AI Redevelopment Plan with Automated Design

I’m redeveloping an under-utilised site into a vibrant mixed-use destination that blends residential, commercial and recreational facilities. The project hinges on an AI-first workflow: automated design processes will turn raw data into test-fit layouts and concept schemes so early decisions are informed, fast and defensible.

Scope
The datasets on hand span demographic shifts, live market-trend feeds and detailed environmental-impact studies. They will drive the generation of multiple design iterations—parametric massing, unit mixes, daylight and energy simulations—until we converge on a concept that balances density, livability and sustainability across homes, retail and leisure spaces.

Key activities
• Ingest and clean the three data streams, exposing actionable metrics inside a repeatable pipeline (Python, Pandas, or preferred stack).
• Train or configure the generative design engine of your choice—Grasshopper + Galapagos, Autodesk Forma, or a custom ML model—to cycle through thousands of scheme options while respecting zoning and ESG constraints.
• Visualise the highest-scoring alternatives with clear KPIs: residential yield, commercial GFA, green-space ratio, carbon footprint, projected IRR.
• Package the final concept set as editable 3D files (Revit, IFC or Rhino) and a concise decision report.

Acceptance criteria
1. At least three distinct AI-generated master-plan options, each traceable back to input data assumptions.
2. Source scripts/notebooks and a short README to re-run the pipeline on updated data.
3. Presentation-ready visuals and a summary PDF highlighting how demographic, market and environmental insights shaped the recommended scheme.

This assignment is self-contained and milestone-driven; once the concept package is signed off, any further detailing can become a separate phase. Let’s create a data-smart blueprint that delivers both community value and commercial strength.