AI-powered property intelligence and investment decision support
PropertyGPT
PropertyGPT explores how AI can assist buyers and investors by combining listing data, location intelligence, infrastructure signals, rental yields, market trends, and user preferences.
Story
Property searches usually force people to stitch together scattered facts and gut feeling. I built PropertyGPT to turn that fragmented process into a clearer decision workflow that surfaces trade-offs, risks, and opportunity signals in one place.
Problem
Property decisions require synthesizing fragmented inputs (pricing, yield, location, infra, risk) that are hard for buyers and investors to evaluate consistently.
Solution
Built a decision-support system that fuses structured market data with retrieval-augmented LLM reasoning to generate personalized assessments, opportunity/risk flags, and recommendation rationale.
Outcome
Demonstrated a practical AI workflow for real-estate decision support, turning scattered market signals into explainable, user-specific insights.
Architecture
A placeholder implementation path that can be expanded with screenshots, data contracts, system diagrams, and measurable results as the project matures.
01
Listing ingestion
02
Location + infrastructure enrichment
03
Rental yield and trend features
04
User preference profiling
05
RAG + LLM synthesis
06
Recommendation and risk summary
Product Artifacts
Sanitized examples to demonstrate product thinking and execution style when proprietary materials cannot be shared.
- PRD outline (problem framing, success metrics, rollout plan)
- Workflow wireframe / journey snapshot
- Evaluation rubric or quality checklist
- Operational metrics dashboard mock
Metrics to Track
- Recommendation relevance
- Decision-time reduction
- User trust/clarity score
- Signal coverage across properties
Product Role
- Defined product scope and decision framework
- Designed data synthesis and recommendation logic
- Prototyped explainable AI decision outputs