Description
What if you could generate a reliable MEP cost estimate before modeling a single element in Revit software? That's exactly what we built: a Python-based estimation bot trained on more than 300 real MEP projects from our historical data. Instead of relying on disconnected spreadsheets or manual takeoffs, the bot uses pattern recognition to generate estimates at the proposal stage before BIM begins. Our solution achieves more than 95% accuracy versus final costs, and it reduces estimation time by more than 70%. In this session, we'll show how we structured the data, trained the model, and integrated it into our workflow. You'll leave this session with a practical framework to turn historical data into a predictive asset.
Key Learnings
- Discover how to clean and structure historical MEP data to build a reliable training data set for predictive estimation models.
- Learn about the architecture of a Python-based machine-learning estimation bot and how to validate its accuracy against real outcomes.
- Learn about applying a phased framework to integrate predictive estimation into pre-BIM proposal workflows.
- Learn the business impact of using proprietary data versus off-the-shelf estimation tools.