Real Estate Buyer AI Assistant - Part 1

Challenge:

Real estate agencies face unprecedented challenges in today's competitive market. Home buyers are overwhelmed by the complexity of the buying journey, leading to longer sales cycles and decision fatigue. Traditional manual processes for property research, document review, and financial comparisons are time-intensive and prone to errors. 

Modern buyers expect instant responses, personalized property recommendations, and seamless digital experiences. However, most agencies still rely on traditional methods that can't scale to meet these expectations. This creates a significant opportunity for agencies that can leverage AI to differentiate themselves in the market

Solution:

Our real estate AI assistant leverages the AWS Squad framework - a sophisticated multi-agent orchestration system that coordinates specialized AI agents to handle complex, multi-step workflows. This architecture ensures that each aspect of the home-buying journey is handled by a specialized agent optimized for specific tasks.

Core Architecture Components

The solution utilizes Amazon Nova models, AWS's latest generation of foundation models that deliver frontier intelligence with industry-leading price performance. Each Nova model serves a specific purpose:

• Amazon Nova Micro: Handles fast, text-only tasks like classification and routing with minimal latency
• Amazon Nova Lite: Processes multimodal inputs (text, images, video) for property descriptions and quick analysis
• Amazon Nova Pro: Powers complex reasoning tasks, document analysis, and sophisticated property matching

Multi-Agent System Design

The AWS Squad framework enables our system to employ multiple specialized agents working in coordination. Rather than relying on a single monolithic AI system, we create a distributed architecture where:

1. A supervisor agent analyzes incoming requests and routes them to appropriate specialist agents
2. Specialized agents handle specific domains like property search, document analysis, or financial calculations
3. Context management ensures seamless handoffs between agents while maintaining conversation history

This approach significantly improves task success rates and accuracy compared to single-agent systems.

Real Estate Buyer AI Assistant - Architecture Diagram

Solution Details:

The solution primarily uses AWS Agent Squad Multi-Agent framework - for classifying, orchestrating multiple AI agents

Property Knowledge Base Retriever Agent

This agent handles natural language property searches by leveraging Amazon Bedrock Knowledge Bases integrated with Amazon OpenSearch Serverless. The system:

• Stores property descriptions as vector embeddings in OpenSearch
• Uses Nova Pro for semantic matching between user queries and property data
• Retrieves relevant properties based on natural language descriptions rather than rigid filters

For example, a query like "cozy 3-bedroom home near good schools under $500k" gets processed semantically to find properties that match the intent, not just the exact keywords.

Property Search Agent

Built using Amazon Bedrock Agents with Lambda Action Groups, this agent converts user queries about specific properties into structured database interactions. It handles requests for:

• Neighborhood details and demographics
• Interior and exterior property features
• Sales history and tax information
• Comparative market analysis

The agent uses Amazon DynamoDB for efficient property data storage and retrieval, leveraging DynamoDB's single-table design patterns for optimal performance.

Property Calculator Agent

This specialized agent handles complex financial calculations using the Model Context Protocol (MCP). It provides:

• Mortgage calculations with current interest rates
• Affordability assessments based on buyer financial profiles
• Buyability analysis considering down payment and closing costs

The agent uses Nova Pro to interpret user queries and convert them into appropriate calculator parameters, ensuring accurate financial guidance.

DynamoDB for Property Data

Our system uses Amazon DynamoDB with a single-table design pattern to store property information efficiently. This approach provides:

• Scalability: Handle millions of property records with consistent performance
• Cost-effectiveness: Minimize storage costs through efficient data modeling
• Fast access: Sub-millisecond response times for property lookups

Property data is structured with composite keys enabling efficient queries by location, price range, and property characteristics.

Vector Embeddings with OpenSearch

Amazon OpenSearch Serverless serves as our vector database for semantic property search. The system:

• Generates embeddings for property descriptions using Amazon Titan Text Embeddings
• Stores vector representations alongside metadata for hybrid search capabilities
• Enables semantic similarity matching for natural language queries

This combination of traditional keyword search and semantic vector search provides superior results compared to either approach alone.

S3 for Document Storage

Property documents, inspection reports, and images are stored in Amazon S3 with appropriate security controls. This provides:

• Unlimited scalability for document storage
• Cost-effective long-term archival
• Integration with processing services for document analysis

Model Selection Strategy

The choice of Nova models is crucial for balancing performance, cost, and accuracy across different use cases:

Nova Micro for Classification Tasks

• Use case: Quick routing decisions and simple text extraction
• Benefits: Lowest latency (200 tokens/second) and minimal cost
• Example: Extracting property IDs from user queries

Nova Lite for Multimodal Processing

• Use case: Image analysis and quick property descriptions
• Benefits: Fast multimodal processing at low cost
• Example: Generating property descriptions from listing photos

Nova Pro for Complex Reasoning

• Use case: Document analysis, contract comparison, financial calculations
• Benefits: Best balance of accuracy, speed, and cost for sophisticated tasks
• Example: Analyzing mortgage contracts to identify buyer-friendly terms

Architecture Explanation:

The architecture is explained in detail in the below video that demonstrates the use of various AWS services, tools, and open source Agentic AI framework with AWS Agent Squad.

Real Estate Buyer AI Assistant Architecture

References:

• AWS Agent Squad - https://github.com/awslabs/agent-squad
• AWS Nova - https://aws.amazon.com/ai/generative-ai/nova/
• AWS Bedrock - https://aws.amazon.com/bedrock/