Documentation

AI Research & Automation

FeasiBuild is not just a calculator; it is an AI-powered feasibility engine. From the moment you select a location and asset type, AI works in the background to research market benchmarks, calibrate financial models, and generate institutional-grade commentary.

1. Market Research & Smart Defaults (Components 1 & 2)

The foundation of any feasibility study is accurate market data. FeasiBuild's AI engine analyzes your initial inputs (Country, City, Asset Type, Segment, and Positioning) to automatically populate default values for construction rates, land costs, revenue benchmarks, and operating expense ratios.

Dynamic Real-Time Research

FeasiBuild conducts live market research the moment you make your selections. It scrapes and analyzes current transaction data, construction cost indices, and rental comparables to generate bespoke, up-to-the-minute placeholder values and S-Curve phasing profiles.

2. Contextual AI Hints & Guardrails

Beyond just filling in numbers, the AI acts as a junior analyst, providing contextual guidance and validating your inputs against institutional norms.

  • •Rule-of-Thumb Recommendations: In Component 1 (e.g., Construction Period), the AI analyzes your building configuration (e.g., 12 tower floors) and suggests a realistic timeline range (e.g., 24-36 months).
  • •Allocation Benchmarks: In Component 1 Step 13, the AI suggests precise percentage splits for Soft Costs and POWC based on your specific asset class.
  • •Financial Guardrails: The AI defines "safe" thresholds (e.g., Land/TDC target ≤ 51%, Minimum DSCR 1.4x) and flags when your inputs breach market norms.
  • •Override Tracking: When you manually change an AI-populated field, the system highlights it with an "amber border" to maintain a clear audit trail of AI defaults vs. user overrides.

3. Automated Feasibility Study Generation

Once the financial model is complete, the AI synthesizes all your inputs, selections, and financial results into a comprehensive, narrative-driven Feasibility Study.

How it Works:

The AI does not just dump data into a template. It uses a fixed parameter framework—meaning it knows exactly which slides and topics need to be covered for a specific asset type. It then dynamically writes the content by combining:

  • User Inputs: Location, asset positioning, and specific design choices.
  • Financial Results: The actual IRRs, multiples, and cash flow outputs from Components 1-6.
  • Market Commentary: AI-researched context about the specific sub-market (e.g., Dubai Marina high-rise trends).
  • Risk Factors: Automatically generated risk matrices based on the scenario analysis.

4. Intelligent Scenario Calibration (Component 6)

Stress-testing a project requires realistic assumptions about how markets move. The AI calibrates the "Downside" and "Upside" scenario presets based on historical volatility for your specific asset class and region.

For example, a "Downside" shock for a Dubai Residential High-Rise will apply different percentage drops to Sales Price and Velocity than a "Downside" shock for a Malaysian Landed development. The AI ensures your stress tests reflect actual market behavior, not arbitrary guesses.