Fort Mason

Fort Mason needed to turn raw Zillow listing data into a structured format that could support complex real estate analysis. The source data was messy, the use cases were specific, and the team needed a repeatable pipeline rather than a one-off spreadsheet.

The challenge was not just scraping listings. It was normalizing the data so it could be filtered, compared, and acted on without manual cleanup every time the source refreshed.

I built a custom n8n workflow that pulls data via Apify, transforms it through a series of structured steps, and lands it in Google Sheets in a clean, queryable format. The pipeline handles ingestion, normalization, deduplication, and formatting in one automated flow.

The output is not a dump of listings. It is organized data the team can actually use: sortable, filterable, and ready for the analysis that drives their decisions.