Case Study - AI-powered home search that understands you
Homi uses AI to help people find their next home by understanding preferences, location needs, and lifestyle requirements — making home search feel personal and effortless.
- Client
- Homi
- Year
- Service
- Product Development
Overview
The home search experience has been broken for years. People spend hours scrolling through listings, applying endless filters, and still miss homes that would be perfect for them. Homi was built to solve this problem with AI that actually understands what you're looking for.
Instead of traditional search filters, Homi uses conversational AI to learn about your preferences, budget, location requirements, and lifestyle needs. The system analyzes listings across multiple sources and presents homes that genuinely match — not just on paper, but in the ways that matter for how you'll actually live there.
The platform launched with immediate traction: 250+ organic signups validated the problem, broker partnerships provided access to premium inventory, and early revenue proved the business model. The technology stack — Next.js, TypeScript, and tRPC — enabled rapid iteration while maintaining code quality and type safety.
The next phase involves expanding data sources, deepening AI capabilities to understand neighborhood dynamics, and building features for the entire home search journey from discovery to closing.
What I built
- Next.js
- TypeScript
- tRPC
- AI Integration
- Product Strategy
Building Homi was about creating a fundamentally better way to search for homes. The AI doesn't just filter listings — it understands context, preferences, and what makes a place feel like home.
Founder of Homi
- Organic signups
- 250+
- Broker pilots
- 3
- First MRR
- $2K+
- Avg. search time
- 4.2s