CASE STUDY
Using AI to Streamline Home Construction Planning
KEY OUTCOME
The client automated forecasting and prediction processes, saving time, reducing costs, and enabling smarter planning.
How did Data Ideology solve this organization’s challenge?
CHALLENGE SUMMARY
The client’s lot start planning was heavily manual, requiring teams to research surrounding communities to determine what homes to build next. Property listing descriptions were also being manually written and copied into MLS and website systems. Construction stage predictions and simple calculations were done by hand, and key option selections from contract addendums were unavailable in any digital system. These inefficiencies slowed decision-making and increased labor costs.
SOLUTION SUMMARY
Data Ideology leveraged Snowflake’s Enterprise Data Warehouse and Operational Data Store to support AI-enabled use cases. Third-party contractors accessed this environment to develop models that predicted lot starts and automated property descriptions. Azure Data Factory Machine Learning was used to analyze home construction stage cycles using historical attributes. Additionally, a custom Doc AI model was built in Snowflake to extract and structure option selection data from final contract addendums—turning previously inaccessible documents into actionable data.
Deep Dive
THE CHALLENGE
- Manual planning of lot starts drained resources and delayed build schedules.
- Sales teams were spending time writing and pasting home descriptions instead of selling.
- Construction cycle time predictions lacked precision and automation.
- Option selections in contract documents were invisible to reporting systems.
- Decision-makers lacked timely, structured data to guide planning and guarantees.
THE RESULTS
- Automated lot start forecasting reduced time and effort spent on community research.
- AI-generated listing descriptions saved hours of manual sales team work.
- Implemented predictive modeling to support the Close Ready Guarantee (CRG) program.
- Extracted contract addendum data for the first time, unlocking new reporting capabilities.
- Shifted key forecasting and planning tasks from manual to AI-assisted processes, enabling faster and more accurate decisions.
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