case study
We built an AI-powered property solution connecting investors and brokers like never before. Investors use an intelligent assistant to find investment-grade properties, while brokers receive qualified, scored leads for consultations.
TECHNOLOGY STACK
NEXT.JS
SHADCN UI
Our client operates in the Dubai real estate investment market and wanted to leverage AI to transform property discovery. Traditional platforms force investors to scroll endlessly and manually filter listings, making it hard to identify high-potential opportunities. Brokers face low-quality leads and limited insight into real buyer intent.
The client’s vision was a conversational, data-driven platform that captures investor goals and connects brokers with qualified prospects — all built on their existing property data infrastructure.
We started with a comprehensive discovery phase to validate the product concept and align business goals with technical execution. This included detailed UI design for both investors and brokers, detailed user flows, and a full Business Requirements Document (BRD), preliminary technology stack and required team with seniority.
Based on the approved scope, we designed a scalable cloud architecture and AI orchestration flows, then delivered the MVP using an agile, sprint-based process. The platform was built with production-ready infrastructure, integrated AI assistant logic, payment flows, and third-party data sources.
Quality assurance, performance testing, and AI conversation validation were embedded throughout development, ensuring the MVP was stable, secure, and ready for real users. Alongside launch, we prepared a post-MVP roadmap to support future growth and feature expansion.
To ensure the product solved real business and user problems, the project began with a structured discovery phase that aligned investment goals, broker workflows, AI capabilities, and technical constraints before development. This prepared the team for MVP delivery with a validated scope, clear priorities, and minimal delivery risk.
The discovery phase focused on turning the product vision into a concrete, build-ready plan. Key outcomes included:
User-Centric Prototyping & Design
High-fidelity UX prototypes, interactive flows, and UI designs for investors and brokers, ensuring intuitive, goal-oriented experiences.
Business & AI Logic Validation
Detailed Business Requirements Document (BRD), AI interaction scenarios, and lead qualification rules confirmed to meet business goals.
Scalable System Architecture
Cloud architecture and AI orchestration flows designed to support the conversational assistant, secure data handling, and future feature expansion.
Monetization & Operational Flows
Verified lead monetization and secure transaction flows aligned with operational requirements.
The AI assistant captures investor goals through conversation and queries the client’s property database via APIs to surface relevant properties. Investors can save properties or request meetings, becoming qualified leads. These leads are scored and enriched with context before reaching brokers, shortening sales cycles and improving deal conversion.
Investor Interface & Dashboard
A personalized environment where investors interact with the AI assistant, review matched properties, and refine investment criteria through conversation rather than static filters.
Conversational AI Assistant
An intelligent co-pilot that captures intent, asks clarifying questions, and delivers investment-grade property recommendations based on user goals and market data.
Lead Scoring & Investor Profiling Engine
Behavioral and conversational data is analyzed to qualify and rank leads, giving brokers structured insights instead of raw inquiries.
Broker Portal & Lead Management
A dedicated workspace where brokers access verified leads, review investor profiles, and manage subscriptions and lead acquisition.
Admin & Management Tools
Internal tools for managing users, data sources, AI configurations, and operational workflows.
"Technically, the agent is built as a structured workflow with conditional routing and tool usage. In practice, this means it makes decisions: when to query the knowledge base, when to update memory, when to ask a clarifying question, and when to generate recommendations. This approach combines the intelligence of the model with system-level controllability, which is critical for a production-grade solution," says Bohdan, AI engineer.
The agent doesn’t simply process isolated requests — it operates with long-term memory, storing and carefully updating user preferences while analyzing them over time. The memory is not a “blind archive.” It is structured, deduplicated, and used as an initial hypothesis for recommendations, but always refined through the current interaction. This enables the system to generate real estate recommendations tailored to a specific client, rather than to an average user profile.
A strong emphasis was placed on ensuring that the agent proactively initiates meaningful follow-up questions and works through interactive widgets directly within the chat. Instead of merely asking, “What are you looking for?”, the system immediately suggests relevant options, parameters, and scenarios.
The platform leverages a dual-model approach to balance speed, accuracy, and efficiency in production. The primary model, gpt-4.1-mini, powers the conversational assistant, handling reasoning, dialogue, and tool execution with stable, real-time responses. A lightweight auxiliary model, gpt-4o-mini, performs keyword extraction for the retrieval-augmented generation (RAG) pipeline, preprocessing user input for faster, more precise context retrieval.
This separation ensures the system efficiently allocates resources: the main assistant focuses on strategic guidance while the auxiliary model handles structured preprocessing.
Reliable, high-quality responses
0.50–0.70 similarity for general queries, 0.910+ for exact facts
Structured lead generation
Conversational interactions feed directly into lead scoring for brokers
Tool-driven workflow
Search, property fetch, and profile enrichment are automated, shortening decision cycles
Consistent, professional tone
Every interaction maintains the style of a knowledgeable, confident advisor
In my view, the most compelling aspect of this architecture is the balance between autonomy and predictability. The agent behaves like a consultant rather than a filter-based search form. It analyzes context, adapts to the user, and proposes options, while maintaining transparent, scalable logic that supports future evolution and expansion.
Bohdan Oliinyk
AI Engineer
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