GanttAI trains its model by analyzing historical project data, cleaning and categorizing tasks, and applying machine learning to identify patterns in task durations, dependencies, and delays. As more data is added, the model continuously refines its predictions to generate highly reliable, bias-free schedules.

Yes, you can build a model with poor historical data, but the trade-off is lower prediction accuracy. However, you can still benefit from GanttAI’s autogenerated task lists, AI-driven data interaction, and collaborative features to enhance your scheduling workflow while improving predictions over time.

Yes, if historical resource loading data is available, we incorporate it as a project attribute when training the model. This helps GanttAI understand crew productivity and use that insight for future resource allocation and scheduling.

Yes, absolutely. Your data is never shared, and any models trained by you or for you remain fully private.