Real Estate Chatbot
Overview
- Audience: Real‑estate agencies
- Product: AI‑powered chatbot delivering detailed, real‑time insights on property listings.
- Impact: Boosted client engagement and reduced agent workload by automating routine inquiries.
Role & Timeframe
As the sole developer, I handled everything from ideation and database integration to deployment. The first production release shipped in 5 weeks (March → April 2025).
Context / Problem
Real‑estate agents field countless repetitive questions—price, amenities, neighborhood stats, renovation history—that sap time from high‑value client interactions. Prospective buyers meanwhile expect instant, comprehensive answers to stay engaged and make confident decisions.
Solution
I built an AI‑driven real‑estate chatbot tightly integrated with a live property database. It answers niche property‑specific questions in real time, including:
- Price & availability
- Amenities (pools, gyms, smart‑home tech)
- Property age & renovation history
- Neighborhood facts & nearby conveniences
Key Technical Decisions
- OpenAI GPT‑4 API for natural, conversational responses.
- PostgreSQL for fast, reliable property data retrieval.
- Next.js + Tailwind CSS for a performant, responsive front‑end.
System Diagram
High‑level architecture: users chat with a Next.js front‑end; queries hit GPT‑4 and a PostgreSQL database for contextual answers.
Results & Screenshots
The assistant pulls tenant data directly from the live database.
When data is missing, the chatbot states it doesnt know instead of guessing.
Since deployment:
- ↓ 40 % reduction in repetitive agent interactions
- ↑ 55 % faster response time to prospective buyers
- Positive feedback from early‑adopter agencies praising the chatbot’s accuracy and ease of use
Next Steps
- Deepen contextual understanding for complex, multi‑turn inquiries
- Create a more polished front-end