As a data science consultant for this artificial intelligence company, I focused on improving the representation of and access to a proprietary dataset with automated data processing, graph databases, and large language models. By automating the processing and validating of tabular data, I improved data integrity and reduced the manual time spent reviewing records by automating the processing and validating of tabular data. After enhancing data quality, I established a knowledge graph that better modeled the relationship-rich data that was modeled and maintained in a tabular format. By creating a SME-informed graph data model and importing the tabular data into a Neo4j graph database using Python and Cypher, I provided more intuitive ways to analyze, interact with, and visualize data. To further improve the experience of interacting with and querying the graph database, I researched and tested how best to use ChatGPT’s large language model to convert natural language into dynamic Cypher queries that retrieve information from the knowledge graph without directly interacting with the graph and/or using code. The research paper written to share findings was published on OnePetro (under my maiden name Gipson).