Troubleshooter

In a time when our world faces a climate crisis and lack of resources, easy access to re-utilization is a priority.

Our app tries to solve a problem regarding every sort of electronics. By inquiring the user and resorting to databases, it diagnoses the problem and provides a solution. That may be a quick DIY fix with tutorials, order a replacement part with the best deal or dispatch a freelancer technician with the best cost/quality.

Motivation

The sale of electronic devices has only grown over the last few years, mainly because of new IoT products. The forecast is that this consumption will only grow in the upcoming years, consequently, the demand for repairing such devices as well as an environmental concern also follow this trend.

This motivated us to think of a solution that corresponds to an existing demand today, but which will grow even more in the future. With new technologies such as machine learning algorithms, we can find creative solutions that can help consumers, facilitating the repair of those devices or finding qualified professionals.

This idea has striked us a few months ago, when we had to come up with a sustainable business project for a college matter. Since there, we have been working and perfecting the idea. Then we saw that JUNITEC was organizing this competition, so we thought it was the perfect opportunity for us to show for our work.

We believe that with the quality of our team, as well as the support of TecStorm and the help of highly qualified mentors, we can deliver a project that creates value in people's lives and that in the end also has a positive impact on the environment.

How to Make

1) Create a database with the most diverse electronic devices, their parts, main defects found in similar devices, manufacturers, etc...

2) Create an application with a user-friendly graphical interface, where through a search, the user can easily find his / her device in our database

3) Through a series of questions, we will collect data relating to the problem encountered by the user.

4) Through machine learning algorithms we will diagnose the problem and from there, we will give an            alternative to the user:

     1- DIY, where a step-by-step tutorial on how to solve the problem will be given

     2- Facilitate access to professionals who can solve the problem in a simple and accessible way

5) In case the user chooses qualified professionals, will be shown all professionals that are close to him, showing the waiting time until this professional arrives, as well as the evaluation of this professional by previous users, and a GPS that shows exactly where is the professional, like Uber.