BuildSimHub has four main components: BuildSim Cloud, Cloud Simulation toolset, Code Compliance toolset, and Data Analytic toolset. BuildSim Cloud is the first energy model management platform that can read and analyze EnergyPlus models. Equipped with the popular version control system, GIT, the BuildSim Cloud can effectively track model changes, compare and merge the model differences. It is a perfect tool for managing complex modeling projects in a collaborative environment for multiple parties. Cloud simulation tool significantly improves the work efficiency of modeling through smart simulation options. With large models, the Cloud simulation can be configured to run four periods in a year in parallel to boost the simulation speed by up to 2.5 times. For parametric studies, the system can optimize the resources to parallelize the simulations, ensuring the job be completed in time. Code compliance toolset includes automating both performance baseline models as well as local and certification compliance reports. Currently, there are ASHRAE 90.1 PRM (performance rating method) automation tool, NYC EN1 reporting tool and Title 24 reporting tool. With a model built based on the design case, the user can quickly get a baseline model as well as a populated compliance report with a button click. Data Analytics is the first ever toolset provides data visualization and machine learning modules for energy modeling tasks. More than thousands of data points including time-series (hourly, monthly), surface level, zone level, and building level outputs can be easily displayed with interactive charts and 3D geometry in a few clicks. The energy modeling dashboard, built on top of charts, is customizable and shareable among team members, which significantly improves the model quality reviews. The dashboard can be easily templated and reused in any other modeling tasks. Last but not the least, new released BuildSim Learn is the first machine learning module built for energy modeling. It utilizes the power of artificial intelligence to boost the efficiency of energy modeling. In a recent case study, the module enables real-time instant design decision making as well as reduces design optimization time by more than 50%.