Finding the right property is rarely as simple as it should be. Whether someone is searching for a three-bedroom apartment with a balcony in a quiet neighbourhood or a modern office space near public transport, the process is often frustrating. Search filters don’t always deliver relevant results, listings lack key details, and buyers are left sorting through pages of properties that don’t quite fit.

On the agency side, it’s not any easier. Real estate agents spend hours manually searching for matches, responding to endless client inquiries, and trying to keep up with shifting preferences. It’s a time-consuming, error-prone process that slows down deal-making and leaves clients waiting longer than necessary.

Softblues saw a clear problem here—and built a solution. Their Real Estate AI Agent takes property discovery to another level. Instead of relying on static search filters and manual matching, this system understands what buyers are looking for, refines results based on interactions, and continuously improves recommendations using AI-driven logic.

By combining large language models (LLMs), vector databases, and structured real estate data, the AI agent interprets natural language searches, finds the most relevant listings, and even asks follow-up questions to refine results. It’s not just about showing properties—it’s about understanding what buyers really want and delivering better, smarter, and faster recommendations.

In this blog, we’ll break down how the agent works and its underlying architecture. Finally, we’ll let you know how you can build a similar agent using open source architecture. 

How It Works: AI-Powered Property Discovery and Personalized Search

The AI Real Estate Recommendation System acts like a real estate assistant that understands buyer preferences, refines recommendations in real time, and continuously learns from user interactions. It works through two key modules:

1. Smart Property Discovery Module

The Smart Property Discovery Module ensures that users get relevant property matches without relying on rigid search filters. Instead of forcing users to manually tweak criteria, the system interprets natural language inputs and dynamically refines search results based on intent.

For example, if a user types:
“Please find me a 3-bedroom flat in Camden, ideally with a garden and close to schools”
the system instantly:

• Extracts key parameters such as location, number of bedrooms, and special features.
• Retrieves the five best-matching properties, providing detailed descriptions that highlight factors like proximity to schools and garden size.
• Generates follow-up questions to refine results—such as budget, parking needs, or neighbourhood preferences.
• Adapts suggestions based on responses, offering alternative options that might provide better value (e.g., suggesting similar flats in nearby Chalk Farm).

This module doesn’t just return static listings—it engages users in a dynamic, interactive property search that evolves with their needs.

2. Property Q&A Widget

Once a user clicks on a property, the Property Q&A Widget takes over, providing deeper insights and allowing for more refined searches. Instead of leaving users to research details on their own, the system pre-generates context-aware questions based on the search query, such as:

• What are the local school ratings?
• How large is the garden?
• What’s the energy efficiency rating?
• Are there any planned developments nearby?

Beyond these predefined insights, the system offers an interactive chat where users can ask specific questions—whether it’s about loft conversion potential, council tax, or property comparisons.

Additionally, the system enables instant property comparisons, helping buyers evaluate differences in size, price, and amenities side by side. Over time, it continuously learns from each interaction, refining future property suggestions and maintaining context across the entire search journey.

Together, these two modules create a highly personalized and interactive real estate search experience, saving users time while ensuring they receive accurate, well-matched recommendations. 

Now, let’s talk a bit about the technology and architecture behind this system, breaking down how it processes and optimizes search data in real-time.

The Architecture Behind the Agent

1. ETL Pipeline: Cleaning and Structuring Property Data

At the foundation of the system is an ETL (Extract, Transform, Load) pipeline that ensures raw real estate data is structured, validated, and optimized for downstream AI processing.

Key Stages of Data Processing:

• Raw Data Extraction: Pulls real estate listings, metadata, and agency details from multiple sources (databases, listing platforms, external APIs).
• Data Preprocessing: Standardizes formats, normalizes attributes like location, price, and square footage, and removes inconsistencies.
• Data Validation: Cross-verifies property details against multiple sources, ensuring data accuracy and filtering out duplicates.
• Structured Data Storage: Saves processed listings into the system’s specialized multi-database infrastructure, making it query-ready for AI-powered searches.

By structuring data at ingestion, the system ensures that search and recommendation models work with clean, normalized information, eliminating inconsistencies that often plague real estate listings.

2. Multi-Database Architecture: Powering High-Performance Queries

The AI assistant relies on a multi-layered database infrastructure optimized for both structured SQL-based lookups and vectorized semantic searches.

