Reconstructed for Portfolio — NDA Protected
Developed an intelligent property-matching system that cut buyer response time by 90% and helped a real estate professional dramatically increase deal closures within months.
Context
A real estate advisor was overwhelmed with manual, repetitive tasks—sorting spreadsheets, cross-checking listings, and verifying property preferences for each potential buyer. These manual processes slowed response times, caused missed opportunities, and limited client capacity.
My Role & Responsibilities
Conducted an in-depth analysis of the client’s workflow to pinpoint inefficiencies.
Designed and developed an AI-powered listing recommender that automated property filtering and ranking.
Integrated natural language understanding so the agent could interact with the system conversationally.
Created an intuitive interface to make recommendations instantly accessible from any device.
Tech Stack
OpenAI API for natural language interpretation
Custom-built property database (SQL-based)
Python + FastAPI for backend logic and property scoring
Lightweight front-end dashboard for real-time search and display
Steps I Took
Workflow Discovery – Mapped every step of the property search process to identify time drains and repetition.
System Design – Modeled buyer intent extraction using NLP to interpret unstructured input (e.g., “three-bedroom near metro, good schools”).
Algorithm Development – Built a weighted scoring engine that matched listings against criteria such as location, amenities, and historical client behavior.
Interface Development – Designed a streamlined dashboard for search, ranking, and email-based client sharing.
Testing & Iteration – Validated matches using historical data to fine-tune accuracy and speed.
Outcome (Approximate / Relative Metrics)
Matching Speed: Reduced property search time by 90%.
Conversion: Tripled monthly deal closures due to faster and more personalized responses.
Client Experience: Boosted buyer satisfaction through accurate and relevant recommendations.
Scalability: System scaled easily to new city-level datasets with minimal reconfiguration.
Learnings & Trade-offs
Training the AI on real regional data greatly improved match quality.
Maintaining simplicity in UX was critical—AI worked best when it blended invisibly into daily tools.
Avoiding data overload and focusing on top 5 results built user trust and improved adoption.
