ORA // Translation
ORA is a mobile app that provides immigrants with situation-based translation needed for building a life in New York City. It offers contextual words and phrases to help people with tasks such as reporting an issue to their landlord, explaining a problem to a doctor, talking to their child’s teacher, etc.
During a four-month fellowship through Blue Ridge Labs at the Robin Hood Foundation, two co-founders and I came up with the idea, built it, and released it to the public.
The goal of the fellowship is to create digital products that will help improve the lives of low-income New Yorkers. From the start, I knew I wanted to focus on non-native English speakers, so we started by visiting community-based organizations already working with that target demographic. We sat in on English classes and just asked the students what was difficult about their lives. We started hearing the same things over and over again: going to the hospital, parent-teacher conferences, MTA reroutes, etc. Google Translate just wasn’t reliable enough. We knew there was something here.
We started with a card-sorting exercise with some students where they ranked some of the problems they faced in order of importance for them to have language assistance. Based on the results, we narrowed down a couple of topics to start.
Before we went digital, we started by making paper booklets, like the one above. We distributed the booklets at these organizations, neighborhood fairs, and community centers. We brought people in to do further research, including role-playing scenarios.
Once we proved the validity, we built out our content library, a working prototype, and figured out a way to get ORA on people’s phones without having a developer. We continued to work with our ESL partners to test the idea and spread the word.
We launched with just common words and phrases for Health, Housing, School, and Work in both Spanish and Simplified Chinese. Within a month of it being released to the public, through mainly word of mouth we had over 100 users spending an average of 3 minutes on the app despite the limited amount of content and functionality.