Key Database Components:

• Properties Database: Stores structured property data, including attributes like price, location, and features.
• Vector Database: Enables semantic similarity matching by encoding property details into embeddings, allowing for more intuitive searches beyond exact keyword matches.
• Metadata Repositories:
  • Properties Metadata: Categorizes real estate attributes for structured filtering.
  • Agency Metadata: Stores agency-level policies, FAQs, and business rules.
  • User Context Data: Captures session history to enable personalized recommendations based on prior searches.

The vector database is a critical addition, as it allows the system to retrieve properties not just by filters but by meaning—ensuring that listings with similar characteristics surface even if users don’t phrase their queries exactly.

3. Context Manager: Orchestrating AI Agents and User Interactions

A key challenge in AI-driven property discovery is maintaining session context across searches and interactions. The Context Manager acts as the central orchestrator, ensuring:

• Persistent user session data: Tracks previous queries and refinements to deliver contextually aware recommendations.
• Dynamic preference adaptation: Learns from interaction patterns to refine search results over time.
• Real-time synchronization across AI agents: Ensures that all agents operate with a unified dataset and session history.

For example, if a user repeatedly searches for “modern apartments with open kitchens,” the system prioritizes listings with those features, even if the user doesn’t specify them explicitly in every query.

4. AI Agent Network: Task-Specific AI Modules Working in Parallel

Softblues’ system employs a modular AI agent architecture, with each agent handling a specific function within the recommendation workflow.

Core AI Agents and Their Functions:

• Recommendation Agent:
  • Uses LLM-powered SQL query generation to fetch the most relevant property listings.
  • Interacts with both structured SQL databases and vectorized property embeddings for hybrid search capabilities.
• Q&A Agent:
  • Interprets natural language queries related to property details.
  • Provides answers to location-based and feature-specific questions (e.g., “What are the nearby schools?”).
• Alternatives Agent:
  • Identifies and suggests similar properties based on embeddings and user search behavior.
  • Uses vector similarity scoring to rank listings with the highest semantic relevance.
• General Info Agent:
  • Handles agency-level inquiries (e.g., policies, legal requirements, contract details).
  • Serves as a structured data retrieval interface for non-property-specific questions.

This multi-agent architecture enables parallel processing, where different AI components handle distinct aspects of the search experience simultaneously—ensuring that users receive fast, context-aware, and accurate property recommendations.

Bringing It All Together: How the System Operates

1. User Inputs Query → Context Manager retrieves past interactions to refine request.
2. Recommendation Agent Generates SQL Query → Fetches structured property data.
3. Vector Search Executes in Parallel → Finds semantically similar properties.
4. Alternatives Agent Expands the Search → Suggests relevant listings beyond the initial result set.
5. Q&A Agent Provides Contextual Answers → Handles detailed user inquiries about listings.
6. Final Results Are Enriched and Personalized → Delivered via chatbot or UI interface.

This workflow ensures real-time, intelligent property discovery, reducing manual filtering while enhancing accuracy and personalization.

Building Similar AI Agents: The Hard Way vs. The Smarter Way

Looking at the architecture behind Softblues’ real estate recommendation system, it’s clear that developing a robust AI agent isn’t a weekend project. From integrating multiple databases to orchestrating AI agents and ensuring real-time responses, the process requires weeks or even months of development, infrastructure setup, and testing. And that’s before considering scalability, security, and data synchronization.

But here’s the thing: you don’t have to start from scratch.

If you need a ready-made solution, you can simply get this same agent from our upcoming marketplace—pre-configured and optimized for various use cases; these agents are fully customizable and can be deployed on your own cloud or on-prem infrastructure in just a few clicks. Or just contact our partners from Softblues.

But if you want to build a custom AI agent similar to the one that we explained in our blog, we can provide you with all the building blocks needed to develop, test, and deploy AI-powered solutions without the usual headaches. With built-in integrations for open-source tools like databases, vector stores, event engines, and logging tools, you can focus on perfecting the AI logic rather than managing infrastructure.

Your AI, Your Data, Your Control

Unlike other AI platforms, Qubinets ensures that your data stays fully under your control, which means that everything is deployed on your infrastructure of choice, without third-party access, making it an ideal solution for businesses handling sensitive customer and transaction data.

Whether you need a plug-and-play AI agent or want to build and deploy a fully custom solution, Qubinets provides the tools to get there faster, with less complexity, and complete ownership over your AI infrastructure.