Elton A.I is an app used to automate the repetitive task of uploading receipts for reimbursement. My role in this project was being the sole designer in charge of the whole user experience from the flowcharts to the UI design. I worked alongside a team of developers and the Product Owner from the client side.
Originally posted here
Millions of people travel every day for work. Naturally they have travel expenses. At the end of the day, they end up with a big pile of receipts that they have to manually upload to an application in order to have that money reimbursed to them. This brings two problems for companies: from the employee side, they have to remember to do this or they'll end up losing money. From the employer side, they have no accurate way to know how much an employee is spending, so they probably are reimbursing them more o less money that they should. Both situations are uncomfortable.
Our strategy was to simplify the busy work so employees can focus on their task at hand. The technology behind the app is a OCR (Optical Character Recognition) system linked to a machine learning engine. This engine uses different sources, like your calendar, your camera and your credit card info to predict with a growing confidence rate if the user has valid receipts and submits them for approval.
We had several meetings with the client team to learn about their research. Currently users are spending so much time dealing with expenses that they end up dropping the subject altogether. Contrary to most apps, in this case the least time the user spends in the app, the better. The first thing we learned is that for this to work we had to create rapport with the user because we would be asking them to trust us with really sensitive information.
We agreed with the client team that the first step was to work a lot on the onboarding process. We did several iterations fine-tuning, testing, reworking everything from the aestethic to the copywriting to make sure the users trusted the system enough to allow it to access all the permissions necessary. We drafted a lot of possible looks for the app, going from illustration to pictures, and did several tries on the user flow for this stage. On parallel, the Dev team started focusing their efforts on a consistently reliable machine learning model.
