DECISION SYSTEMS · SC-28
AI Property Development Feasibility & Operations System
A property-development decision system combining feasibility modelling, documents, scenarios and workflow automation.
Starting point
Specialized decisions need a system that connects research, modelling, documents and review gates.
01 · BUSINESS PROBLEM
Complex business decisions fail when evidence, assumptions and workflow live in separate places.
Specialized decisions need a system that connects research, modelling, documents and review gates.
Fragmented evidence
Facts and assumptions are spread across files and sources.
Manual synthesis
Important conclusions depend on repeated analyst work.
Weak review trail
It is difficult to see who approved which assumption.
02 · DECISION LOGIC
From evidence to a reviewable decision package.
The system makes source evidence, assumptions, scenarios and approvals explicit.
Decision sequence
Each stage answers a different operating question
Structure source evidence and assumptions.
Quantify the business question.
Challenge weak assumptions and gaps.
Package the evidence for action.
Decision rule — The decision improves when every claim can be traced back to evidence or an explicit assumption.
03 · WHAT CHANGED
Research, modelling and workflow in one controlled path.
The implementation combines structured inputs, analytical logic and reviewable outputs.
Structure evidence before interpretation.
Keep assumptions editable and traceable.
Use scenario analysis for uncertainty.
Require review before material conclusions or actions.
04 · ARCHITECTURE
A modular path from input to decision.
Inputs → preparation → core logic → validation → decision output
SC-28 · SYSTEM ARCHITECTURE
Inputs → preparation → core logic → validation → decision output
Public portfolio implementation
Inputs
Source signals
Capture the operating inputs required by the system. [Python]
Preparation layer
Normalize context and create a stable analytical contract. [FastAPI]
Core system
Core engine
Run the main analytical or automation logic. [PostgreSQL]
Decision logic
Apply the rule, model or orchestration logic that changes the decision. [Pandas]
Validation
Validation
Test outputs against explicit quality criteria. [GeoPandas]
Controls
Keep approvals, thresholds or constraints visible. [OpenAI]
Decision output
Decision output
Expose the result in a form the user can act on. [n8n]
Monitoring
Record outcomes, exceptions and evidence for iteration. [Docker]
Integration boundaries
Python
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
FastAPI
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
PostgreSQL
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
05 · EVIDENCE & ECONOMICS
Measure what changes the decision.
Public implementation, inspectable technical proof and decision-focused validation.
Evidence quality
Sources
12
Representative public example.
Scenarios
3
Representative public example.
Review gates
4
Representative public example.
Review coverage
Traceable assumptions
100%
Representative public example.
Decision outputs
5
Representative public example.
Reference economics
€10M
Reference scenario
1%
Illustrative improvement
€100k
Decision value
06 · TECHNICAL PROOF
Review the code behind the project.
Tools used
BUSINESS CONCLUSION
Specialized analytics creates value by turning fragmented evidence into a defensible decision.
The system is useful when the reasoning path remains visible from source to recommendation.