GoodbyeMama Roommate Matching Platform

Designed and developed a scalable MVP for a roommate matching platform focused on lifestyle compatibility rather than traditional listing-based discovery. The objective was to improve shared living outcomes by prioritizing human compatibility, reducing friction in roommate selection, and enabling faster, more informed decisions.

The product was built as a full web MVP using Bubble, designed to handle structured user data, matching logic, and real-time communication in a unified system.

Most roommate platforms rely heavily on property listings instead of human compatibility. This leads to:

Poor lifestyle matches

High user churn after initial connection

Lack of structured decision-making in shared living

The core challenge was to design a system that shifts focus from “finding a place” to finding the right person to live with.

We built a structured MVP system that combines behavioral data, matching logic, and communication into a single flow:

Guided onboarding with lifestyle and preference profiling

Compatibility-based matching system instead of random discovery

Structured user profiles with behavioral attributes

Integrated chat system for matched users

Unified interface combining matches, listings, and communication

The system was engineered to ensure smooth data flow between user profiles, matching logic, and interaction layers.

Built and delivered MVP in ~28 days

Designed compatibility-driven matching architecture

Structured database logic for scalable user profiling and filtering

Implemented real-time interaction flow between matched users

Optimized onboarding flow to improve profile completion and reduce drop-off

The entire system was built to be modular, allowing future expansion into advanced AI-based matching and recommendation layers.

Created a structured compatibility-first roommate discovery system

Improved match relevance by prioritizing lifestyle alignment over listings

Reduced friction in decision-making through unified matching + chat flow

Built a scalable MVP architecture ready for future feature expansion (AI scoring, advanced filters, recommendation engine)

This project demonstrates how shifting from listing-based systems to behavior-driven matching architecture can fundamentally improve user outcomes in shared living platforms, while maintaining scalability and performance from day one.