AI · Scoring · Telegram
Homebuyers get 200+ listing alerts a day. Most are noise. I built an AI that reads a buyer's real priorities and delivers the 3–5 homes that actually matter — with the reasoning visible.
The Problem
Zillow and Redfin are built to surface everything. That's great for the platforms — more alerts means more engagement — but it's the wrong product for a buyer who's already overwhelmed. The signal is buried in the noise.
Real estate agents solve this with judgment. But agent judgment isn't available at 7am, isn't personalised to your exact criteria, and isn't scalable. I wanted to see if AI could replicate that filtering instinct — and be transparent enough that buyers would actually trust it.
Users & What I Learned
Wants to open one message in the morning and know exactly which 3–5 homes to look at. Not a ranked list of 50. Not a spreadsheet. A confident recommendation they can act on.
Test buyers override a black-box ranking. They need to see why a home scored 85 vs 45 — which factors drove it, which deal-breaker penalised it. Without transparency, the system gets ignored.
This insight shaped the entire algorithm design. Transparent scoring with visible factor weights wasn't a nice-to-have — it was the thing that made the product usable. A buyer who understands the score acts on it. A buyer who doesn't, doesn't.
Decisions I Made and Why
User research showed buyers stretch on budget before they compromise on a must-have. Weighting must-haves above price reflects how buyers actually make decisions — not how platforms model preferences.
Email digests get archived unread. Telegram gets opened. Channel choice is a product decision — the best ranking algorithm fails if nobody sees the output. I tested both and measured open rates.
Hard exclusions remove homes buyers might want to see with caveats. A −5% penalty per deal-breaker keeps them visible while clearly signalling the tradeoff — and buyers can make the final call.
Outcomes
The Scoring Algorithm
5-Agent Processing Pipeline
Behind every ranking is an AI pipeline that fetches listings, normalises data, scores homes intelligently, and formats the digest for delivery.
Try the Demo
Fill in your profile to see how the algorithm ranks Bay Area homes. All data is dummy data for demonstration.
What This Demonstrates
Not guessing. Transparent weighting: budget 15%, location 15%, must-haves 25%. Every factor has a reason tied to user needs.
Tested with real buyers. 2 test profiles with different budgets and priorities show the system works across personas.
Users see exactly why a home scored 85 vs 45. Builds trust. Shows you think about user confidence, not just accuracy.