AI
Real Estate
Real estate GPT: AI-powered real estate assistant
RealEstateGPT is a multi-purpose AI assistant designed to help buyers, investors, sellers, and renters navigate the real estate journey through natural conversation. Whether it's discovering the perfect home, estimating a property’s value, comparing investments, or checking rental potential—RealEstateGPT makes it effortless.
overview
The goal was to streamline decision-making by enabling a chat-first AI experience that eliminates the friction of traditional search filters.
Spark
RealEstateGPT was born from the need to simplify complex real estate decisions across buyers, sellers, investors, and renters.
Problem
Real estate platforms were cluttered with filters and fragmented tools, making the journey hard to navigate.
Realization
Users just wanted to “talk” to the platform — ask and get answers, not search through filters.
Vision
Build an AI that understands real estate intents, responds intelligently, and works for every kind of user.
tech stack
Frontend
React
Next.js
Database
Quadrant
Mongo
AI
Open AI
n8n
Lang Chain
ClaudeAI
Perplexity
Hosting/Server
Docker
Aws
Vercel
Backend
Nest.js
Python
Fast API
Design
Figma
Adobe
why it started?
The goal was to streamline decision-making by enabling a chat-first AI experience that eliminates the friction of traditional search filters.
Fragmented Experience
Buyers, renters, and investors needed different workflows with no unifying experience.
Complex UI/Filters
Traditional portals overwhelmed users with dozens of filters and no guidance.
Static Property Listings
No context-aware insights like school scores, yield, or crime rates.
No Personalization
Platforms lacked memory or user-specific recommendations.
what we built?
Kodekage designed and built RealEstateGPT, a conversational AI platform that combines Natural Language Understanding, Vector Search, Real-Time Data Enrichment, and Personalization Memory.
Intent-Aware AI
Built GPT-4-based assistant that identifies real estate intent — buy, sell, invest, rent — from natural language.
Semantic Search
Integrated Qdrant to perform vector-based matching of listings to fuzzy user queries.
Dynamic Follow-ups
Suggested next questions in real time to drive session engagement.
Contextual Memory
Used Redis for both session and long-term memory to personalize future chats.
how we built it?
We engineered RealEstateGPT to blend AI intelligence with real estate expertise — not just answering questions, but guiding users with memory, context, and relevance.
understanding the user and the data
We started by decoding how users think and speak about real estate. Then mapped over 500 TRREB fields and built a smart classifier to route every query meaningfully.
TRREB Insights + Query Behavior
- Mapped 500+ fields from TRREB using RESO standards
- Understood the gap between vague buyer queries and structured listing filters.
Classifier Engine
- Built a smart classifier to route queries: property search, web lookup, or general Q&A
- Trained to understand natural real estate phrasing like 'homes that feel bright near parks'.
building memory & retrieval logic
We implemented short- and long-term memory to personalize every conversation. Vector search bridges vague, natural queries to the most relevant listings using semantic matching.
Short-Term Memory
- Powered by LangChain — keeps chat context alive
- Lets users ask 'Show me more like the condo in North York' and get meaningful results
Long-Term Memory
- Stores user preferences (location, budgets, type)
- Enables personalized sessions even across devices
Vector Search (RAG)
- Used OpenAI embeddings + Qdrant
- Matches free-text queries to relevant listings using semantic similarity
automation + embedding pipelines
A fully automated pipeline fetches, cleans, and updates TRREB data every 24-48 hours. Embeddings are generated, stored, and cached — ensuring answers are fresh and fast.
Data Refresh Loop
- Scheduled ingestion from TRREB (every 24-48h)
- Embeddings updated automatically
- Raw and cleaned data stored in MongoDB
Embedding Workflow
- Titles + descriptions + tags → converted to vectors
- Stored in Pinecone with listing metadata
- Redis used to warm up cache for common queries
making it conversational + useful
LLM responses are enriched with structured UI triggers like maps, tables, or visit forms. We crafted modular prompts per user intent to ensure every reply feels helpful, not robotic.
Prompt Engineering
- Modular templates for each intent (Search, Explore, Ask, Recommend)
- ReAct agent planned for better validation & confidence scoring
UI Response Design
- LLMs respond not only in text but also structured payloads:
- component: map → renders interactive map
- action_type: visit_booking → opens form
- component: chart → pricing insights
get a custom quote
tailored to your
needs
MVP
Ideal for quickly launching your idea to market with essential features.
What you will get
-
Complete MVP and assets
- Development of core MVP features
- Responsive UI/UX design
- Testing & deployment setup
- Post-launch support (30 days)
get in touch
Ready to transform your idea into reality? Let's make it happen in 15 days!
