applications Case Study

Apartment Scout

Automated listing pipeline that collects, filters, and ranks apartments based on real-world search criteria.

Status Active System (v1 Live) Runtime Automated listing collection and filtering workflow with tracked outputs. Role Builder / Automation Designer
Apartment Scout cover image

Overview

Apartment Scout is an automated apartment search workflow that pulls listings from multiple sources, filters them against defined criteria, and surfaces the highest-fit options in a more structured way than manually checking sites one by one.

Problem

Apartment hunting across multiple listing sites is repetitive and noisy. I wanted a workflow that could collect listings, apply practical filters like neighborhood, size, and pet-friendliness, and surface the best matches without constant manual searching.

Architecture

  • Source Layer
    • listing sites
    • scraped results
    • search inputs
  • Filtering Layer
    • fit rules
    • criteria checks
    • deduplication
  • Tracking Layer
    • structured results
    • top matches
    • status tracking
  • Output Layer
    • dashboard
    • sheet output
    • notifications
Listing sources apartments • rentals • scraped pages │ ▼ Collection pipeline scrape • normalize • deduplicate │ ▼ Fit filtering neighborhood • size • pet-friendly • layout │ ▼ Structured results tracked matches • ranked options • workflow output │ ▼ Dashboard / sheet / notifications

Public-safe workflow view for apartment scraping, filtering, and match surfacing.

Technology Stack

Technologies

scraping pipeline Node.js Google Sheets notifications workflow automation

Capabilities

  • Multi-source listing collection
  • Filter-based fit scoring
  • High-match result surfacing
  • Structured tracking workflow
  • Notification-ready outputs

Implementation

Apartment Scout is designed as an automation pipeline that collects listing data, filters it against user-defined rules, and pushes higher-fit options into a tracked review surface.

Outcome

Apartment Scout reduces search friction by surfacing better-fit listings faster and turning a repetitive manual process into a more structured workflow.

What's Next

  • More listing sources
  • Better deduplication
  • Ranking improvements
  • Cleaner review dashboard