Nest Seeker

🚀 Inspiration

Searching for a rental home is often overwhelming—users spend hours tweaking filters and scanning irrelevant listings. We wanted a smarter, more human-centric experience. What if you could just say what you’re looking for and instantly get homes that match your needs?

💡 What it does

Nest Seeker is an intelligent rental assistant that eliminates filter fatigue. Users simply type or speak their preferences (e.g., “a quiet 2-bedroom near downtown with lots of sunlight and pet-friendly”), and Nest Seeker ranks the most relevant listings—categorized and highlighted by match quality. It personalizes search without the need for filters or forms.

How it works

Natural Language Processing: The processUserQuery function in your rental service intelligently parses unstructured user input to identify:

Budget ranges ($1200-1800)
Pet preferences (dog, cat, both, none)
Amenity needs (pool, gym, coffee shops)
Location context (work proximity, ZIP preferences)

⚠️ Challenges we ran into

Mapping unstructured user input to structured listing filters dynamically
Ensuring ranking was relevant and not overly generic
Balancing lightweight UX with rich search results
Handling edge cases in vague user queries

🏆 Accomplishments that we're proud of

Delivered a filter-free rental search experience using natural language
Successfully integrated LLM to parse and rank rental listings
Made the search experience more human, accessible, and intuitive

📚 What we learned

Multimodal LLMs can be powerful for personalization but require careful constraint
Simplicity in UX can drive better adoption than complex filtering systems
Users think in goals, not filters—LLMs help bridge that gap

🔮 What's next for Nest Seeker