lack standardized financial reporting and appear as heterogeneous PDFs with complex tables and inconsistent layouts.We introduce an agent-based Retrieval-Augmented Generation (RAG) system that uses hybrid retrieval and internal chain-of-thought (CoT) reasoning to extract relevant financial data, classify EWS investments, and allocate budgets with grounding evidence spans. While these components are individually established, our contribution is their integration into a domain-specific workflow tailored to heterogeneous MDB reports and numerically grounded EWS budget allocation. On a manually annotated CREWS Fund corpus, our system outperforms four alternatives (zero-shot classifier, few-shot “zero rule” classifier, fine-tuned transformer-based classifier, and few-shot CoT+ICL classifier) on multi-label classification and budget allocation, achieving 87% accuracy, 89% precision, and 83% recall. We further benchmark against the Gemini 2.5 Flash AI Assistant on an expert-annotated MDB evidence set co-curated with the World Meteorological Organization (WMO), enabling a comparative analysis of glass-box agents versus black-box assistants in transparency and performance. The system is publicly deployed and accessible at https://ews-front.vercel.app/ (see Appendix A for demonstration details and Appendix B for dataset statistics and splits). We will open-source all code, LLM generations, and human annotations to support further work on AI-assisted climate finance.

InfluenceMap’s LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity’s support or opposition to science-based policy pathways for achieving the Paris Agreement’s goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.