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Design/methodology/approach This research evaluated the databases of SQL,No-SQL and graph databases to compare and contrast efficiency and performance.To perform this experiment the data were collected from multiple sourcesincluding stock price and financial news. Python is used as an interface toconnect and query databases (to create database structures according to thefeed file structure, to load data into tables, objects, to read data , toconnect PostgreSQL, ElasticSearch, Neo4j. Purpose Modern applications of LLM(Large language model) including RAG (Retrieval Augmented Generation) withMachine Learning, deep learning, NLP (natural language processing) or DecisionAnalytics are computationally expensive. Finding a better option to consumeless resources and time to get the result. Findings The Graph database of ESG(Environmental, Social and Governance) is comparatively better and can beconsidered for extended analytics to integrate ESG in business and investment.Practical implications A graph ML with a RAG architecture model can beintroduced as a new framework with less computationally expensive LLMapplication in the equity filtering process for portfolio management.Originality/value Filtering out selective stocks out of two thousand or morelisted companies in any stock exchange for active investment, consuming lessresource consumption especially memory and energy to integrate artificialintelligence and ESG in business and investment.

Graph database while computationally efficient filters out quickly the ESG integrated equities in investment management