Case study

AI Personalization Reshapes Real Estate in India

About Client

A leading real estate platform in India, the company with over 1 million property listings across 25+ major metropolitan areas and tier-2 cities.
It connects over 10,000 real estate agents with over 500,000 active buyers and sellers, enabling seamless property transactions.
With its digital-first approach, the platform has achieved a 300% increase in user engagement over the past decade, establishing itself as one of India’s most trusted property marketplaces.

Problem STATEMENT

When we first sat down for discussions with the Indian client, their leadership team shared the growing pains of running a large-scale real estate platform. While traffic was high, conversion and customer experience were falling short.

Too Many Choices, Not Enough Guidance
With thousands of property listings available, users felt overwhelmed. The platform lacked intelligent filters or personalized suggestions, making it difficult for buyers to quickly find what suited them. This led to frustration and longer decision-making times.
High Drop-Off Rates
An alarming 70% of users left the platform without booking a site visit. There was no system in place to re-engage interested buyers or nudge them toward the next step. As a result, many warm leads turned cold, affecting overall sales performance.
Customer Support Overloaded
Agents were inundated with repetitive queries, many of which could have been automated or addressed earlier in the user journey. With wait times often exceeding 4 hours, genuine buyers faced delays in getting meaningful assistance, hampering their buying experience.

Solution

Our developers, data engineers, and AI specialists collaborated closely with the client to design and implement a comprehensive AI solution that would revamp the entire customer engagement experience.

AI-Powered Smart Property Search & Recommendation Engine

We deployed machine learning models to analyze user search patterns, preferences, and past interactions. The system delivered personalized property recommendations in real time.

Dynamic filtering and ranking algorithms ensured users were shown the most relevant listings based on location, budget, and behavior, making property discovery significantly faster and more intuitive.

AI-Powered Chatbot

An intelligent chatbot was integrated to offer 24/7 customer assistance. It guided users through tailored property searches, answered common and complex queries about listings, neighborhoods, and pricing, and managed appointment scheduling and follow-ups.

This reduced pressure on support agents and ensured immediate help was always available.

Technical Implementation

Machine Learning & AI Models

Built a deep learning-based recommendation engine using TensorFlow, Scikit-learn, and PyTorch.
Developed NLP-based chatbots using Azure AI services and OpenAI’s GPT models.
Implemented sentiment analysis with BERT and custom neural networks to better understand user intent.

Data Processing & Integration

Aggregated user interactions, search history, and property listings from platforms like MagicBricks, 99acres, and Housing.com.
Integrated CRM data to offer a unified view of customer behavior and engagement.

AI Chatbot & Virtual Assistant

Deployed the chatbot using Azure AI Services for smooth natural language interactions.
Integrated across WhatsApp, website chat, and the mobile app for consistent multi-channel support.

Cloud Infrastructure & Scalability

Hosted the platform on AWS and Azure for high availability and real-time performance.
Used Kubernetes and a microservices architecture to efficiently manage AI workloads and ensure scalability.

Technical Architecture

Business Impact

Stronger Engagement with Property Listings
The introduction of AI-driven recommendations led to a 50% increase in property inquiry engagement, as users were presented with listings that closely matched their preferences and behavior.
Faster Customer Support, Less Human Dependency
AI-powered chatbots reduced reliance on human agents and brought down response times by 30%, ensuring users received quick answers and assistance around the clock.
Boost in Conversion Rates
With personalized property suggestions guiding buyers more effectively, the platform saw a 20% increase in conversion rates, resulting in more site engagement and successful transactions.
Improved Customer Experience
Users enjoyed a smoother journey with real-time support and relevant suggestions. This significantly reduced frustration and helped lower the drop-off rates across the platform.
Operational Efficiency at Scale
By automating responses and streamlining buyer interactions, the platform minimized repetitive agent tasks, allowing the support team to focus on high-intent buyers and strategic queries.

All in all, the integration of AI-powered recommendations and NLP-based chatbots transformed the client’s real estate platform. It made property searches faster, responses quicker, and boosted conversions, all while reducing the load on support teams. Looking ahead, the client plans to introduce multi-lingual chatbots, voice-based search, and AI mortgage support to further enhance user experience and business growth.

Industry

Real Estate & Construction

Services Used

Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), Power BI, Tableau

Region

India

Function/Department

Sales and Business Development

Engagement Model

End to End Project Lifecycle Management

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Ankush

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By Use Case

AI Personalization Reshapes Real Estate in India

About Client

Problem STATEMENT

Solution

Technical Implementation

Technical Architecture

Business Impact

Industry

Services Used

Region

Function/Department

Engagement Model

Drop Your Business Concern

Ankush

Services

Data Engineering

AI & Machine Learning

Business Intelligence

Use Cases

Products

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