RAG-Based Real Estate AI Platform
Realty Pulse Pro
Traditional property filtering systems struggled to provide contextual property recommendations and conversational search experiences across large and inconsistent real estate datasets.
A RAG-powered real estate intelligence platform enabling agencies and brokers to create AI property assistants using OpenAI APIs, semantic search, vector retrieval, and conversational property discovery workflows.
Project profile
Role
Full Stack & AI Engineer
Timeline
2024 - 2025
Client/Company
Talentelgia Product
Stack
Node.jsPythonFastAPIReactMongoDBRedisDockerOpenAI APIsGPT-4EmbeddingsFiasDBRAG ArchitectureLLM WorkflowsVector Search
RAG + Vector Search
AI Architecture
Conversational Property AI
Core System
FastAPI + React + MongoDB
Primary Stack
Overview
A deeper summary of the product, system, or engagement.
Realty Pulse Pro is an AI-powered real estate intelligence and property operations platform built for brokers, agencies, and property consultants. The platform enables organizations to manage property listings, broker workflows, CRM pipelines, and intelligent customer engagement through customizable AI-powered property assistants.
A major focus of the platform was building a Retrieval-Augmented Generation (RAG) based chatbot infrastructure where organizations could create dedicated projects, upload property datasets, configure AI assistants, and integrate those chatbots directly into their websites. The chatbot system allowed end users to ask natural-language real estate queries such as nearby properties, budget-based recommendations, location-specific searches, bedroom requirements, and contextual property discovery questions.
The system leveraged OpenAI APIs, GPT-based conversational workflows, vector embeddings, semantic retrieval pipelines, FastAPI services, and FiasDB vector infrastructure to deliver contextual property recommendations and intelligent conversational search experiences. Uploaded property data was transformed into searchable embeddings enabling highly relevant retrieval workflows, AI-assisted property recommendations, and natural-language conversational responses.
The platform also included CRM operations, realtime lead tracking, broker management systems, advanced property filtering, operational dashboards, and realtime customer interaction workflows. Redis-backed caching systems and optimized retrieval services improved response speed and platform scalability during concurrent user interactions.
Dockerized deployment infrastructure and modular backend services enabled scalable operational environments while supporting multi-project chatbot deployments and realtime conversational workflows.
Architecture and solution
Core summary
Designed a scalable RAG-based conversational retrieval architecture using vector embeddings, OpenAI APIs, FiasDB similarity search, FastAPI retrieval services, and Redis-backed caching to deliver intelligent natural-language property discovery workflows. React Frontend, Node.js APIs, FastAPI Retrieval Services, RAG Processing Layer, OpenAI Integration Layer, GPT Response Generation, Vector Embedding Pipeline, Redis Cache Layer, MongoDB Database, FiasDB Vector Search Engine, Realtime CRM Systems, Dockerized Infrastructure
Highlights
- Designed a scalable RAG-based conversational retrieval architecture using vector embeddings, OpenAI APIs, FiasDB similarity search, FastAPI retrieval services, and Redis-backed caching to deliver intelligent natural-language property discovery workflows.
- React Frontend, Node.js APIs, FastAPI Retrieval Services, RAG Processing Layer, OpenAI Integration Layer, GPT Response Generation, Vector Embedding Pipeline, Redis Cache Layer, MongoDB Database, FiasDB Vector Search Engine, Realtime CRM Systems, Dockerized Infrastructure
Capabilities and focus areas
The detail pages now surface the delivery depth, not just the headline summary.
Features and capabilities
- Architected scalable backend APIs for real estate operations and AI chatbot workflows.
- Designed and implemented RAG-based property chatbot systems.
- Integrated OpenAI APIs for conversational AI and contextual property recommendations.
- Built semantic retrieval pipelines for natural-language property discovery.
- Developed project-based chatbot infrastructure for organization-specific deployments.
- Implemented vector embedding and similarity-search workflows using FiasDB.
- Built FastAPI services for retrieval orchestration and conversational AI operations.
- Developed website-integrated AI chatbot workflows for customer-facing real estate interactions.
- Optimized MongoDB indexing and Redis caching for large-scale property retrieval operations.
- Built realtime lead tracking and CRM operational systems.
- Managed Dockerized deployment and scalable backend infrastructure workflows.
Engineering focus areas
- Backend architecture & APIs
- Infrastructure & deployment workflows
- Performance optimization
- Realtime WebSocket coordination
Outcomes and impact
A more complete record of what the work enabled or improved.
- Built scalable RAG-based conversational property search systems.
- Integrated OpenAI-powered conversational AI workflows for real estate businesses.
- Enabled AI-powered website chatbots for property discovery and customer engagement.
- Improved property recommendation relevance using vector similarity retrieval.
- Centralized broker operational workflows into a unified platform.
- Reduced property discovery latency through optimized retrieval pipelines.
- Implemented scalable semantic search infrastructure for conversational AI workflows.
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