Vdoma AI Startup
About
A startup built from the ground up to simplify apartment search and rental with AI-powered search, personalized recommendations, and automated landlord assistance.
Problem
Existing rental platforms are overloaded with outdated listings, poor search experience, and slow communication between tenants and landlords.
Finding a rental home has become frustrating and time-consuming
The rental market is crowded with fake listings, outdated information, and fragmented platforms. Users spend hours filtering irrelevant properties, contacting landlords manually, and comparing dozens of listings without confidence that the information is accurate.
At the same time, landlords struggle with repetitive conversations, answering the same questions repeatedly, and managing multiple inquiries across different platforms. Existing rental apps rarely leverage AI to simplify either side of the experience.
The goal of VDOMA AI was to create a startup that reimagines the apartment rental journey by combining verified listings, AI-powered search, personalized recommendations, and intelligent landlord assistance into a single mobile experience.
An AI-powered rental platform designed around people, not listings
Instead of relying on traditional filters alone, users can simply describe the home they're looking for in natural language. AI analyzes the request, recommends the most relevant properties, explains why each listing matches, and continuously improves recommendations based on user preferences.
For landlords, AI automates repetitive communication by answering common questions, assisting with listing management, and helping attract better-qualified tenants.
By combining verified listings, conversational AI, personalized matching, and an intuitive interface, VDOMA AI transforms apartment hunting into a faster, more transparent, and significantly more enjoyable experience.
Key insights from market and competitor research
During the discovery phase, I analyzed the rental market, user behavior, and competing platforms. This research uncovered several key challenges that became the foundation of the VDOMA AI concept.
Finding a home is still a slow and unreliable process:
- Users frequently encounter fake, duplicate, or outdated listings.
- Most rental platforms require users to spend a significant amount of time browsing irrelevant properties and manually adjusting filters.
- At the same time, landlords often struggle to showcase their properties effectively and connect with the right tenants.
AI-driven approach to property search
After researching the competitive landscape, I identified an opportunity to build a product that goes beyond traditional filtering.
Instead of relying solely on manual filters, VDOMA AI combines AI-powered search, structured filters, and listing content analysis to better understand user intent and recommend the most relevant properties.
For landlords, the platform also leverages AI to simplify listing creation by automatically generating optimized titles, descriptions, and market-based price suggestions. Additionally, landlords can create custom property filters that improve matching accuracy with users' search requests.
By combining verified profiles, verified listings, AI-powered recommendations, and intelligent search, VDOMA AI delivers a faster, safer, and more personalized rental experience for both tenants and landlords.
AI-Powered Search
- Search using natural language instead of relying only on traditional filters.
- AI analyzes both your preferences and keywords from property descriptions to find the most relevant apartments.
- Every recommendation combines filters, listing content, and semantic matching for more accurate results.
- Reduces the time spent browsing irrelevant listings.
Smart Property Matching
- Every listing receives an AI Match Score based on user preferences.
- AI explains why a property is recommended.
- Users can instantly compare apartments by relevance.
Interactive Map Search
- Explore listings directly on the map.
- Draw and save custom search areas.
- Receive notifications when new properties appear inside saved zones.
AI Landlord Assistant
- AI automatically answers common tenant questions.
- Provides instant responses even when the landlord is busy.
- Reduces repetitive communication.
Verified Profiles
- Verify tenant and landlord accounts using official identity verification.
- Verified badges help users identify trusted profiles.
- Creates a safer and more transparent rental experience.
AI-Assisted Listing Creation
- Add custom filters to describe unique property features and improve AI matching.
- AI automatically generates the listing title and description based on the property's location, amenities, and apartment type.
- Get AI-powered rental price suggestions using market data from the selected area.
- Better listing quality leads to more accurate recommendations and higher visibility in AI search.
How can AI make apartment search faster and more personalized?
To better understand the apartment rental journey, I analyzed competing platforms, explored the PropTech market, reviewed user feedback, and studied current industry trends. The goal was to identify opportunities where AI could simplify apartment discovery and improve the experience for both tenants and landlords.
Research Questions
- What are the biggest frustrations during apartment search?
