Case Study: Expensify achieves 30–40% reduction in manual interventions and improved data quality with Constructor.io

Autosuggest dramatically improves data quality and user experience

Expensify, which powers expense reporting for thousands of companies (customers include Snapchat, the Virgin Group, Yahoo, Uber and Atlassian) processes billions in expense spending each year. Its users frequently had to manually correct misspelled vendor names and data scientists faced large variations in merchant names that required costly exception rules, creating frustration for customers and extra work for Expensify’s data team. To address this, Expensify evaluated and implemented a vendor solution from Constructor.io.

Expensify deployed Constructor.io’s self-learning autosuggest in its expense entry tool and data pipeline, improving both user experience and underlying data quality. The result was a 30–40% reduction in manual merchant-name corrections, enabling better analytics and fewer exception rules, and helped optimize $32 billion in expenses per year — outcomes Expensify attributes directly to Constructor.io’s autosuggest.

Expensify

Matt McNamara

CTO