This talk describes how the presenter developed a free graph-based geo-intelligence engine that serves fast, scalable, and reliable data analysis. The engine's value lies in its flexibility and applicability to any relational dataset, as well as its integration of open-source technologies and libraries. They chose to build their geo-intelligence engine on a graph infrastructure to enable faster, index-free queries and better support for interconnected data. To showcase the capabilities of our engine, a geo-financial software that provides users with a powerful tool for analyzing financial scores of companies based on geo-location was developed. Businesses can quickly and easily analyze data to gain valuable insights into competitors, potential partnerships, and market trends. This software presents the results of the analysis in a user-friendly and visually appealing format, making it accessible even to non-technical users. Their geo-financial analysis software is based on user-specified location and range. The user interacts with an Angular frontend, which incorporates the Leaflet library for map interaction and an OpenStreetMap basemap. The backend is based on Golang, which handles authentication and message queueing interaction with a Python analysis tool. The data retrieved for Python processing comes from a Neo4j graph database, which is accessed through Cypher queries and networking algorithms. All of the software components are located in separate containers, promoting flexible and independent scalability achieved with Docker Compose and orchestrated by Kubernetes. In this presentation, their graph-based geo-intelligence engine will be discussed, which is the backbone of our application. The geo-financial analysis application itself, providing a demo and demonstrating how it can be used for business geo-intelligence analysis will be show cased.