- Why do users spend so much time browsing listings?
- Which features are missing from today's rental platforms?
- How can AI deliver more relevant recommendations?
- How can landlords create better listings with less effort?
Main Themes and Insights
After analyzing competitors, market research, and user feedback, several recurring patterns emerged. These insights became the foundation of the VDOMA AI product concept.
Apartment Search
Users spend too much time browsing irrelevant listings. Even after applying filters, people still scroll through dozens of apartments that only partially match what they're looking for.
Key Insight: Filters alone are not enough. The platform should understand the context behind a user's request and prioritize the most relevant properties.
"I don't want to browse hundreds of apartments when only a few actually fit my needs."
AI Recommendations
People describe what they want using natural language. Users naturally search with phrases like "bright apartment near the metro with a modern interior" instead of relying only on filters.
Key Insight: AI should analyze structured filters, natural language queries, and property descriptions simultaneously to deliver the most accurate matches.
"I just want to describe the apartment I'm looking for instead of filling in dozens of filters."
Personalization
Every renter has different priorities. Some users prioritize price, while others care more about location, neighborhood, or interior style.
Key Insight: Recommendations should adapt to individual priorities instead of treating every search the same.
"Being close to the metro is more important to me than having a larger apartment."
Listing Creation
Creating high-quality listings requires time and experience. Many landlords struggle to write compelling titles, detailed descriptions, and competitive pricing.
Key Insight: AI can automatically generate listing titles, descriptions, and suggested pricing based on the neighborhood, apartment type, and property details. Landlords can also create custom filters, helping their listings match user searches more accurately.
"If AI helps me create a better listing, it will reach the right renters faster."
Designing a marketplace that works for both renters and landlords
Most rental platforms focus almost entirely on the renter's experience. During research, I realized that landlords face just as many challenges: creating listings, responding to repetitive questions, pricing properties, and managing multiple apartments.
The product needed to create value for both sides of the marketplace instead of solving problems for only one audience.
Marketplace Analysis
Balancing the needs of two different user groups
Instead of optimizing only one user journey, I mapped both sides of the marketplace.
Tenant Goals:
- Find relevant apartments faster
- Spend less time browsing
- Receive personalized recommendations
- Save interesting areas
- Compare properties easier
Landlord Goals:
- Publish listings quickly
- Receive qualified inquiries
- Reduce repetitive communication
- Improve listing quality
- Manage multiple properties
Key product decisions shaped by research
Insights gathered from competitor analysis, user research, and landlord interviews influenced several core product decisions. Rather than adding AI for the sake of innovation, each feature was designed to solve a specific user problem identified during discovery.
AI Search instead of More Filters
Challenge
Users described apartments using natural language, while existing platforms relied almost entirely on filter combinations.
Decision
Allow users to search using conversational prompts while combining AI with traditional filters and listing content analysis.
Outcome
A more intuitive search experience that reduces time spent browsing irrelevant properties.
AI-Assisted Listing Creation
Challenge
Landlords spend significant time writing descriptions, choosing titles, and setting competitive prices for every listing.
Decision
Use AI to generate listing titles, descriptions, and price suggestions based on property details while allowing users to review everything before publishing.
Outcome
A faster publishing workflow without removing user control over the final listing.
Saved Areas instead of Only Favorites
Challenge
Apartment searches often focus on neighborhoods rather than individual properties, requiring users to repeatedly check the same locations.
Decision
Introduce custom map areas that users can save and monitor for new listings.
Outcome
The search becomes proactive, notifying users when relevant properties appear inside their preferred locations.
Automatic Realtor Status
Challenge
Private landlords and real estate professionals have different needs. Requiring users to choose an account type during registration adds unnecessary friction, while treating both groups the same makes it difficult to introduce professional features.
Decision
Every user starts as a private landlord. When more than one active property is published, the account is automatically upgraded to a Realtor profile, unlocking professional tools and a subscription plan designed for managing multiple listings.
Outcome
The onboarding process remains simple for individual landlords, while the platform naturally distinguishes professional users and creates a scalable monetization model based on portfolio size.
