WhyHow.AI has developed an open-source platform integrated with MongoDB to enhance the use of knowledge graphs for data management and insights, providing a scalable foundation with vector search and aggregation features to support AI endeavors. This solution addresses the limitations of traditional retrieval-augmented generation systems by using knowledge graphs to capture intricate relationships and contextual nuances in enterprise data, enabling accurate and deterministic retrieval processes. The platform combines large language model workflows with Python and JSON-native graph management to efficiently query and derive insights from structured and unstructured data. MongoDB’s capabilities, including its modular architecture and vector search, facilitate the seamless integration of data, making it an ideal choice for managing complex datasets and improving retrieval accuracy. The use of knowledge graphs extends beyond information retrieval, serving as a foundation for enterprise memory and supporting future model training and insights by maintaining structured datasets. This collaboration allows organizations to unlock the potential of their data, driving innovative knowledge management solutions.