Realease

First-time homebuyers are anxious. Finding a home brought stress to many. Today, it's making people tense. Realease is the first home-buying platform built to replace anxiety with clarity.

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Inspiration

The idea for Realease was born from watching friends and family spiral into “analysis paralysis” during their first home search. Young professionals, growing families, and those preparing to start a family are being crushed under the pressure of finding the “perfect” home. What should feel exciting and hopeful instead feels overwhelming and paralyzing.

Buying a home can cause tremendous anxiety. Buyers aren’t scared of houses. They’re scared of making a mistake.

Today’s real estate apps are built to show listings. Filters. Prices. Photos. Square footage. But almost none help buyers process the emotional and financial weight of the decision. We kept asking ourselves: How can buyers feel confident a home is truly a good financial decision? That question led us to build a “Fiduciary Friend” - an AI that doesn’t just surface properties, but stands beside you through the most expensive and stressful decision of your life. Realease isn’t another listing platform. It’s a confidence engine designed to turn uncertainty into clarity, and fear into informed action. It's Realease.

What it does

  • Memory-First Chatbot: A 24/7 assistant that remembers your past concerns and provides context for every recommendation.
  • Match-Grade AI: A recommendation engine that learns your lifestyle "vibes" and matches them with hard data.
  • The Confidence Dashboard: Breaks the chaotic buying process into a linear, manageable journey.
  • Predictive Analytics: Uses a simplified Monte Carlo simulation to show 1, 5, and 10-year value forecasts, giving users a "Financial Stability Score."
  • Valuation Analysis: shows whether a house is over/undervalued in a given area.

How we built it

Tech Stack

Frontend

  • React+Typescript

API

  • Python, FastAPI, SQLAlchemy
  • NeonDB SQL database

Auth

  • Clerk (google/email login)

Services

  • RapidAPI for zillow listings
  • HuggingFace AI model

AI

  • pandas, numpy
  • Monte Carlo Simulations

Deployment

  • Vercel for hosting both backend and frontend

Challenges we ran into

  • Connecting the login to the database, storing personalization, and retreiving that data as context for the AI assistant was very painful

Accomplishments that we're proud of

  • Solving the problems in aforementioned challenges
  • Custom LLM can accurately predict a home's future valuations
  • Successfully created a context-aware AI assistant with persistent data storage

What we learned

We learned that transparency is the antidote to anxiety. Users don't just want a "Yes" or "No" from an AI; they want to see the Why. Building the "Explainability" feature taught us that first-time buyers are highly capable of making big decisions if they are given the right tools in plain English.

What's next for Realease

  • The next step is the "Document Demystifier." we plan to implement a feature where users can upload their specific inspection reports or loan estimates, and Realease will highlight red flags or hidden costs in real-time. We also want to integrate local zoning data to alert buyers if that "quiet backyard" is scheduled to become a construction zone in two years.
  • Collaborative Filtering so that users who have similar preferences may get more accurate personalization

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